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Machine learning (ML) algorithms can be applied to predict the suitability of soil for crop cultivation based on digital soil mapping. We used three distinct models viz. Multinomial Logistic Regression (MnLR), Random Forest (RF), and Extreme Gradient Boosting (XGBoost) to predict the suitability of wheat and pearl millet in the Barmer district of the Thar Desert. After the computation of crop suitability classes, ML techniques were used to develop suitability maps of wheat and pearl millet in the study area. The study found that the RF and XGBoost models worked well to classify crop suitability. The RF model showed that 11.9% of the total area was highly suitable, 1.6% was moderately suitable, 14.9% was marginally suitable, and 71.6% was not suitable for wheat crop. RF model for pearl millet showed that 15.5% of the area is highly suitable. Soil suitability mapping for wheat showed a Kappa index ranging from 0.23 to 0.57 and an overall accuracy ranging from 0.79 to 0.86, whereas the prediction of suitability for pearl millet showed a moderate range of Kappa index from 0.31 to 0.58 and accuracy from 0.63 to 0.77. The area under curve (AUC) for wheat crop was 0.72, 0.88, and 0.91 for MnLR, RF, and XGBoost models, respectively. Overall, the RF model performed better than the MnLR model, showing a 16% increase in accuracy. Therefore, the developed suitability maps using ML provide valuable details on agricultural potential in the Indian desert region while harmonizing its impact on the environment and the economy. [ABSTRACT FROM AUTHOR]

The information on land use/land cover (LULC) is indispensable in regional planning, policy formulation and tracking land use/land cover changes (LULCC). The rapid urbanization of hilly terrains, driven by population growth, has significant implications for landslide risk reduction. Recognizing the need for an innovative approach for extracting LULC information, the present study uses a random forest (RF) classifier to develop a novel, pre-trained and universal tool that automatically generates LULC classification maps based on natural colour satellite imagery without any training input from the end-user. The proposed framework with an overall accuracy of 0.75 and an area under the curve (AUC) score of 0.95 in the receiver operating characteristic curve (ROC) approach was used for mapping built-up area expansion in regions susceptible to rainfall-induced landslides in Idukki block panchayat (administrative division), Kerala, India. By comparing the LULC information for the years 2012 and 2022, it was understood that the built-up area in the location has increased from 12.76% of the total area in 2012 to 26.48% in 2022. It is important to consider the rapid increase in built-up area expansion in the 'very high' landslide susceptibility zones in the study area. This clearly demonstrates the need for hazard inclusive planning and tracking of LULCC, for disaster risk reduction. Research Highlights: A pre-trained Land Use/Land Cover (LULC) classification tool is developed using the Random Forest (RF) classifier. Based on natural colour satellite imagery, the tool automatically generates LULC maps for various landscapes worldwide. The tool demonstrates a satisfactory performance, achieving an overall accuracy of 0.75 and an overall ROC AUC score of 0.95. The tool was used to understand the LULC changes in Idukki block panchayat between 2012 and 2022. [ABSTRACT FROM AUTHOR]

In Western countries, speech recognition-based technologies have significantly developed compared to the countries of the South Asian subcontinent like India. India is a multilingual country (22 scheduled languages) with over 1.3 Billion population of which a major percentage faces difficulty with the user interface of different technological advancements and therefore speech recognition tools are very useful. In this paper, we propose LIFA: Language Identification From Audio - a fully automated tool that can identify the spoken language (phrases/words) and invoke the language-specific recognition engine. Experiments were performed on more than 2200 hours of data from the top-11 spoken languages in India. The clips were parameterized with a novel linear predictive cepstral coefficient (LPCC)-based features, which we call LPCC-Grade (LPCC-G). The proposed feature is capable of focusing on the distribution of energy across different frequency ranges in an audio clip for better classification while avoiding high dimensionality issues. Using a random forest-based classifier, we achieved the highest accuracy of 99.01%. Further, we tested the robustness of the system with different noisy scenarios on multiple datasets wherein accuracies in the range of 79%-98% were obtained. We also studied other popular existing features in our comparison where accuracies of 96.37 % and 92.48 % were obtained for LSF and MFCC-based features. [ABSTRACT FROM AUTHOR]

Efficient discrimination of diverse kharif crops, remains crucial for crop monitoring and production forecasting, and plays a pivotal role in decision-making for food security in India. This study aims to harness temporal backscatter data from EOS-04 C-band synthetic aperture data (SAR) payload to achieve precise discrimination among six short-duration (cereal, oilseeds, fibre) and long-duration (fibre, pulses) kharif crops. The study integrates limited ground-truth polygons and a Random Forest machine learning approach for analysing EOS-04 time-series data. The classification accuracies were found to be higher than 75% across all kharif crops, with cereals exhibiting the highest accuracy, succeeded by fibre, oilseed and pulse crops. A key focus lies in identifying optimal polarization combinations for effective discrimination among diverse kharif crop types. The study reveals that the synergistic utilization of dual polarizations outperforms individual co- or cross-polarizations, notably benefiting discrimination of cotton, soybean and groundnut crops. Horizontal–vertical polarizations are found to be most effective for achieving peak accuracies in rice and red gram crops. Furthermore, the analysis indicates a promising potential for early crop assessment, presenting an opportunity to furnish precise crop estimates at least one and a half months before the harvest. [ABSTRACT FROM AUTHOR]

Artificial Intelligence is quickly emerging as a technological solution for the agriculture industry to surmount its classical challenges. Artificial Intelligence is facilitating farmers to refine their products and alleviate unfavourable impacts due to the environment. The central concern of this paper is predictive analytics to develop a machine learning model to identify and predict crop yield based on multiple environmental factors. In this paper, a hybrid learner 'RaNN' is proposed that combines the feature sampling and majority voting technique of Random Forest in-combination with the multilayer Feedforward Neural Network to predict the crop yield. Research has also ascertained the essential features responsible for accurate yield prediction. The proposed model works for rice yield prediction, one of the chief grains of India. The region chosen for the work is Punjab, which is among the largest producer states of India for rice. The dataset consists of 15 attributes comprising the weather and agriculture data collected from the Indian Meteorological Department Pune, and Punjab Environment Information System (ENVIS) Center, Government of India. The study has also made a comparative assessment of 'RaNN' with machine learning methods like Multiple Linear Regression, Random Forest, Decision Tree, Boosting Regression, Support Vector Machine Regression, Ensemble Learner, and Artificial Neural Network. Our model RaNN has listed a better prediction accuracy with minimal error among the other techniques providing a 98% correlation between the actual and the predicted yield. Abbreviations: AI – Artificial Intelligence; ANN – Artificial Neural Network; BR – Boosting Regression; Chem Fert – chemical fertilisers; DT – Decision Tree; EL – Ensemble Learner; ENVIS – Punjab Environment Information System; GBM – Stochastic Gradient Boosting Method; GPS – Global Positioning System; HMAX – highest maximum temperature in degrees C; IMD – Indian Meteorological Department; L1 – Lasso regression; L2 – Ridge regression; LMIN – lowest minimum temperature; ML – Machine Learning; MAE – Mean Absolute Error; MEVP – mean evaporation in mm; MLR – Multiple Linear Regression; MMAX – mean maximum temperature in degrees C; MMIN – mean minimum temperature in degrees C; MSSH – Mean sunshine duration in hours; MWS – mean wind speed in km/h; P1 – number of days with precipitation (0.1–0.2 mm); P2 – number of days with precipitation (greater than or equal to 0.3 mm); RaNN – Hybrid RF-ANN model; RMSE – Root Mean Squared Error; $${R^2}$$ R 2 – Coefficient of determination; RD – number of rainy days; RF – Random Forest; SVM Reg – Support Vector Machine Regression; TMRF – total rainfall per month in mm [ABSTRACT FROM AUTHOR]

The historical prevalence of banking crises and their profound impact on global economies underscores the imperative for policy makers to refine their crisis forecasting frameworks. Against this backdrop, the present study endeavors to predict potential banking crises in India by leveraging a spectrum of artificial intelligence and machine learning techniques (AI-ML). These techniques encompass logistic regression, random forest, naïve Bayes, gradient boosting, support vector machine, neural networks, K-nearest neighbors, and decision trees. Initially, a banking fragility index was constructed utilizing monthly banking data spanning 2002 to 2023, demarcating the periods of crisis and stability. Subsequently, an extensive array of early warning indicators (EWIs) encompassing asset prices, macroeconomic factors, external influences, and credit-related variables were employed to forecast crisis periods. Our findings reveal that AI-ML models exhibit reasonable accuracy in predicting banking crises. Moreover, advanced model performance metrics highlight neural networks and random forest models as particularly effective in crisis prediction, surpassing other methodologies. Notably, among the EWIs, variables related to credit, interest rates, and liquidity emerge as possessing relatively higher information value in discerning fragilities within the Indian banking system. Importantly, the methodological framework presented herein can be extrapolated for banking crisis prediction in other economies. [ABSTRACT FROM AUTHOR]

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Sugarcane is one of the leading commercial crops grown in India. The prevailing weather during the various crop-growth stages significantly impacts sugarcane productivity and the quality of its juice. The objective of this study was to predict the yield of sugarcane during different growth periods using machine learning techniques viz., random forest (RF), support vector machine (SVM), stepwise multiple linear regression (SMLR) and artificial neural networks (ANN). The performance of different yield forecasting models was assessed based on the coefficient of determination (R2), root mean square error (RMSE), normalized root mean square error (nRMSE) and model efficiency (EF). Among the models, ANN model was able to predict the yield at different growth stages with higher R2 and lower nRMSE during both calibration and validation. The performance of models across the forecasts was ranked based on the model efficiency as ANN > RF > SVM > SMLR. This study demonstrated that the ANN model can be used for reliable yield forecasting of sugarcane at different growth stages. [ABSTRACT FROM AUTHOR]

Wetlands are essential for preserving numerous natural cycles and providing habitat for a wide variety of wildlife. They are essential to maintaining the appropriate balance of the ecosystem. The Ramsar Convention categorises wetlands as Ramsar Wetlands based on certain parameters (botanical, geological, and hydrological features) and national importance. There could be multiple threats to the wetland ecosystem such as habitat loss, pollution, invasive species, climate change, overexploitation, hydrological modifications, etc. This study delves into assessing the climate vulnerability of 15 newly designated Ramsar sites in India in 2022. Historical analysis of the inundation area of these sites from 1991 to 2022 was performed using pre-processed Landsat imageries in Google Earth Engine. The accuracy of mapping inundation was evaluated by creating error matrix and analyzing user's accuracy, producer's accuracy and overall accuracy. Future climate vulnerability was assessed using recent climate projections of shared socioeconomic pathway (SSP 245) from Coupled Model Intercomparison Project-Phase 6 (CMIP6). Machine learning regression algorithms were employed to understand the relationship between wetland inundation and climate variables, paving the way for predictive analysis. Some Ramsar sites showed significantly decreasing trends in past inundation patterns, highlighting their potential vulnerability to climate changes. This analysis underscores the importance of proactive measures to protect these Ramsar sites in the face of evolving climatic conditions, emphasizing the critical role of conservation efforts in preserving these essential ecosystems. [ABSTRACT FROM AUTHOR]

Climate change and other extinction facilitators have caused significant shifts in the distribution patterns of many species during the past few decades. Restoring and protecting lesser-known species may be more challenging without adequate biogeographical information. To address this knowledge gap, the current study set out to determine the global spatial distribution patterns of Indigofera oblongifolia (Forssk) a relatively lesser-known leguminous species. This was accomplished by utilizing three distinct bioclimatic temporal frames (current, 2050, and 2070) and four greenhouse gas scenarios (RCPs 2.6, 4.5, 6.0, and 8.5), in addition to non-climatic predictors such as global livestock population, human modification of terrestrial ecosystems, and global fertilizers application (nitrogen and phosphorus). Furthermore, we evaluate the degree of indigenousness using the geographical area, habitat suitability categories, and number of polygons. This research reveals that climatic predictors outperform non-climatic predictors in terms of improving model quality. Precipitation Seasonality is one of the most important factors influencing this species' optimum habitat suitability up to 150 mm for the current, 2050 RCP 8.5 and 2070-RCPs 2.6, 4.5, and 8.5. Our ellipsoid niche modelling extends the range of precipitation during the wettest quarter and maximum temperature during the warmest month to 637 mm and 26.5–31.80 degrees Celsius, respectively. India has a higher indigenous score in the optimal class than the African region. This findings suggest that the species in question tends to occupy contiguous regions in Africa, while in India, it is dispersed into several smaller meta-populations. [ABSTRACT FROM AUTHOR]

Heavy metals from mica waste not only deteriorate the soil quality but also results in the uptake of metals in the crop. The present investigation was conducted to evaluate the effects of different fractions of metals on the uptake in rice, soil microbial and biochemical properties in mica waste-contaminated soils of Jharkhand, India. From each active mine, soil samples were randomly collected at distances of < 50 m (zone 1), 50–100 m (zone 2), and >100 m (zone 3). Sequential metal extraction was used to determine the fractions of different metals (nickel (Ni), cadmium (Cd), chromium (Cr) and lead (Pb)) including water-soluble (Ws) and exchangeable metals (Ex), carbonate-bound metals (CBD), Fe/Mn oxide (OXD) bound metals, organically bound metals (ORG), and residues (RS). The Ni, Cr, Cd and Pb in rice grain were 0.83±0.41, 0.41±0.19, 0.21±0.14 and 0.17±0.08 mg/kg respectively. From the variable importance plot of the random forest (RF) algorithm, the Ws fraction of Ni, Cr and Cd and Ex fraction of Pb was the most important predictor for rice grain metal content. Further, the partial dependence plots (PDP) give us an insight into the role of the two most important metal fractions on rice grain metal content. The microbial and enzyme activity was significantly and negatively correlated with Ws and Ex metal fractions, indicating that water-soluble and exchangeable fractions exert a strong inhibitory effect on the soil microbiological parameters and enzyme activities. [Display omitted] [ABSTRACT FROM AUTHOR]

Aim: Interspecific competition plays a key role in shaping carnivore communities. Top‐down effects can impact the coexistence of superordinate and subordinate competitors and prey across a shared landscape. Limited resources, their abundance and diminishing habitats can all exacerbate interspecific competition. Sometimes, behavioural mechanisms or adaptations are critical for subordinate carnivores to coexist with larger species. Here, we investigated the distribution pattern of the striped hyaena (Hyaena hyaena) population with respect to habitat features, sympatric carnivores and their prey species across a large landscape. Location: Western and Eastern Ghats, India. Taxon: Striped hyaena. Methods: We collected presence–absence data for hyaenas, tigers, leopards, dholes and their prey using camera‐trapping and indirect sign surveys. Using a random forest algorithm, we modelled the local distribution of hyaenas as a function of important ecological variables, including climate, domestic prey abundance and the predicted probabilities of occurrence of other large carnivores and their prey. Results: We found that striped hyaenas were confined to relatively small, discrete areas compared to the three other predators, likely a mechanism for achieving coexistence in the landscape. Our empirical results suggested that hyaenas tend to avoid high occurrence probability areas of their co‐predators at a landscape level but foothold opportunities such as, arid climatic conditions, and prey abundance primarily determined spatial incumbency. Coexistence in an area with low to medium relative abundance of co‐predators offsets the interplay between carcass acquisition and risk associated. The coexistence of species was favoured by altitudinal heterogeneity probably providing denning refugia and also by their preference towards suboptimal thorn forests that sustain a low density of co‐predators. Main Conclusions: Our study showed a desisting impact of large predators on the distribution of the striped hyaena population. Furthermore, we suggest preventing habitat alteration in suboptimal habitats of large carnivores to facilitate coexistence in a shared landscape. [ABSTRACT FROM AUTHOR]

Morphometric analysis of a river basin, encompassing the assessment of shape, drainage, and terrain characteristics, is crucial for erosional, hydrological, and hydro-geological studies However morphometric analysis of large river basins conventionally poses computational challenges. In this study, Google Earth Engine (GEE), a cloud-based platform was utilized to conduct morphometric modeling and evaluation of geomorphology of river basins using JavaScript Application Programming Interfaces (APIs). Consecutively, soil erosion susceptibility mapping based on morphometric parameters using an ensemble of statistical and machine learning algorithms specifically Principal Component Analysis (PCA), Random Forest (RF), and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) was performed. These methods were applied to India's largest (Ganga: 834,933.65 Km2) and second-largest (Godavari: 310,000 Km2) river basins. Correlation-based comparative analysis of PCA and RF-derived erosional susceptibility rankings for the Ganga (GARB) and Godavari (GRB) river basins was also performed followed by the identification of highly susceptible zones. Results indicate that 28.50% (86,800 Km2) of GRB and 9.85% (81,823.49 Km2) of GARB are highly susceptible to geological erosion. Highly susceptible GARB sub-watersheds were 2.7 times more susceptible than those in GRB, while less susceptible GARB sub-watersheds were 1.96 times more susceptible than GRB counterparts. Machine learning with RF excelled in river basins with greater morphometric variation (GARB), while PCA proved more effective in homogenous river basins (GRB). This study introduces an alternative method for morphometric modeling based erosional analysis, emphasizing the suitability and constraints of statistical and machine learning algorithms and providing valuable guidance for soil conservation efforts. [ABSTRACT FROM AUTHOR]

Frequent floods are a severe threat to the well-being of people the world over. This is particularly severe in developing countries like India where tropical monsoon climate prevails. Recently, flood hazard susceptibility mapping has become a popular tool to mitigate the effects of this threat. Therefore, the present study utilized four distinctive Machine Learning algorithms i.e., K-Nearest Neighbor, Decision Tree, Naive Bayes, and Random Forest to estimate flood susceptibility zones in the Agartala Urban Watershed of Tripura, India. The latter experiences debilitating floods during the monsoon season. A multicollinearity test was conducted to examine the collinearity of the chosen flood conditioning factors, and it was seen that none of the factors were compromised by multicollinearity. Results showed that around three-fourths of the AUW area was classified as moderate to very high flood-prone zones, while over 20 percent was between low and very low flood-prone zones. The models applied performed well with ROC-AUC scores greater than 70 percent and MAE, MSE, and RMSE scores less than 30 percent. DT and RF algorithms were suggested for places with similar physical characteristics based on their outstanding performance and the training datasets. The study provides valuable insights to policymakers, administrative authorities, and local stakeholders to cope with floods and enhance flood prevention measures as a climate change adaptation strategy in the AUW. [ABSTRACT FROM AUTHOR]

Identifying farming practices that decrease susceptibility to infectious diseases and optimise food conversion efficiency is valuable for chicken welfare and productivity, the environment, and public health. Enterotypes can be used to define microbial community phenotypes that have differential, potentially significant impacts on gut health. In this study, we delineated enterotypes by analysing the microbiomes of 300 indigenous Kadaknath and 300 commercial Cobb400 broiler chickens raised across 60 farms in western India. Using a compositional data approach, we identified three distinct enterotypes: PA1 (n=290), PA2 (n=142) and PA3 (n=67). PA1 and PA2 clustered more closely with each other than with PA3, however, PA2 had significantly lower alpha diversity than PA1. PA1 had a high Firmicutes: Bacteroides ratio, was dominated by Faecalibacterium and had a higher abundance of Prevotellamassilia than other enterotypes. PA2 was characterised by its low alpha diversity, a high abundance of the common taxa Phascolarctobacterium A and Phocaeicola dorei and a significantly higher Campylobacter abundance than PA1. PA3 had the highest Bacteroidota abundance of the three enterotypes and was defined by high prevalence of lower abundance taxa such as CAG-831 and Mucispirillum schaedleri. Network analysis showed that all enterotypes have different proportions of competing Firmicutes-dominant and Bacteroidota-dominant guilds. Random Forest Modelling using defined farm characteristics was predictive for enterotype. Factors affecting enterotype include whether farms were open, enclosed or caged, the location of farms, whether visitors were allowed inside, the number of people in contact with the chickens, chicken line, the presence of dogs and whether flock thinning took place. This study suggests that enterotypes are influenced by farming practices, hence modification of practices could potentially be used to reduce the burden of zoonotic pathogens such as Campylobacter. [ABSTRACT FROM AUTHOR]

In today's world, by integrating remote sensing technology and modern state-of-the-art machine learning techniques, obtaining Land Use Land Cover (LULC) maps has become easier in comparison to traditional manual methods. The performance of a Machine Learning classifier is influenced by various factors. The objective of this study is to evaluate the impact of sampling design in rough complex terrain located in the Northern Himalayan region in Uttarakhand state, India, where reference data is often limited due to the geographical characteristics of the study area. Three sampling design strategies have been incorporated in this study, namely, stratified random sampling with a proportional number of samples (SRS)proportional, stratified random sampling with an equal number of samples (SRS)equivalent and stratified systematic sampling with an equal number of samples with a minimum distance of 10 m between the consecutive samples (SSS)D = 10 m for the LULC classification. In this study, Sentinel-2 data of 10 m spatial resolution for the study area of Dehradun district, Uttarakhand, India, has been selected. The following conclusions can be drawn from the results of this study (i) (SRS)proportional achieved the highest Overall Accuracy (OA) among all the three sampling techniques. The OA and kappa score (ka) using (SRS)proportional are OA = 90.25 and ka = 0.874 by Random Forest, OA = 88.84 and ka = 0.856 by Support Vector Machine and k Nearest Neighbours (kNN) obtained OA = 87.72 and ka = 0.842, respectively. (ii) It was found that in the case of (SRS)proportional, the majority classes like the deciduous forest, evergreen forest and cropland achieved higher recall and precision values in comparison to those obtained from the other two sampling strategies, i.e. (SRS)equivalent and (SSS)D = 10 m. (iii) The results showed that while switching from (SRS)proportional to (SRS)equivalent or from (SRS)proportional to (SSS)D = 10 m, there was a slight reduction in the precision and recall values for the majority classes and a slight increase for a few of the minority classes. [ABSTRACT FROM AUTHOR]

According to researches, there are around 188 million unemployed people around the globe. We may find many job vacancies on job portals and across the internet to help the job seekers. India alone has more than a hundred job portals. One major issue people face here is that the job seekers are not sure if the employer is real or fake. Most of these portals do not have a system that could check if the employer, posting a job is real or fake. Scammers are making use of this opportunity to post fake job offers which might look genuine to the job seekers applying for it. This way the poor job seekers might lose a large amount of money and time. A best possible solution for this problem would be that the job portal itself being able to identify if the job being posted is real or fake. This paper suggests using a machine learning model to achieve this goal. The idea here is to use natural language processing to understand and analyze the job posting and then making use of a machine learning model to predict if the job posting is real or fake. The first step is to import a dataset which has real life real and fake job posting. In this project, Employment Scam Aegean Dataset provided by University of Aegean Laboratory of Information and Communication system Security is being used. This dataset contains 18000 samples containing real life job postings. Various text cleaning techniques like lemmatization, stop words removal and special characters and punctuation removal is done on the data. Once the text data is processed, various algorithms like Random Forest, Linear SVC, Gradient Boosting Classifier, Gaussian naïve Bayes classifier and XGB classifier is used to test the performance of the model. The best two algorithms with respect to the percentage of accuracy with which the models could classify real and fake job posting was taken into consideration. Random Forest and Linear SVC could give accuracy close to 98%. Both of these algorithms were tuned using GridSearchCV, a library function which is a part of sklearn's model selection package. After tuning, the performance of both these algorithms increased and Linear SVC gave a better accuracy score of 99%. Hence Linear SVC is being used in this project for predicting real and fake job posting on a job portal.. [ABSTRACT FROM AUTHOR]

In present study, the variability in wintertime maximum (Tmax) and minimum (Tmin) temperature patterns over India using observed and deep learning techniques have been assessed. The analysis has been caried out for the period 1979–2018 during the months from November to February. The month of February depicted strongest variability in Tmax and Tmin over Northwest India (NWI) with significant + ve trend for upper half of the country. Wintertime temperature variability was seen to be dominant in the Indo-Gangetic plain area covering some parts of NWI and Northeast India (NEI) for Tmax and Tmin. Also, a gradual increase in the spatial coverage, engulfing majority of South Peninsular India (SPI) and Central India (CI) of the rising Diurnal Temperature Range (DTR) was found from November to January. Decreasing DTR was observed only for January extending along Indo-Gangetic plains. The model Random Forest (RF) performed quite well relative to Long Short-Term Memory model (LSTM) in predicting the winter temperatures (especially for Tmax) during all the considered months. The RF made a robust Tmax forecast during NDJF over all India (RMSE – 0.51, MAPE – 1.4). However, its performance is not up to the mark during the month of February over NEI (RMSE – 1.63, MAPE – 4.5). The maximum fluctuating patterns of temperature have been found during the month of February. The study emphasizes on algorithm-based approaches to study the temperature, so that better understanding could be developed for the meteorological sub-divisions over India. [ABSTRACT FROM AUTHOR]

In India, agriculture plays an important role in the nation’s gross domestic product (GDP) and is also a part of civilization. Countries’ economies are also influenced by the amount of crop production. All business trading involves farming as a major factor. In order to increase crop production, different technological advancements are developed to acquire the information required for crop production. The proposed work is mainly focused on suitable crop selection across districts in Tamil Nadu, considering phenotype factors such as soil type, climatic factors, cropping season, and crop region. The key objective is to predict the suitable crop for the farmers based on their locations, soil types, and environmental factors. This results in less financial loss and a shorter crop production timeframe. Combined feature selection (CFS)-based machine regression helps increase crop production rates. A brief comparative analysis was also made between various machine learning (ML) regression algorithms, which majorly contributed to the process of crop selection considering phenotype factors. Stacked long short-term memory (LSTM) classifiers outperformed other decision tree (DT), k-nearest neighbor (KNN), and logistic regression (LR) with a prediction accuracy of 93% with the lowest classification accuracy metrics. The proposed method can help us select the perfect crop for maximum yield. [ABSTRACT FROM AUTHOR]

An increase in population expansion, urban sprawling environment, and climate change has resulted in increased food demand, water scarcity, environmental pollution, and mismanagement of water resources. Groundwater, i.e., one of the most precious and mined natural resources is used to address a variety of environmental demands. Among all, irrigation is one of the leading consumers of groundwater. Various natural heterogeneities and anthropogenic activities have impacted the groundwater quality. As a result, monitoring groundwater quality and determining its suitability are critical for the sustainable long-term management of groundwater resources. In this study, groundwater samples from 35 different sampling stations were collected and tested for various parameters associated with irrigation water quality. Hybrid MCDM (fuzzy-AHP) method was used to determine the groundwater suitability for irrigation purposes. The suitability map obtained using spatial overlay analysis was classified into low, moderate, and high irrigation water suitability zones. Along with suitability analysis, various regression-based machine learning models such as multiple linear regression (MLR), random forest (RF), and artificial neural network (ANN) were used and compared to predict irrigation water suitability. Results depicted that the ANN model with the highest R2 value of 0.990 and RMSE value near to zero (0) has outperformed all other models. The present methodology could be found useful to predict irrigation water suitability in the region where regular sampling and analysis are quite challenging. [ABSTRACT FROM AUTHOR]

Porosity estimation is essential for the exploration of hydrocarbons in sedimentary basins, which are affected by several factors such as the burial depth, lithology changes, sedimentary environment and diagenetic degree. Hence, detailed understanding of reservoir porosity is essential for estimating potential economic reserves and developing an explored hydrocarbon field. The present study uses high‐quality three‐dimensional (3D) seismic reflection data to decipher the porous zones within the Eocene‐Oligocene‐Miocene intervals of the Upper Assam Basin. The coherency seismic attribute has brought out several fault‐bounded structures that control the reservoir geometry. Random forest‐based machine learning technique has been utilized for the porosity estimation within the formations of interest. It is observed that the north‐east (NE) parts of the targeted intervals are porous with varying porosity of 0.28–0.42. The Miocene period witnessed a high amount of sedimentation within the basin. Different structural highs and thicknesses are pronounced in the NE part of the study region. Thus, the Miocene intervals are favourable leads for the exploitation of hydrocarbons. [ABSTRACT FROM AUTHOR]

India is the world's second-largest exporter of agricultural products. Agriculture is India's most populated sector of the economy which plays a key part in the country's overall socio-economic fabric. Crop yield prediction is crucial yet desirable for every country to ensure food security. Machine learning can be a very important decision support tool for the prediction of crop yield which can help in preparing for the future in a much more substantial way. Many studies have been conducted for crop yield prediction using multi-source data, still, the predictions of the crop of the Rajasthan region in India have not been investigated. In the present study well-known machine learning techniques, decision tree, random forest, lasso regression, and support vector regression have been implemented for crop yield prediction using multi-source data. Machine learning techniques were implemented on data set that consists of Vegetation Indices, extracted from remote sensing data and Weather data. Random forest attained the more accurate prediction with the coefficient of determination (R2) of 0.77, root mean square error of 0.39 t/ha and mean absolute error of 0.28 t/ha. The research obtained a good estimation of crop yield prior to the harvest. The crop yield is impacted by the change in environmental factors, this research would assist the farmers to have a prior estimation of the yield. The study also reveals that gradually adding more data to the model increases the overall prediction rate. [ABSTRACT FROM AUTHOR]

India is often described as a country of villages, where a majority of the population depends on agriculture for their livelihood. The landscape of Indian agriculture is approximately 159.7 million hectares. Agriculture plays a pivotal role in India's Gross Domestic Product (GDP), accounting for about 18% of the nation's economic output. Diseases and pests can have detrimental effects on crops, leading to reduced yields. These challenges can include the spread of plant diseases, infestations by insects or other pests, and the overall degradation of crop health. Early detection of diseases in crops is crucial for several reasons. Detecting diseases at an early stage allows for prompt intervention, such as applying appropriate pesticides or taking preventive measures. The main aim of this study is to develop a highly effective method for plant leaf disease detection using computer vision techniques. Here, leaf disease detection comprises histogram equalization, denoising, image color threshold masking, feature descriptors such as Haralick textures, Hu moments, and color histograms to extract the salient features of leaf images. These features are then used to classify the images by training Logistic Regression, Linear Discriminant Analysis, K-nearest neighbor, decision tree, Random Forest, and Support Vector Machine algorithms using K-fold validation. K-fold validation is used to separate the validation samples from the training samples, and the K indicates the number of times this is repeated for the generalization. The training and validation processes are performed in two approaches. The first approach uses default hyperparameters with segmented and non-segmented images. In the second approach, all hyperparameters of the models are optimized to train segmented datasets. The classification accuracy improved by 2.19% by utilizing segmentation and hyperparameter tuning further improved by 0.48%. The highest average classification accuracy of 97.92% is achieved using the Random Forest classifier to classify 40 classes of 10 different plant species. Accurate detection of plant disease leads to the sustained growth of plants throughout the growing span of the plants. [ABSTRACT FROM AUTHOR]

Summary: Agriculture is the major backbone of India. Therefore, the crop recommendation system is important for the farmers and the country as it reflects the country's economic growth. The crop yield rate is affected due to various parameters such as climatic changes, soil properties, temperature, humidity and so forth. An effective prediction system is required to monitor the field and suggest suitable crops that can provide a maximum yield rate. Therefore, a prediction system is developed in the proposed framework for crop recommendation using the sensor information collected from Maharashtra, India. The dataset has been built with the information collected by 250 sensors located in different Maharashtra places. Initially, the gathered dataset is subjected to preprocessing steps like data cleaning, removing duplicate values, and filling up the missing values. Then, robust and flexible machine learning models like decision tree, random forest, and support vector machine are used, which analyze the preprocessed data and predict the suitable crops that show high yield for a particular area. The implementation of the proposed framework is done using Python. The RF classifier achieved the highest accuracy rate of 95% among the three classifiers. [ABSTRACT FROM AUTHOR]

Soil Organic Carbon (SOC) is a crucial indicator of ecosystem health and soil quality. Machine learning (ML) models that predict soil quality based on environmental parameters are becoming more prevalent. However, studies have yet to examine how well each ML technique performs when predicting and mapping SOC, particularly at high spatial resolutions. Model predictors include topographic variables generated from SRTM DEM; vegetation and soil indices derived from Landsat satellite images predict SOC for the Lakhimpur district of the upper Brahmaputra Valley of Assam, India. Four ML models, Random Forest (RF), Cubist, Extreme Gradient Boosting (XGBoost), and Support Vector Machine (SVM), were utilized to predict SOC for the top layer of soil (0–15 cm) at a 30 m resolution. The results showed that the descriptive statistics of the calibration and validation sets were close enough to the total set data and calibration dataset, representing the complete samples. The measured SOC content varied from 0.10 to 1.85%. The RF model's performance was optimal in the calibration and validation sets (R2c = 0.966, RMSEc = 0.159%, R2v = 0.418, RMSEv = 0.377%). The SVM model, on the other hand, had the next-lowest accuracy, explaining 47% of the variation (R2c = 0.471, RMSEc = 0.293, R2v = 0.081, RMSEv = 0.452), while the Cubist model fared the poorest in both the calibration and validation sets. The most-critical variable in the RF model for predicting SOC was elevation, followed by MAT and MRVBF. The essential variables for the Cubist model were slope, TRI, MAT, and Band4. AP and LS were the most-essential factors in the XGBoost and SVM models. The predicted OC ranged from 0.44 to 1.35%, 0.031 to 1.61%, 0.035 to 1.71%, and 0.47 to 1.36% in the RF, Cubist, XGBoost, and SVM models, respectively. Compared with different ML models, RF was optimal (high accuracy and low uncertainty) for predicting SOC in the investigated region. According to the present modeling results, SOC may be determined simply and accurately. In general, the high-resolution maps might be helpful for decision-makers, stakeholders, and applicants in sericultural management practices towards precision sericulture. [ABSTRACT FROM AUTHOR]

In the present study, a model has been developed for nowcasting using the Automatic Weather Station (AWS) data collected from Thiruvananthapuram, Kerala, India. The Proposed model is based on machine learning techniques: Random Forest (RF), which had been coupled with Principal Component Analysis (PCA). PCA minimizes the presence of multicollinearity issue in the AWS data, which enables the RF to access independent effects of predictors efficiently to predict rainy or non-rainy conditions of the atmosphere for the next 4 hours during the peak summer monsoon of month July. The sensitivity and feasibility of the model were tested for different predictors such as wind speed, temperature, pressure, relative humidity, sunshine, and rainfall, where the demarcation between rainy and non-rainy events was computed using a precision-recall curve. The performance of proposed algorithms for rainfall events is evaluated by using different statistics such as accuracy, precision, recall, probability of detection (POD), and false alarm rate (FAR). The proposed algorithm is found to nowcast with an accuracy rate of 90% and the probability of detection is 68%. The analysis of in-situ observations establishes that the most influential predictors for the nowcasting of rainfall are atmospheric pressure and wind speed. [ABSTRACT FROM AUTHOR]

In a survey conducted in the year 2020, we came to know that India's around 50% of population includes young people of the age group of 25 and students. Guiding this young mass in the right way and strengthening their future is a huge responsibility put over the head of the elder citizens of India such as their parents teachers and professors. This paper aims to build a model that can predict the students' competency level and the risk factors or the fields where he needs to put their effort to improve themselves, and this model also helps the parents, professors and Educational institutes to know about their children's and students in which zone they stand, are they ready to compete with others. This analysis is done by using different ML bifurcation algorithms. Also we aim to find the best classifier which can emerge with the highest predicting accuracy among all other classifiers to the above-said problem. The accuracy of 88.5% is achieved through the proposed machine learning algorithm for particular education datasets which have been taken into consideration. [ABSTRACT FROM AUTHOR]

Agriculture serves as the mainstay of India's economy, bearing a vital responsibility in nourishing an expanding populace. The thriving of this sector is contingent upon numerous variables, among which the choice of the optimal crop plays a pivotal role. The advent of Machine Learning (ML) has engendered a transformative impact on the agricultural sector by facilitating the prediction of suitable crops, contingent on soil attributes. This study undertakes the examination of diverse ML algorithms, encompassing Decision Tree, Linear Regression, Naïve Bayes, Random Forest, Extreme Gradient Boosting (XGBoost), and Support Vector Machine, to assess their efficacy in recommending optimal crops based on soil parameters. The parameters under consideration include Phosphorus, Nitrogen, Potassium, Electrical Conductivity, pH, Organic Carbon, Boron, Iron, Zinc, Copper, Manganese, and Sulphur. The crop recommendations are focused on Rice, Cotton, and Jowar for the Kurnool district of Andhra Pradesh, India. Among the assessed models, it was observed that the XGBoost model surpassed others in terms of accuracy in determining the most suitable crop for the given soil parameters. The experimental findings substantiate the precision of the model in forecasting the apt crop, thereby underscoring the immense potential of ML in the agricultural domain. This investigation signifies a considerable stride towards optimal crop recommendation, thereby increasing the potential for enhanced yield and profitability. Through the incorporation of technological innovation, agriculture can be rendered more efficient, cost-effective, and sustainable, thus laying the groundwork for a promising future. [ABSTRACT FROM AUTHOR]

Given India's vast expanse and dense population, the prediction of agricultural yields is crucial for ensuring food security. The task, however, is complex due to the influence of a multitude of factors, such as agricultural practices, environmental conditions, and technological advancements. Existing machine learning (ML) models face difficulties due to the quality and variability of data, model overfitting, intricate model structures, insufficient feature engineering, and temporal dependencies. Therefore, a robust and efficient model that addresses these challenges is imperative. In this study, an investigation was conducted using five prevalent ML algorithms — Random Forest (RF), XGBoost, Decision Tree (DT), Support Vector Machine (SVM), and Linear Regression (LR) — on a crop prediction dataset sourced from Kaggle. Algorithms that exhibited the highest coefficient of determination (R²) were selected to construct a hybrid model for aggregate prediction. Results demonstrated that the proposed hybrid model, encompassing DT, XGBoost, and RF, surpassed individual classifiers in terms of R² score and outperformed the existing models, achieving an accuracy of 98.6%. This provides a robust and efficient framework for crop yield predictions. Consequently, a user-friendly tool, 'Crop Yield Predictor', was developed, rendering the model accessible and practical for on-ground applications in agriculture. This tool effectively translates complex data and algorithms into actionable insights, bridging the gap between advanced machine learning techniques and practical agricultural applications. [ABSTRACT FROM AUTHOR]

Numerous applications in agriculture, climate, ecology, hydrology, and the environment are severely constrained by the lack of detailed information on soil texture. The purpose of this study was to predict soil particle-size fractions (PSF) in the Ri-Bhoi district of Meghalaya state, India, using a random forest model (RF). For the modeling of soil particle-size fractions, we employed 95 soil profiles (456 depth-wise layers) gathered from a recent national land resource inventory as well as currently accessible environmental variables. Sand, silt, and clay content were predicted using the Random Forest model at varied depths of 0–5, 5–15, 30–60, 60–100, and 100–200 cm. Our results showed the R2 for sand was found to be 0.30 (0–5 cm), 0.28 (5–15 cm), and 0.21 (15–30 cm). For the sand, silt, and clay fractions, respectively, the concordance correlation coefficient (CCC) was found to be greater in the 0–30 cm, 0–60 cm, and 0–15 cm depths. When there is a reasonably close monitoring of the coverage probability with a confidence level along the 1:1 line, prediction interval coverage probability (PICP) gives a decent indicator of what to anticipate. The most crucial variables for the prediction of sand and silt were channel network base level (CNBL) and LS-Factor, whereas Min Temperature of Coldest Month (°C) (BIO6) was discovered for clay prediction. For all three soil texture fractions, the range between the 5% lower and 95% higher prediction bounds was large, indicating that the existing spatial predictions may be improved. The maps of soil texture were significantly more precise, and they accurately depicted the spatial variations of particle-size fractions. Additionally, there is still a need to investigate novel methodologies for extensive digital soil mapping, which will be very advantageous for many international initiatives. [ABSTRACT FROM AUTHOR]

The relationship between humans and nature must be taken into account as LULC changes in order to map and monitor the natural resources accessible on earth's surface. The amalgamation of remote sensing and GIS advancements in technology has made it easier to track variations in land use and cover from times gone by to the today. To properly represent the city, decision makers and policy makers frequently need accurate and updated LULC data. By utilizing remote sensing and GIS methodologies, this study piece illustrates the changes to the LULC status and driving factors associated to it in study area from 2010 to 2020. Machine learning Random Forest (RF) classifier was used to categorize LANDSAT photos in Google Earth Engine. The results of this analysis show that the Pauri Garhwal district in the state of Uttarakhand has experienced a rapid LULC changes from 2010 to 2020 using GIS and remote sensing methods. The fundamental reason for these changes in the study area is the conversion of forest, agricultural land, and arid land into urban areas. Therefore, it is crucial to control the factors causing LULC changes for sustainable ecosystem; otherwise, these LULC classes lose their ability to provide essential services for a healthy ecosystem. [ABSTRACT FROM AUTHOR]

In a recent survey conducted in the year of 2022, it came to know that India contains around 50 percentages of its population is occupied by young people belonging to the age group of 25 and falls under a category of student. Guiding this young mass people in a right guidelines and strengthening the future of this students are a huge responsibility. The power of students are elder citizens of India such as their parent's teachers, professors, entrepreneur etc. The only way to strengthen the future of the young students is through educating them. In order to analyze how effectively the student can compete with the modern world and what kind of teaching methodology are needed to be adopted to each student, which is very important to every country peoples. Therefore, we are provided to be interpret the effective education to the children's and students. To monitor student's academic record there is a need of building a new model which can predict the performance and risk factor associated with the students for the next academic year. This is done by gathering the student's previous academic record to analysis with different classifier techniques. The proposed work is aim to build a model, that can predict the student's competency level and the risk factors or students are improve himself with all fields effort to both academic and non-academic activity. This proposed model also helps the parents, teachers, and educational institutes. This entire analysis is done by using different Machine Learning (ML) bifurcations algorithm. Also we aim to find the best classifier which can emerge with a highest predicting accuracy among all other classifiers to the above said problems. [ABSTRACT FROM AUTHOR]

The understating of natural resources and hazards is very fundamental from spatial perspective to reduce loss of resource and human lives. For this, the main aim of the current research is to synthesize the three natural resources (groundwater, wetland, and forest) into one multi-resource (MR) potentiality map and three natural hazards (flood, landslide, and shoreline erosion) into one multi-hazard (MH) susceptibility map. To achieve this goal, several inventories, geo-environmental factors, satellite bands, and indices have been taken as input data sets. Random forest (RF) model has been considered for individual mapping (except shoreline erosion mapping) due to its high precision. For shoreline erosion mapping, digital shoreline analysis system (DSAS) model has been employed. The accuracies of the model have been determined from the area under receiver operating characteristic (AUROC) curve, producer accuracy (PA), and user accuracy (UA). RF model has more than 90% prediction accuracy in different resource and hazard mapping, whereas for the forest mapping, PA and UA are 90% and 80%, respectively. The results of this study for multi-resource mapping show forest (24.05%) and groundwater (22.28%) as the major resources, whereas flood (17.27%) is the most destructive in comparison with other hazards. The multi-resource and multi-hazard maps of the research area provide an important tool to land managers and policymakers for sustainable development and management. [ABSTRACT FROM AUTHOR]

Every developing country relies on transportation, and there has been an exponential expansion in the development of various sorts of vehicles with various configurations, which is a major component strengthening the automobile sector. India is a developing country with increasing road traffic, which has resulted in challenges such as increased road accidents and traffic oversight issues. In the lack of a parametric technique for accurate vehicle recognition, which is a major worry in terms of reliability, high traffic density also leads to mayhem at checkpoints and toll plazas. A system that combines an intelligent domain approach with more sustainability indices is a better way to handle traffic density and transparency issues. The Automatic Licence Plate Recognition (ALPR) system is one of the components of the intelligent transportation system for traffic monitoring. This study is based on a comprehensive and detailed literature evaluation in the field of ALPR. The major goal of this study is to create an automatic pattern recognition system with various combinations and higher accuracy in order to increase the reliability and accuracy of identifying digits and alphabets on a car plate. The research is founded on the idea that image processing opens up a diverse environment with allied fields when employing distinct soft techniques for recognition. The properties of characters are employed to recognise the Indian licence plate in this study. For licence plate recognition, more than 200 images were analysed with various parameters and soft computing techniques were applied. In comparison to neural networks, a hybrid technique using a Convolution Neural Network (CNN) and a Support Vector Machine (SVM) classifier has a 98.45% efficiency. [ABSTRACT FROM AUTHOR]

Recent studies have highlighted several mental health problems in India, caused by factors such as lack of trained counsellors and a stigma associated with discussing mental health. These challenges have raised an increasing need for alternate methods that can be used to detect a person's emotion and monitor their mental health. Existing research in this field explores several approaches ranging from studying body language to analysing micro-expressions to detect a person's emotions. However, these solutions often rely on techniques that invade people's privacy and thus face challenges with mass adoption. The goal is to build a solution that can detect people's emotions, in a non-invasive manner. This research proposes a journaling web application wherein the users enter their daily reflections. The application extracts the user's typing patterns (keystroke data) and primary phone usage data. It uses this data to train an ensemble machine learning model, which can then detect the user's emotions. The proposed solution has various applications in today's world. People can use it to keep track of their emotions and study their emotional health. Also, any individual family can use this application to detect early signs of anxiety or depression amongst the members. [ABSTRACT FROM AUTHOR]

This work focused on the prediction of generation of renewable energy (solar and wind) using the machine learning ML algorithms. Prediction of generation are very important to design the better microgrids storage. The various ML algorithms are as logistic regression LR and random forest RA and the ARIMA, time series algorithms. The performance of each algorithm is evaluated using the mean absolute error, mean squared error, root mean squared error, and mean absolute percentage error. The MAE value for the ARIMA (0.06 and 0.20) model for solar and wind energy is very less as compared to RF (15.65 and 61.73) and LR (15.78 and 54.65) of solar and wind energy. Same with MSE and RMSE, the MSE and RMSE value for the ARIMA of solar energy model obtained is 0.01 and 0.08 and wind energy is 0.07 and 0.27 respectively. Comparative analysis of all of these matrices of each algorithm for both the dataset, we concluded that the ARIMA model is best fit for the forecasting of solar energy and wind energy. [ABSTRACT FROM AUTHOR]

Mustard is the second most important edible oilseed after groundnut for India. Adverse weather drastically reduces the mustard yield. Weather variables affect the crop differently during different stages of development. Weather influence on crop yield depends not only on the magnitude of weather variables but also on weather distribution pattern over the crop growing period. Hence, developing models using weather variables for accurate and timely crop yield prediction is foremost important for crop management and planning decisions regarding storage, import, export, etc. Machine learning plays a significant role as it has a decision support tool for crop yield prediction. The models for mustard yield prediction was developed using long-term weather data during the crop growing period along with mustard yield data. Techniques used for developing the model were variable selection using stepwise multiple linear regression (SMLR) and artificial neural network (SMLR-ANN), variable selection using SMLR and support vector machine (SMLR-SVM), variable selection using SMLR and random forest (SMLR-RF), variable extraction using principal component analysis (PCA) and ANN (PCA-ANN), variable extraction using PCA and SVM (PCA-SVM), and variable extraction using PCA and RF (PCA-RF). Optimal combinations of the developed models were done for improving the accuracy of mustard yield prediction. Results showed that, on the basis of model accuracy parameters nRMSE, RMSE, and RPD, the PCA-SVM model performed best among all the six models developed for mustard yield prediction of study areas. Performance of mustard yield prediction done by optimum combinations of the models was better than the individual model. [ABSTRACT FROM AUTHOR]

The predictive models from different machine learning methods are useful tools for sensitive geo-mechanical properties determination. Many such models were previously established to manage time efficiency in a real-field scenario. The objective of this study is to assess and optimize three machine learning-based approaches, namely Support Vector Machine (SVM), Random Forest (RF) and Deep Neural Network (DNN), for predicting the performance of lateritic soil shear strength. The laboratory tests were accomplished to obtain the index parameters after collecting the representative lateritic soil samples along the Ratnagiri-Sangameshwar section of National Highway 66 in Maharashtra, India. To produce the training and test datasets for the prediction models, the intrinsic properties of the soil are documented, including unit weight, maximum dry density, liquid limit, plasticity index and optimal moisture content. Further, 102 lab-tested lateritic soil data of input parameters are implemented to perform the three supervised learning algorithms. The performance and accuracy of the studied methods were measured using the root mean square error (RMSE) and goodness of fit (R2). The outcome demonstrates that the deep neural network model has the highest prediction accuracy for the residual soil shear strength among the three distinct proposed machine learning (ML) models. [ABSTRACT FROM AUTHOR]

Elevated fluoride in groundwater is a severe problem in India due to its extensive occurrence and detrimental health impacts on the large population that thrives on groundwater. Although fluoride is primarily a geogenic pollutant, existing model-based studies lack the amalgamation of the influence of geologic factors, specifically tectonics, for identifying groundwater fluoride distribution. This drawback encourages the present study to investigate the association of the tectonic framework with fluoride in a multi-model approach. We have applied three machine learning models (random forest, boosted regression tree, and logistic regression) to predict elevated groundwater fluoride based on fluoride measurements across India. The random forest model outperformed other models with an accuracy of 93%. Tectonics was found to be one of the most important predictors alongside "depth to water table." Two major areas of high risk identified were the northwest parts and the south–southeast cratonic peninsular region. The random forest model also performed significantly well over the validation dataset. We estimate that nearly 257 million people are exposed to elevated fluoride risk in India. We endeavor that the findings of our study would be an effective tool for identifying the areas at risk of elevated fluoride and also assist in undertaking effective groundwater management strategies. [ABSTRACT FROM AUTHOR]

Forest inventory parameters play an important role in understanding various biophysical processes of forest ecosystems. The present study aims at integrating Terrestrial Laser Scanner (TLS) and ALOS PALSAR L-band Synthetic Aperture Radar (SAR) data to assess Aboveground Biomass (AGB) in the Barkot Forest Range, Uttarakhand, India. The integration was performed to overcome the AGB saturation issue in ALOS PALSAR L-band SAR data for the high biomass density forest of the study area using 13 plots. Various parameters, namely, Gray-Level Co-Occurrence Matrix (GLCM) texture measures, Yamaguchi decomposition components, polarimetric parameters, and backscatter values of HH and HV band intensity, were derived from the ALOS SAR data. However, TLS was used to obtain the diameter at breast height (dbh) and tree height for the sample plots. A total of 23 parameters was retrieved using TLS and SAR data for integration with the LiDAR footprint. The integration was performed using Random Forest (RF) and Artificial Neural Network (ANN). The statistical measures for RF were found to be promising compared with ANN for AGB estimation. The R2 value obtained for the RF was 0.94, with an RMSE of 59.72 ton ha−1 for the predicted biomass value. The RMSE% was 15.92, while the RMSECV was 0.15. The R2 value for ANN was 0.77, with an RMSE of 98.46 ton ha−1. The RMSE% was 26.0, while the RMSECV was 0.26. RF performed better in estimating the biomass, which ranged from 122.46 to 581.89 ton ha−1, while uncertainty ranged from 15.75 to 85.14 ton ha−1. The integration of SAR and LiDAR data using machine learning shows great potential in overcoming AGB saturation of SAR data. [ABSTRACT FROM AUTHOR]

Landslides are a significant issue in India due to the country's varied topography, heavy monsoon rains, and deforestation, which contribute to soil instability and increased landslide risk. These natural disasters can cause damage to infrastructure and loss of life. In light of the ongoing problem of landslides in India, this research paper aims to address the need for effective landslide prediction strategies. Through the findings of this research study, a novel approach has been presented for predicting landslide occurrences in India, which will aid in reducing the impact of these events on infrastructure, communities, and lives. The work that has been carried out using data and information based in India has shown to have a low accuracy level. As a result, the model created using this information is not deemed to be very reliable. This study focuses on predicting landslides in India using machine learning models such as (Xtreme Gradient Boost) XGboost, random forest, and AdaBoost. Previous research on landslide prediction in India had not been widely done or had not achieved acceptable accuracy levels. This study aims to address this gap in knowledge and improve the predictability of landslides in India. The research is based on a database of different areas in India and aims to increase awareness and save lives and resources by predicting landslides with 91% accuracy. [ABSTRACT FROM AUTHOR]

Assessment of spatiotemporal dynamics of meteorological variables and their forecast is essential in the context of climate change. Such analysis can help suggest possible solutions for flora and fauna in protected areas and adaptation strategies to make forests and communities more resilient. The present study attempts to analyze climate variability, trend and forecast of temperature and rainfall in the Valmiki Tiger Reserve, India. We utilized rainfall and temperature gridded data obtained from the Indian Meteorological Department during 1981–2020. The Mann–Kendall test and Sen's slope estimator were employed to examine the time series trend and magnitude of change at the annual, monthly and seasonal levels. Random forest machine learning algorithm was used to estimate seasonal prediction and forecasting of rainfall and temperature trend for the next ten years (2021–2030). The predictive capacity of the model was evaluated by statistical performance assessors of coefficient of correlation, mean absolute error, mean absolute percentage error and root mean squared error. The findings revealed a significant decreasing trend in rainfall and an increasing trend in temperature. However, a declining trend for maximum temperature has been observed for winter and post-monsoon seasons. The results of seasonal forecasting exhibited a considerable decrease in rainfall and temperature across the Reserve during all the seasons. However, the temperature will increase during the summer season. The random forest machine learning algorithm has shown its effectiveness in forecasting the temperature and rainfall variables. The findings suggest that these approaches may be used at various spatial scales in different geographical locations. [ABSTRACT FROM AUTHOR]

Agriculture is essential for the existence of all human beings. It is not only a need, but it also contributes to national growth. Agriculture is the only industry that benefits both itself and the rest of the country. It not only offers food and raw materials to a substantial section of the people but also work prospects. Agriculture is the principal source of income for the majority of people in India. It has been in the country for thousands of years, and modern technology and equipment have substituted ancient farming practices. With the advancement of technology in every industry, it has become increasingly vital to incorporate technology into agriculture. Machine learning facilitates the study of large volumes of data and may deliver faster and more precise results, which can aid in the identification of lucrative possibilities and risky threats. The major goal of this research project is to develop a Machine Learning (ML) model to predict farm production. To estimate crop yields, the data was collected and trained using supervised machine learning with six distinct regression models. With a Mean absolute error (MAE) of 468.16 and a Cross-Validation score of 0.6087, Random Forest Regressor outperformed the other models. [ABSTRACT FROM AUTHOR]

Groundwater is an essential source of water especially in arid and semi-arid regions of the world. The demand for water due to exponential increase in population has created stresses on available groundwater resources. Further, climate change has affected the quantity of water globally. Many parts of Indian cities are experiencing water scarcity. Thus, assessment of groundwater potential is necessary for sustainable utilization and management of water resources. We utilized a novel ensemble approach using artificial neural network multi-layer perceptron (ANN-MLP), random forest (RF), M5 prime (M5P) and support vector machine for regression (SMOReg) models for assessing groundwater potential in the Parbhani district of Maharashtra in India. Ten site-specific influencing factors, elevation, slope, aspect, drainage density, rainfall, water table depth, lineament density, land use land cover, geomorphology, and soil types, were integrated for preparation of groundwater potential zones. The results revealed that the largest area of the district was found under moderate category GWP zone followed by poor, good, very good and very poor. Spatial distribution of GWP zones showed that Poor GWPZs are spread over north, central and southern parts of the district. Very poor GWPZs are mostly found in the north-western and southern parts of the district. The study calls for policy implications to conserve and manage groundwater in these parts. The ensembled model has proved to be effective for assessment of GWP zones. The outcome of the study may help stakeholders efficiently utilize groundwater and devise suitable strategies for its management. Other geographical regions may find the methodology adopted in this study effective for groundwater potential assessment. [ABSTRACT FROM AUTHOR]

Occurrences of extreme events, especially floods, have become more frequent and severe in the recent past due to the global impacts of climate change. In this context, possibilities for generating a near‐accurate streamflow forecast at higher lead times, which could be utilized for developing a reliable flood warning system to minimize the effects of extreme events, are highly important. This paper aims to investigate the potential of a novel hybrid modeling framework that couples the random forest algorithm, particle filter, and the HBV model for improving the overall accuracy of forecasts at higher lead times through the dynamic error correction schematic. The new framework simulates an ensemble of streamflow for estimating uncertainty associated with the predictions and is applied across two snow‐fed Himalayan rivers: the Beas River in India and the Sunkoshi River in Nepal. Several statistical indices along with graphical performance indicators were used for assessing the accuracy of the model performance and associated uncertainty. The modeling framework achieved the Nash Sutcliffe Efficiency of 0.94 and 0.98 in calibration and 0.95 and 0.99 in validation for the Beas and Sunkoshi river basin respectively for a 7‐day ahead forecast. Thus, the proposed framework can be considered as a promising tool having reasonably good performance in forecasting streamflow at a higher lead time. Key Points: Hybrid hydrological model integrates process‐based model with machine learning algorithm through data assimilation techniqueDynamic error correction framework capable of improving the streamflow forecast at longer lead time is proposedOverall the developed framework improves the forecast accuracy along with quantifying the model prediction uncertainty [ABSTRACT FROM AUTHOR]

Pigeon pea is the second most important grain legume in India, primarily grown under rainfed conditions. Any changes in agro-climatic conditions will have a profound influence on the productivity of pigeon pea (Cajanus cajan) yield and, as a result, the total pulse production of the country. In this context, weather-based crop yield prediction will enable farmers, decision-makers, and administrators in dealing with hardships. The current study examines the application of the stepwise linear regression method, supervised machine learning algorithms (support vector machines (SVM) and random forest (RF)), shrinkage regression approaches (least absolute shrinkage and selection operator (LASSO) or elastic net (ENET)), and artificial neural network (ANN) model for pigeon pea yield prediction using long-term weather data. Among the approaches, ANN resulted in a higher coefficient of determination (R2 = 0.88–0.99), model efficiency (0.88–1.00) with subsequent lower normalised root mean square error (nRMSE) during calibration (1.13–12.55%), and validation (0.33–21.20%) over others. The temperature alone or its interaction with other weather parameters was identified as the most influencing variables in the study area. The Pearson correlation coefficients were also determined for the observed and predicted yield. Those values also showed ANN as the best model with correlation values ranging from 0.939 to 0.999 followed by RF (0.955–0.982) and LASSO (0.880–0.982). However, all the approaches adopted in the study were outperformed the statistical method, i.e. stepwise linear regression with lower error values and higher model efficiency. Thus, these approaches can be effectively used for precise yield prediction of pigeon pea over different districts of Karnataka in India. [ABSTRACT FROM AUTHOR]

Rainfall has a direct effect on agriculture, hydroelectric generation, and water resources management, etc. Many natural catastrophes are also closely linked to rainfall intensity and duration, including flood and drought. Therefore, it is essential to have fast and reliable technique of forecasting rainfall intensity and duration for regional water resource management. Timely rainfall forecasting is also required to avoid and mitigate potential harmful impacts of natural catastrophes such as landslides, floods, and droughts. Rainfall prediction is usually based on numerical weather models combined with meteorological radar data. Such models have been used extensively in studies, including multiple regressions and climatology averaging techniques, numerical methods, and empirical formulations. Forecast accuracy depends on uncertainty, and probabilistic forecasting handles the challenge of unpredictability better than deterministic predictions. The behavior of Random Forest, Gradient Boosting, and Decision tree models has been studied to optimize the results generated from data fed into them. Gradient Boosting was found to work best among those tested, with features related to and affecting rainfall predictability giving an accuracy of 93%. Random forest and Decision tree method having 90% and 78.5% of accuracy was achieved respectively. It was also observed that Mean Absolute Error (MAE) is 1.54, Mean Squared Error (MSE) is 24.94, Root Mean Squared Error (RMSE) is 4.99 for the 40 year time period data. This prediction will be useful for the Meteorological Department, State Disaster Management Department, Water Resources Management Department of State including Dam and Reservoir inflow management. [ABSTRACT FROM AUTHOR]

Human life sustained for decades due to the availability of basic needs, and freshwater is one of them. However, groundwater quality is constantly under pressure. This can be attributed to anthropogenic activities not limited to urban areas but to rural zones. Machine learning methods like linear discriminant analysis (LDA), Classification and Regression Trees (CART), k-Nearest Neighbour (KNN), Support Vector Machines (SVM) and, Random Forest (RF) models were used to analyse groundwater quality variables. The mean accuracy of each classifier was calculated, and the obtained mean accuracies were 77.5% (LDA), 87% (CART), 96% (KNN), 93.5% (SVM) and 96% (RF). RF and KNN models were selected as optimal models with higher accuracy. This study made it apparent that machine learning algorithms can estimate and predict water quality variables with significant accuracy. In this study, the observations and variables were compared with the water quality index and drinking water limits provided by the Bureau of Indian Standards. The water quality index for each observation was calculated. If at least four variables have a higher value than prescribed limits, it was assigned a value of 1; if more than four variables reported higher values, it was assigned a value of 2. [ABSTRACT FROM AUTHOR]

Multimodel ensemble (MME) approach would help modellers to know the advantages of individual global circulation models (GCMs) and to avoid the weaknesses associated with them, and it would help the river basin modellers to make appropriate modelling decisions. The study highlights the river basin‐scale development of MME as a convenient way to reduce the parameter and structural uncertainties in the Coupled Model Intercomparison Project Phase 6 (CMIP6) GCMs simulations after identifying the best five CMIP6 GCMs based on the rating metric calculations. Furthermore, the performance of the MME was enhanced by integrating three machine learning algorithms (artificial neural network [ANN], random forest [RF], support vector machine [SVM]). Subsequently, comparative assessment depicted the improved performance in MME‐integrated ML algorithms compared to simple arithmetic mean (SAM) in simulating observed precipitation (P), maximum temperature (Tmax), and minimum temperature (Tmin) over the Damodar River basin (DRB), India. The statistical metrics indicate that the SVM and RF methods yielded better results than SAM and ANN methods, thus selected for future projections. The robustness of the MME‐RF and MME‐SVM approach has also been observed while capturing the spatial pattern as IMD‐observed with well representation of climate indices for both wet and dry seasons. Future projections with MME‐SVM and MME‐RF suggested a possible rise in mean annual P in the range of 1.4–15% and 6.8–39% with an increasing trend in temperature (Tmax, Tmin) under the SSP245 and SSP585 scenarios, respectively. Replicating the spatial pattern of the future climatic variables projections evinced a warmer and drier climate in the southwest part of the DRB for both SSP scenarios during wet and dry season and thence warned a probable drier condition on the southwest part of the DRB in future time slices. [ABSTRACT FROM AUTHOR]

The study on land use and land cover (LULC) changes assists in analyzing the change and regulates environment sustainability. Hence, this research analyzes the Northern TN coast, which is under both natural and anthropogenic stress. The analysis of LULC changes and LULC projections for the region between 2009–2019 and 2019–2030 was performed utilizing Google Earth Engine (GEE), TerrSet, and Geographical Information System (GIS) tools. LULC image is generated from Landsat images and classified in GEE using Random Forest (RF). LULC maps were then framed with the CA-Markov model to forecast future LULC change. It was carried out in four steps: (1) change analysis, (2) transition potential, (3) change prediction, and (4) model validation. For analyzing change statistics, the study region is divided into zone 1 and zone 2. In both zones, the water body shows a decreasing trend, and built-up areas are in increasing trend. Barren land and vegetation classes are found to be under stress, developing into built-up. The overall accuracy was above 89%, and the kappa coefficient was above 87% for all 3 years. This study can provide suggestions and a basis for urban development planning as it is highly susceptible to coastal flooding. [ABSTRACT FROM AUTHOR]