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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]

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]

Aim/Purpose This paper aims to analyze the availability and pricing of perishable farm produce before and during the lockdown restrictions imposed due to Covid-19. This paper also proposes machine learning and deep learning models to help the farmers decide on an appropriate market to sell their farm produce and get a fair price for their product. Background Developing countries like India have regulated agricultural markets governed by country-specific protective laws like the Essential Commodities Act and the Agricultural Produce Market Committee (APMC) Act. These regulations restrict the sale of agricultural produce to a predefined set of local markets. Covid-19 pandemic led to a lockdown during the first half of 2020 which resulted in supply disruption and demand-supply mismatch of agricultural commodities at these local markets. These demand-supply dynamics led to disruptions in the pricing of the farm produce leading to a lower price realization for farmers. Hence it is essential to analyze the impact of this disruption on the pricing of farm produce at a granular level. Moreover, the farmers need a tool that guides them with the most suitable market/city/town to sell their farm produce to get a fair price. Methodology One hundred and fifty thousand samples from the agricultural dataset, released by the Government of India, were used to perform statistical analysis and identify the supply disruptions as well as price disruptions of perishable agricultural produce. In addition, more than seventeen thousand samples were used to implement and train machine learning and deep learning models that can predict and guide the farmers about the appropriate market to sell their farm produce. In essence, the paper uses descriptive analytics to analyze the impact of COVID-19 on agricultural produce pricing. The paper explores the usage of prescriptive analytics to recommend an appropriate market to sell agricultural produce. Contribution Five machine learning models based on Logistic Regression, K-Nearest Neighbors, Support Vector Machine, Random Forest, and Gradient Boosting, and three deep learning models based on Artificial Neural Networks were implemented. The performance of these models was compared using metrics like Precision, Recall, Accuracy, and F1-Score. Findings Among the five classification models, the Gradient Boosting classifier was the optimal classifier that achieved precision, recall, accuracy, and F1 score of 99%. Out of the three deep learning models, the Adam optimizer-based deep neural network achieved precision, recall, accuracy, and F1 score of 99%. Recommendations for Practitioners Gradient boosting technique and Adam-based deep learning model should be the preferred choice for analyzing agricultural pricing-related problems. Recommendations for Researchers Ensemble learning techniques like Random Forest and Gradient boosting perform better than non-Ensemble classification techniques. Hyperparameter tuning is an essential step in developing these models and it improves the performance of the model. Impact on Society Statistical analysis of the data revealed the true nature of demand and supply and price disruption. This analysis helps to assess the revenue impact borne by the farmers due to Covid-19. The machine learning and deep learning models help the farmers to get a better price for their crops. Though the dataset used in this paper is related to India, the outcome of this research work applies to many developing countries that have similar regulated markets. Hence farmers from developing countries across the world can benefit from the outcome of this research work. Future Research The machine learning and deep learning models were implemented and tested for markets in and around Bangalore. The model can be expanded to cover other markets within India. [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]

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]

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]

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]

• This is done to spread awareness among the inhabitants of the country as they will come to know about these unknown people and what they did in the past, and can also be used for investigation to keep a record of the past criminals. • Image classification falls under supervised machine learning technique and uses various classification algorithms to identify a particular image label. • The aim of the paper is to identify the person and provide information that they did before or achieved something to be so famous to be included in this paper. The paper aims to classify the people who are not so famous and have a controversial background such as the politicians, the criminals/most wanted people all around India, the social reformers who are bringing the changes necessary for the nation and the sports athletes who are making the nation proud around the globe. This is done to spread awareness among the inhabitants of the country as they will come to know about these unknown people and what they did in the past, and can also be used for investigation to keep a record of the past criminals. Image classification falls under supervised machine learning technique and uses various classification algorithms to identify a particular image label. The aim of the paper is to identify the person and provide information that they did before or achieved something to be so famous to be included in this paper. This is achieved by implementing three major Machine Learning algorithms namely: Logistic Regression, Random Forest and SVM (Support Vector Machine), among which SVM classifier along with wavelet transformation, and vertical stacking of cropped raw pixel image and the cropped wavelet transformed image to achieve maximum precision and accuracy of 94.61%. [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]

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]

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]

Groundwater fluoride (F) occurrence and mobilization are controlled by geotectonic, climate, and anthropogenic activities, such as land use and pumping. This study delineates the occurrence and mobilization of F in groundwater in a semi-arid environment using groundwater, and an artificial intelligence model. The model predicts climate, soil type, and geotectonic as major predictors of F occurrence. We also present unsaturated zone F inventory, elemental compositions, and mineralogy from 25 boreholes in agricultural, forest, and grasslands from three different land use terrains in the study area to establish linkages with the occurrence of groundwater F. Normalized unsaturated zone F inventory was the highest in the area underlain by the granitic–gneissic complex (261 kg/ha/m), followed by residual soils (216 kg/ha/m), and Pleistocene alluvial deposits (78 kg/ha/m). The results indicate that the unsaturated zone mineralogy has greater control over F mobilization into the groundwater than unsaturated zone F inventory and land-use patterns. The presence of clay minerals, calcite, and Fe, Al hydroxides beneath the residual soils strongly retain unsaturated zone F compared with the subsurface beneath Pleistocene alluvial deposits, where the absence of these minerals results in enhanced leaching of unsaturated zone F. [ABSTRACT FROM AUTHOR]

A two‐level clustering approach is proposed for optimal design/expansion of a ground‐based precipitation monitoring network (GPN). It harnesses the advantages of Infinite Bayesian fuzzy clustering in the first level to partition the study area into homogeneous precipitation zones by considering structural/statistical characteristics and temporal variability of the observed precipitation. In the second level, an ensemble of hierarchical and partitional clustering techniques is considered in the time domain to effectively partition each zone into groups by considering weighted inter‐site dissimilarities of precipitation. The dissimilarities account for correlation, temporal dynamics, and fuzzy mutual information of precipitation at existing stations and possible new gauge locations. Key station's location in each group is identified by a proposed ranking procedure that accounts for population density, land‐use/landcover, and fuzzy marginal entropy of precipitation. For use with the approach, information on precipitation was derived for fine resolution ungauged grids covering the study area using random forest‐based regression relationships developed for gauged grids between merged multiple satellite‐based precipitation products (CHIRPS, IMERG) and ground‐based precipitation measurements. The potential of the proposed approach over other clustering‐based procedures is illustrated through a case study on a GPN comprising 1,128 gauges in Karnataka state (191,791 km2) of India. Potential locations for installing new gauges and areas where there is scope for relocating existing stations are identified. The proposed methodology appears promising and could be extended to design networks monitoring various other hydrometeorological variables. Key Points: Proposes a two‐level clustering approach for optimal design/expansion of ground‐based precipitation monitoring network (GPN)Approach harnesses advantages of infinite Bayesian fuzzy clustering and an ensemble of hierarchical and partitional clustering methodsMerged ground and multiple satellite‐based precipitation derived using random forest method is used to locate key stations to redesign GPN [ABSTRACT FROM AUTHOR]

Introduction: Unlike Western countries, many low- and middle-income countries (LMIC), like India, have a de-centralized emergency medical services (EMS) involving both semi-government and non-government organizations. It is alarming that due to the absence of a common ecosystem, the utilization of resources is inefficient, which leads to shortage of available vehicles and larger response time. Fragmentation of emergency supply chain resources motivates us to propose a new vehicle routing and scheduling model equipped with novel features to ensure minimal response time using existing resources. Materials and Methods: The data set of medical and fire-related emergencies from January 2018 to May 2018 of Uttarakhand State in India was provided by GVK Emergency Management and Research Institute (GVK EMRI) also known as 108 EMSs was used in the study. The proposed model integrates all the available EMS vehicles including partial outsourcing to non-ambulatory vehicles like police vans, taxis, etc., using a novel two-echelon heuristic approach. In the first stage, an offline learning model is developed to yield the deployment strategy for EMS vehicles. Seven well researched machine learning (ML) algorithms were analyzed for parameter prediction namely random forest (RF), convolutional neural network (CNN), k-nearest neighbor (KNN), classification and regression tree (CART), support vector machine (SVM), logistic regression (LR), and linear discriminant analysis (LDA). In the second stage, a real-time routing model is proposed for EMS vehicle routing at the time of emergency, considering partial outsourcing. Results and Discussion: The results indicate that the RF classifier outperforms the LR, LDA, SVM, CNN, CART and NB classifier in terms of both accuracy as well as F-1 score. The proposed vehicle routing and scheduling model for automated decision-making shows an improvement of 42.1%, 54%, 27.9% and 62% in vehicle assignment time, vehicle travel time from base to scene, travel time from scene to hospital, and total response time, respectively, in urban areas. [ABSTRACT FROM AUTHOR]

Accurate estimates of spatial above ground biomass (AGB) in tropical forests are important in understanding the global carbon cycle. Microwave and optical remote sensing datasets have been used extensively for AGB estimation, but their uses are restricted due to saturation in high biomass region. To overcome saturation issues of single sensor based models, the current paper uses a non-parametric Random Forest based approach to spatially estimate biomass over Indian forests using field inventory data in combination with optical (MODIS) and Microwave (L-band ALOS-PALSAR) images along with other bio-climatic parameters (e.g., rainfall, temperature) which significantly influence the biomass accumulation in an ecosystem. Plot level biomass estimates for 6678 sample plots (0.1 ha size), inventoried as part of robustly designed National Forest Inventory (NFI), are computed using volumetric equations, wood density and biomass expansion factors. Spatial above ground biomass estimates were generated using random forest model over two scenarios. Firstly, a single nation-wide model using all the available plot data and secondly, physiographic zone (14 zones over India) wise models with plot data over respective zones. AGB stored in Indian forest is estimated as 7952.3 million tonnes (Carbon equivalent: 3737.58 TgC) with a root mean square error (RMSE) of 31.2% using national level model. Physiographic zone level models estimated the country's biomass as 7597.45 million tonnes (Carbon equivalent: 3570.8 TgC). The above ground biomass estimates from our study indicates that the estimation error in physiographic zone model varies from 25.24% to 54.15% depending upon the sample size and biomass range. We observed that L-band microwave saturated in 140–160 Mg ha−1 range and currently available microwave wavelengths alone is not sufficient to predict entire range of biomass over Indian forests. Inclusion of multisource data using random forest regression model increase the saturation range to 350 Mg ha−1 which is a significant improvement as 94.7% of Indian forests are covered in this range. Model estimation error reduces to 25.6% in AGB range up to 350 Mg ha−1. • L-band ALOS PALSAR backscatter saturates in biomass range140–160 Mg ha−1. • Inclusion of multi source data improved the saturation point to ~350 Mg ha−1. • 94.7% of Indian forest cover is covered in AGB range below 350 Mg ha−1. • Biomass stored in Indian forest estimated as 7952.3 Mt. (carbon equivalent: 3737.58 TgC). [ABSTRACT FROM AUTHOR]

Present scenario problems were facing the crime rate increases day by day, due to crimes being a typical social issue influencing personal satisfaction and the economic development of the general public. It is a fundamental factor that decides if individuals move to another city and what spots should be kept away from when they travel. With the expansion of violations, law requirement organizations are proceeding to request progressed geographic data frameworks and new information mining to deal with enhanced crime examination and better secure their groups. Even though violations could happen all over, offenders must deal with wrongdoing openings they look in most commonplace territories for them. Therefore, it becomes vital to study the factors that impact the crime rate. To be better prepared to respond to criminal activity, it is important to understand the patterns in crime; this study analyzes crime data from all over the nation, available publicly on websites such as data.gov.in or Kaggle. This study investigates the relationship between various factors and the crime rate in India and focuses on the extent of effects of various factors on crime registered under IPC or SLL in all Indian states and the union territories. The study covers the data of all the Indian states and the union territories of periods ranging from 2001 to 2014. However, different classes of crimes have a slightly different range of years. The data for district-wise crimes in India is from 2001 to 2014, the data for crimes against SC, ST & Children is from 2001 to 2012, and the data for murder victims by age and gender is from 2001 to 2010. The findings show that these factors are crucial determinants of the rate of criminal cases registered in India. We unequivocally believe that discovering connections between wrongdoing components could profoundly help foresee the potentially risky hotspots later on. [ABSTRACT FROM AUTHOR]

Zero tillage is an important pathway to sustainable intensification and low-emission agriculture. However, quantifying the extent of zero tillage adoption at the field scale has been challenging, especially in smallholder systems where field sizes are small and there is limited ground data on zero tillage adoption. Remote sensing offers the ability to map tillage practices at large spatio-temporal scales, yet to date no studies have used satellite data to map zero tillage adoption in smallholder agricultural systems. In this study, we use Sentinel-2 satellite data, random forest classifiers, and Google Earth Engine to map tillage practices across India's main grain producing region, the Indo-Gangetic Plains. We find that tillage practices can be classified with moderate accuracy (an overall accuracy of 75%), particularly in regions with relatively large field sizes and homogenous crop management practices. We find that models that use satellite data from only the first half of the growing season perform as well as models that use data throughout the growing season, allowing for the creation of within-season tillage maps. Finally, we find that our model can generalize well through time in the western IGP, with reductions in accuracy of only 5–10%. Our results highlight the ability of Sentinel-2 satellite data to map tillage practices at scale, even in smallholder systems where field sizes are small and cropping practices are heterogeneous. [ABSTRACT FROM AUTHOR]