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This research was established to accurately forecast daily scale air quality index (AQI) which is an essential environmental index for decision-making. Researchers have projected different types of models and methodologies for AQI forecasting, such as statistical techniques, machine learning (ML), and most recently deep learning (DL) models. The modelling development was adopted for Delhi city, India which is a major city with air pollution issues simialir to entire urban cities of India especially during winter seasons. This research was predicted AQI using different versions of DL models including Long-Short Term Memory (LSTM), Bidirectional LSTM (Bi-LSTM) and Bidirectional Recurrent Neural Networks (Bi-RNN) in addition to Kernel Ridge Regression (KRR). Results indicated that Bi-RNN model consistently outperformed the other models in both training and testing phases, while the KRR model consistently displayed the weakest performance. The outstanding performance of the models development displayed the requirement of adequate data to train the models. The outcomes of the models showed that LSTM, BI-LSTM, KRR had lower performance compared with Bi-RNN models. Statistically, Bi-RNN model attained maximum cofficient of determination (R 2  = 0.954) and minimum root mean square error (RMSE = 25.755). The proposed model in this research revealed the robust predictable to provide a valuable base for decision-making in the expansion of combined air pollution anticipation and control policies targeted at addressing composite air pollution problems in the Delhi city., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Recurrent stock market fall and rise sequel by COVID-19, rising global inflation, increase in Fed interest rates, the unprecedented meltdown of technology stocks, fear of trade wars, tightening of governments' fiscal policies call for a new trend in international investing. It is time for the investors to rethink, rebalance and reset their investment strategies to position and protect their portfolios during and post-pandemic period. This paper attempts to forecast the gold prices for the post-pandemic era and explores whether gold will serve as a decisive hedge during this transition period. The techniques of ARCH, GARCH, E-GARCH, A-PARCH, and GARCH-M is employed in forecasting the conditional volatility of gold spot price from Multi Commodity Exchange (MCX) of India. A total of 3631 observations were collected from the daily spot prices of gold from January 2009 to December 2022. The findings show that the gold prices in India are highly persistent similar to other emerging markets and that gold will remain a safe haven for investors and institutional investors in the post-pandemic period. This paper is the first of its kind to forecast gold prices for the post-pandemic period. The forecast price of 10-gram gold is expected to trade for 65,948 ₹ in the Indian MCX by 2026 if the gold prices behold its previous momentum. This forecast will help the investors to plan their portfolio diversification for the post-pandemic period. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this paper is to provide a better method for quality management to maintain an essential level of quality in different fields like product quality, service quality, air quality, etc. Design/methodology/approach: In this paper, a hybrid adaptive time-variant fuzzy time series (FTS) model with genetic algorithm (GA) has been applied to predict the air pollution index. Fuzzification of data is optimized by GAs. Heuristic value selection algorithm is used for selecting the window size. Two algorithms are proposed for forecasting. First algorithm is used in training phase to compute forecasted values according to the heuristic value selection algorithm. Thus, obtained sequence of heuristics is used for second algorithm in which forecasted values are selected with the help of defined rules. Findings: The proposed model is able to predict AQI more accurately when an appropriate heuristic value is chosen for the FTS model. It is tested and evaluated on real time air pollution data of two popular tourism cities of India. In the experimental results, it is observed that the proposed model performs better than the existing models. Practical implications: The management and prediction of air quality have become essential in our day-to-day life because air quality affects not only the health of human beings but also the health of monuments. This research predicts the air quality index (AQI) of a place. Originality/value: The proposed method is an improved version of the adaptive time-variant FTS model. Further, a nature-inspired algorithm has been integrated for the selection and optimization of fuzzy intervals. [ABSTRACT FROM AUTHOR]

Precise food price forecasting is crucial for any country, and searching for appropriate approach(s) from an assortment of available strategies toward this objective is an open problem. The current Indian Wholesale Price Index (WPI) series contains sixty individual food items in the 'manufacture of food product' category. This work considered the monthly data from April 2011 to June 2022, i.e., one hundred thirty-five months' data of these sixty WPIs. The researchers extracted the linearity, curvature, and autocorrelation features for each WPI. The curvature and linearity-based grouping of these WPIs revealed that the WPIs are heterogeneous. This work proposed an extreme learning machine (ELM) approach for forecasting these WPIs. The present work employed the following twenty-two time-series forecasting techniques: six standard methods (Auto ARIMA, TSLM, SES, DES, TES, and Auto ETS), five neural networks (Auto FFNN, Auto GRNN, Auto MLP, Auto ELM, and proposed ELM), and eleven state-of-art techniques (two ARIMA-ETS based ensembles, an ARIMA-THETAF-TBATS based ensemble, one MLP, and seven LSTM-based models) to identify the best forecasting approach for these WPIs. For the majority of WPIs, the offered ELM attained suitable performance in the case of fifteen months of out-of-sample forecasting. Nearly eighty-seven percent of cases achieved high accuracy (MAPE = ten) and outshined others. Upon accuracy comparison, both forecast-MAPE and forecast-RMSE, between the proposed ELM and others, this paper observed that the proposed ELM's performance is more favorable. This paper's findings imply that the proposed ELM is a promising prospect to offer accurate forecasts of these sixty WPIs. [ABSTRACT FROM AUTHOR]

Deep learning models in recent times have shown promising results for solar energy forecasting. Solar energy depends heavily on local weather conditions, and as a result, typically hundreds of models are built, which need site and season-specific training. The model maintenance and management also become a tedious job with such a large number of models. Here, we are motivated to use transfer learning to accommodate local variations in the solar pattern over the available global pattern. It may also be noted that apparently transfer learning has been rarely/never used for solar forecasting. In this paper, we have proposed a bidirectional gated recurrent unit (BGRU) based model, which employs transfer learning for short-term solar energy forecasting. The said model yields better forecasting accuracy compared to site-specific models with a lower variance. It also takes 39.6% less parameters and 76.1% reduced time for training. The current literature suggests that selection of base scenario for transfer learning is an open problem and in this paper, we have also proposed an intuitive strategy for the same. The effectiveness of the same is established through empirical study. [ABSTRACT FROM AUTHOR]

This paper precisely estimates the looming demand for hydrogen within the transportation sector, spanning 14 pivotal G20 nations from 2022 to 2050, anchored in the global vehicle hydrogen market penetration rate. Employing a robust techno-economic modeling methodology and leveraging Geographic Information System (GIS) for enriched visualization, it unveils distinct environmental dividends across three thoughtfully devised scenarios: Business- as -Usual, Moderate, and Aggressive. Findings herald India and China as pinnacles of hydrogen demand, with South Korea and Japan tailing closely. The study forecasts the energy consumption for solar hydrogen production in G20 countries to oscillate between 3.02 and 2.89 million GWh in 2022, while production costs are anticipated to range from $8.47/kg to $10.01/kg. In an assertive scenario, the paper illuminates a pathway towards achieving a significant CO 2 reduction, unmasking environmental savings of up to a staggering 48%, equivalent to 1.5 million mtCO 2 , by 2050. [Display omitted] • Hydrogen demand of G20 countries is analyzed using GIS and techno-economic models. • The projected adoption rates of hydrogen vary among the G20 countries. • Solar energy is crucial in determining each region's annual energy demand. • China, US, and India have the highest solar potential throughout the year. • The environmental analysis shows 850–1.5 million mtCO2 total emissions by 2050. [ABSTRACT FROM AUTHOR]

The emergence of the latest technologies gives rise to the usage of noninvasive techniques for assisting health-care systems. Amongst the four major cardiovascular diseases, stroke is one of the most dangerous and life-threatening disease, but the life of a patient can be saved if the stroke is detected during early stage. The literature reveals that the patients always experience ministrokes which are also known as transient ischemic attacks (TIA) before experiencing the actual attack of the stroke. Most of the literature work is based on the MRI and CT scan images for classifying the cardiovascular diseases including a stroke which is an expensive approach for diagnosis of early strokes. In India where cases of strokes are rising, there is a need to explore noninvasive cheap methods for the diagnosis of early strokes. Hence, this problem has motivated us to conduct the study presented in this paper. A noninvasive approach for the early diagnosis of the strokes is proposed. The cascaded prediction algorithms are time-consuming in producing the results and cannot work on the raw data and without making use of the properties of EEG. Therefore, the objective of this paper is to devise mechanisms to forecast strokes on the basis of processed EEG data. This paper is proposing time series-based approaches such as LSTM, biLSTM, GRU, and FFNN that can handle time series-based predictions to make useful decisions. The experimental research outcome reveals that all the algorithms taken up for the research study perform well on the prediction problem of early stroke detection, but GRU performs the best with 95.6% accuracy, whereas biLSTM gives 91% accuracy and LSTM gives 87% accuracy and FFNN gives 83% accuracy. The experimental outcome is able to measure the brain waves to predict the signs of strokes. The findings can certainly assist the physicians to detect the stroke at early stages to save the lives of the patients. [ABSTRACT FROM AUTHOR]

There remains a gap between the production of scientifically robust forecasts and the translation of these forecasts into useful information such as daily "bulletins" for decision-makers in early warning systems. There is significant published literature on best practice in communicating risk information but very little to guide and provide advice on the process of how these bulletins have been, or should be, developed. This paper reviews two case studies where bulletins were developed for national and district-level government agencies and humanitarian responders: daily reports in response to cyclones Idai and Kenneth in Mozambique and prototype landslide forecast bulletins in the Nilgiris and Darjeeling districts of India. Primary data were collected from producers and intermediaries of the bulletins via interview, and secondary data were analysed on iterative changes in the bulletin development, minutes from internal discussions, and feedback from users to extract learning on both the content and process of developing the bulletins. There were significant similarities in the type of content included in the bulletins, such as the layout, choice of words, and use of visualisation that was consistent with published best practices. Both case studies experienced challenges dealing with uncertainty, complexity, and whether to include advice. There were also similarities in the processes and approaches taken to develop the bulletins. Both case studies took an iterative approach, developed feedback mechanisms, benefitted from experienced multidisciplinary teams, and emphasised the need for strong inter-relationships and the importance and value of preparedness and protocols. A major challenge was the difficulty in balancing science capabilities, including issues related to data scarcity, with user needs, which did not become significantly easier to deal with given more time availability. In particular, there were tensions between developing new forecast products that were urgently needed by users against the limited time for testing and refinement of those forecasts and the risk of misinforming decisions due to uncertainty in the information based on limited data. The findings indicate that whilst more research is needed into existing or best practice processes to develop content for forecast bulletins, there is an existing body of experiential and intuitive knowledge and learning that already exists but that is not yet captured in an appropriate format that could be of significant interest and value to those developing forecast information. This paper goes some way to capturing some of the learning from translating scientific forecasts into useful information, in particular on both the content and the process of developing forecast bulletins for decision-making. [ABSTRACT FROM AUTHOR]

In India, in metropolitan cities, a common problem seen is traffic jams or slowdown on traffic due to potholes. Most of the road accidents are due to potholes. In this paper, the authors have tried to ensure that the road surface quality and the potholes should be monitored continuously and must be repaired as necessary. The best possible distribution of resources could be attained when the real-time data is gathered from the mounted sensory system and displayed and visualized in a way (Google Maps) which is comprehensive and empirical. Different sensors would be mounted onto the public transport vehicles. The method mentioned in this paper uses the ultrasonic sensor and the accelerometer to send the data regarding the intensity of pothole straightaway to the cloud data storage, along with the location coordinates of the potholes and the time stamp. Different databases such as traffic density of the city, rain intensity of different areas is used to train different machine learning models to predict the intensity of potholes at the particular locations and get the best possible accuracy. The best-case accuracy attained was 35.5%. The pothole intensity data is visualized through a heat map application. This system helps in reducing the frequency of traffic jams in any city of India. The method suggested in the paper would help in reducing the number of accidents happening in India due to the potholes. [ABSTRACT FROM AUTHOR]

Global warming caused by CO2 emissions will directly harm the health and quality of life of people. Accurate prediction of CO2 emissions is highly important for policy-makers to formulate scientific and reasonable low-carbon environmental protection policies. To accurately predict the CO2 emissions of the world's major economies, this paper proposes a new fractional grey Bernoulli model (FGBM(1,1, t α )). First, this paper introduces the modeling mechanism and characteristics of the FGBM(1,1, t α ) model. The new model can be transformed into other grey prediction models through parameter adjustment, so the new model exhibits high adaptability. Second, this paper employs four carbon emission datasets to establish a grey prediction model, calculates model parameters with three optimization algorithms, adopts two evaluation criteria to evaluate the accuracy of the model results, and selects the optimization algorithm and model results that yield the highest model accuracy, which verifies that the FGBM(1,1, t α ) model is more feasible and effective than the other six grey models. Finally, this paper applies the FGBM(1,1, t α ) model to predict the CO2 emissions of the USA, India, Asia Pacific, and the world over the next 5 years. The forecast results reveal that from 2020 to 2024, the CO2 emissions of India, the Asia Pacific region, and the world will gradually rise, but that in USA will slowly decline over the next 5 years. [ABSTRACT FROM AUTHOR]

The mountainous areas are vulnerable to climate change and may have many socio-economic and environmental implications. The changing pattern of meteorological variables has deleterious effects on natural resources and livelihood. This paper makes an attempt to analyse trend and forecast metrological variables in Nainital district of India. Monthly, seasonal, and annual trends in rainfall and temperature were examined by Modified Mann–Kendall during 1989–2019. The magnitude of trend in temperature and rainfall was determined using Sen's slope estimator. Ensemble machine learning model was utilized for forecasting the variables for the next 16 years (2020–2035). The effectiveness of the model was examined through statistical performance assessors. The results revealed a significant increasing trend in the rainfall (at the rate of 9.42 mm/year) during 1989–2019. Increasing trend in the mean, minimum, and maximum temperatures on an annual basis was observed in the district. A remarkable increase in the rainfall and temperature was forecasted during various seasons. The findings of the study may help the stakeholders in devising suitable adaptation measures to climate variability. The bagging approach has shown its effectiveness in forecasting meteorological variables. The other geographical regions may find the methodology effective for analyzing climate variability and lessening its impact. [ABSTRACT FROM AUTHOR]

Efforts to leverage peer effects by changing assignment have often fallen short due to endogenous peer choice. To address this, I build a two-part model: agents form networks via continuous linking decisions; conditional on realized networks, outcomes are determined. I provide results on identification of both parts of the model. Using data from a randomized study in India, I estimate the model, assess its performance in out-of-sample prediction, and simulate outcomes under preferential assignment rules. This paper contributes new methodology for identifying effects of alternative assignments in the presence of network endogeneity, as well as identification of network formation models. [ABSTRACT FROM AUTHOR]

Modeling hydrological processes for managing the available water resources effectively is often complex due to the existence of high nonlinearity, and the associated prediction uncertainty mainly arising from model inputs, parameters, and structure. Despite several attempts to quantify the model prediction uncertainty, reducing the same for improving the reliability of models is indispensable for their wider acceptance. This paper presents a novel modeling framework for minimizing the prediction uncertainty in the streamflow simulation of the conceptual hydrological model (HBV) by integrating with the Bayesian‐based Particle Filter technique (PF) and machine learning algorithm (Random Forest algorithm, RF). Initially, the streamflow prediction interval (PI) is derived from the stochastically estimated parameters of the HBV model through the PF technique (HBV‐PF model). As the HBV‐PF model quantifies only parametric uncertainty, the RF algorithm was employed (HBV‐PF‐RF model) for further minimizing the prediction uncertainty by inherently taking care of different sources of uncertainty. The RF algorithm inherently combines the physics of the hydrological system (i.e., process‐based variables) with machine learning‐based approach to minimize the overall prediction uncertainty. The proposed framework was analyzed on Nepal and India's Sunkoshi and Beas River basins, through several statistical performance indices for assessing the accuracy and uncertainty of the model prediction. The framework was observed to be consistently improving the model performance minimizing the uncertainty in both watersheds. Therefore, the proposed framework can be considered to be more reliable in improving the prediction capability of hydrological models. Key Points: Development of a physics‐aware machine learning based hydrological model for streamflow simulationThe proposed framework characterizes the model prediction uncertainty, accounting different sources of uncertainty implicit/explicitlySignificant reduction in overall prediction uncertainty is achieved [ABSTRACT FROM AUTHOR]

The world is currently facing a pandemic called COVID-19 which has drastically changed our human lifestyle, affecting it badly. The lifestyle and the thought processes of every individual have changed with the current situation. This situation was unpredictable, and it contains a lot of uncertainties. In this paper, the authors have attempted to predict and analyze the disease along with its related issues to determine the maximum number of infected people, the speed of spread, and most importantly, its evaluation using a model-based parameter estimation method. In this research the Susceptible-Infectious-Recovered model with different conditions has been used for the analysis of COVID-19. The effects of lockdown, the light switch method, and parameter variations like contact ratio and reproduction number are also analyzed. The authors have attempted to study and predict the lockdown effect, particularly in India in terms of infected and recovered numbers, which show substantial improvement. A disease-free endemic stability analysis using Lyapunov and LaSalle's method is presented, and novel methods such as the convalescent plasma method and the Who Acquires Infection From Whom method are also discussed, as they are considered to be useful in flattening the curve of COVID-19. [ABSTRACT FROM AUTHOR]

Floods are a recurrent phenomenon in many parts of the world. In India, they occur almost every year in some parts of the country and in recent years several areas have been experiencing severe floods, which were almost unheard of a few decades ago. Advances in data collection and in methods of rainfall and hydrologic forecasting and improved flood management by governmental organizations notwithstanding flood risk in each flood season year after year is quite high and consequent loss of life and property continues unabated. This paper revisits flood management in the country and discusses strategies for reducing flood risk and the resulting damage. It also reflects on the role climate change, besides anthropogenic changes, may be playing in the havoc being wrought each year by floods. [ABSTRACT FROM AUTHOR]

This paper proposes a comparison of various time series forecasting models to forecast annual data on sugarcane production over 63 years from 1960 to 2022. In this research, the Mean Forecast Model, the Naive Model, the Simple Exponential Smoothing Model, Holt's model, and the Autoregressive Integrated Moving Average time series models have all been used to make effective and accurate predictions for sugarcane. Different scale‐dependent error forecasting techniques and residual analysis have been used to examine the forecasting accuracy of these time series models. SE of Residuals, Root Mean Squared Error (RMSE), Mean Absolute Error (MAE) and Akaike's Information Criterion (AIC) are used to analyse the forecast's accuracy. The best model has been selected based on the predictions with the lowest value, according to the three‐performance metrics of RMSE, MAE, and AIC. The estimated sugarcane production shows an increasing trend for the next 10 years and is projected to be 37,763.38 million tonnes in the year 2032. Further, empirical results support the plan and execution of viable strategies to advance sugarcane production in India to fulfil the utilisation need of the increasing population and further improve food security. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic is wreaking havoc on society and the current situation the country is in now clearly shows the failure of predicting the second wave in India. Several variants of the coronavirus responsible for COVID-19 have been detected around the world. With several mutations, some are said to be more contagious than the original strain; the recent one is the Indian variant. This necessitates a wholesome study for modeling and forecasting COVID-19 cases in India, which will help us to be prepared. The bottleneck underlines here is the very nature of the virus, which is continuously mutating and unavailability to real-time and precise data for the purpose. This paper is an effort in this regard to the model and forecast the second wave in India. The dataset covers the period 2021-01-16 to 2021-05-16, where different models were used. SARIMA modeling was used for forecasting the cases, deaths and vaccinations using the best-fitted model. The results depict that the prediction was good as the predicted figures were found to be close to the observed values. In this context, we tried to study the pattern of propagation of this variant in India by modeling and forecasting the new deaths, new cases, total deaths, total cases and the total vaccination. Findings are alarming, especially, in the context of the difficulties of the health system in India. [ABSTRACT FROM AUTHOR]

The need for and the use of different data assimilation techniques to improve the quality of streamflow forecast is now well established. In this paper, the goal is to demonstrate the power of a new class of methods known as the forward sensitivity method (FSM) which is based on the temporal evolution of model sensitivities with respect to the control variables consisting of initial conditions and parameters. FSM operates in two phases: the first phase provides a simple algorithm for placing observations at or near where the square of forward sensitivities attains their maximum values. Using only this selected subset of observations in a weighted least squares method, the second phase then provides an estimate of the unknown elements of the control variables. In this paper, FSM based assimilation is applied to a simple class of two parameter model in a medium‐sized agriculture dominant watershed lying in the Krishna River Basin, India. Four assimilation scenarios were tested to determine the effect of assimilating only sensitive observations as well as the impact of temporally evolving initial condition sensitivity. Sensitivity results showed that observations during the monsoon time alone are enough for assimilation purposes, which has helped in reducing the computational time greatly. Assimilation and forecast results also indicated that the scenarios which assimilated only sensitive observations are better in estimating daily streamflow. From the obtained results, it is concluded that FSM based assimilation has significant potential to improve the streamflow simulations, especially in places where data availability remains a major challenge. Key Points: The potential of assimilating only sensitive soil moisture observations on streamflow forecast was demonstrated using a novel forward sensitivity method (FSM) methodImpact of temporally evolving sensitivity of initial condition on hydrological model performance during assimilation was determinedThe sensitivity‐based soil moisture assimilation was effective up to 45 lead days during streamflow forecast [ABSTRACT FROM AUTHOR]

The objective of the paper is to compare hybrid conjunction models with conventional models for the reduction of errors in weather forecasting. Besides the simple models like RBF model, SMO model, and LibSVM model, different hybrid conjunction models have been used for forecasting under different schemes. The forecasts from these models are further compared on the basis of errors calculated and time taken by the hybrid models and simple models in order to forecast weather parameters. In this paper, conjunction models over the convectional models are designed for forecasting the weather parameters for the reduction of error. India is a tropical country with variations in weather conditions. The objective is to build a conjunction model with less error to forecast weather parameters. A hybrid conjunction model is developed and analysed for different weather parameters for different metropolitan cities of India. Performance measurement is analysed for weather parameters. It is observed that, on the basis of error comparison and time taken by the models, the hybrid wavelet-neuro-RBF model gives better results as compared to the other models due to lower values of determined errors, better performance, and lesser time taken. The study becomes significant as weather forecasting with accuracy is a complex task along with the reduction of prediction error by the application of different models and schemes. It is concluded that the proposed hybrid model is helpful for forecasting and making policies in advance for the betterment of the human being, farmers, tourists, and so on as in all these activities, weather forecast plays an important role. [ABSTRACT FROM AUTHOR]

Summary: The importance of monsoon rainfall cannot be ignored as it affects round‐the‐year activities ranging from agricultural to industrial. Due to the nonlinear and dynamic nature of monsoon rainfall, its accurate forecast becomes a challenging task. In this paper, we propose a novel deep and wide neural network model to predict summer monsoon rainfall using historical time‐series data of 118 years. We have considered Rajasthan as our area of study, which is the largest state of India with varying atmospheric regions. Two different types of datasets are used: gridded rainfall dataset from the Indian Meteorological Department (IMD) and station rainfall data from Water Resources Department (WRD). Results obtained on both the datasets demonstrate that a single model works well in efficiently predicting summer monsoon rainfall in different geographical regions of Rajasthan, ranging from plains and plateaus to deserts and hills. We compare the results with various advanced deep learning algorithms such as 1‐dimensional convolutional neural network, long short term memory, and multilayer perceptron. The comparison results show that the proposed method clearly outperforms these algorithms in terms of its forecasting ability, which is evaluated using root mean square error and mean absolute error. [ABSTRACT FROM AUTHOR]

Corona Virus Disease 2019 (COVID-19) is a continuing extensive incident globally affecting several million people's health and sometimes leading to death. The outbreak prediction and making cautious steps is the only way to prevent the spread of COVID-19. This paper presents an Adaptive Neuro-fuzzy Inference System (ANFIS)-based machine learning technique to predict the possible outbreak in India. The proposed ANFIS-based prediction system tracks the growth of epidemic based on the previous data sets fetched from cloud computing. The proposed ANFIS technique predicts the epidemic peak and COVID-19 infected cases through the cloud data sets. The ANFIS is chosen for this study as it has both numerical and linguistic knowledge, and also has ability to classify data and identify patterns. The proposed technique not only predicts the outbreak but also tracks the disease and suggests a measurable policy to manage the COVID-19 epidemic. The obtained prediction shows that the proposed technique very effectively tracks the growth of the COVID-19 epidemic. The result shows the growth of infection rate decreases at end of 2020 and also has delay epidemic peak by 40–60 days. The prediction result using the proposed ANFIS technique shows a low Mean Square Error (MSE) of 1.184 × 10–3 with an accuracy of 86%. The study provides important information for public health providers and the government to control the COVID-19 epidemic. [ABSTRACT FROM AUTHOR]

The Air Quality Index (AQI) dataset contains information on measurements of pollutants and ambient air quality conditions at certain location that can be used to predict air quality. Unfortunately, this dataset often has many missing observations and imbalanced classes. Both of these problems can affect the performance of the prediction model. In particular, predictions for the minority class are very important because inaccurate predictions can be fatal or cause big losses. Moreover, the missing data may lead to biased results. This paper proposes the single imputation of the median and the multiple imputations of the k -Nearest Neighbor (KNN) regressor to handle missing values of less than or equal to 10% and more than 10%, respectively. At the same time, the SMOTE-Tomek Links address the imbalanced class. These proposed approaches to handle both issues are then used to assess the air quality prediction of the India AQI dataset using Naive Bayes (NB), KNN, and C4.5. The five treatments show that the proposed method of the Median-KNN regressor-SMOTE-Tomek Links is able to improve the performance of the India air quality prediction model. In other words, the proposed method succeeds in overcoming the problems of missing values and class imbalance. [ABSTRACT FROM AUTHOR]

Agriculture is the primary profession in India which relies on several climatic factors such as rainfall, temperature, humidity, etc., for the successful growth of crops. Weather and drought forecasting may help to take preventive measures in an unusual situation due to crop failure. Most of the existing work attempts to forecast the weather or analyse the reason for the occurrence of drought and its effects in the past. This paper adopts the machine learning model called the long short-term memory (LSTM) neural network to forecast the long-term rainfall and standardised precipitation index for drought estimation. The predicted annual rainfall from LSTM is taken as an input to forecast the drought conditions of India for the upcoming years, which is a unique approach and objective, and based on this work appropriate decisions can be made for future actions. The past 117 years of rainfall and drought conditions are compared with the recent 50 years by analysing the data in several ways in order to predict the future scenario. The forecasted results are compared with actual observations to demonstrate the effectiveness of the LSTM model to produce adequate results. The error and network loss of the model is 0.059 and 0.0036, which is minimal, and the forecasted rainfall level is almost equal to actual level specifically accuracy is 99.46% for the previous year, 2021. It was found that there is a rainfall decline of 0.04% every year. Apart from the prediction for the country, a clear picture of the region regarding drought forecasts is presented in this work. The real-time drought level is mild and moderate for most of the regions in the country which matches with the drought level determined using forecasted rainfall. [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]

This paper attempts to estimate long-run forecasting of Indian GDP for the post-COVID period using the factor error correction model (FECM). The model builds on a dynamic factor model that directly and indirectly captures many dimensions affecting the cycles of GDP. The availability of big data enables the extraction of a few common factors from large dimensions, which essentially produces better precision in forecasting estimates. We first extract leading factors and then add proxy policy variables to establish their long-run relationship with the GDP which produces insignificant in-sample bias. The estimated long-run relationship has been employed to predict GDP for 2022–35. We found three major dynamic factors that capture 80% of variations from 56 quarterly variables of the Indian economy. These three factors with their four lags and four exogenous policy instruments have been included in the FECM model for forecasting estimation. We find that the real GDP is expected to grow at 4–8% annually, depending upon the actual realisation of external shocks and policies. The expected rise in temperature and oil prices seems to be dampening the growth. Whereas, the institutional reforms for making more effective public investment and the currency digitalisation that reduces cash requirements could play an expansionary role. If the oil price and the temperature remain at the current level, the growth rate can go closer to approximately 8%. [ABSTRACT FROM AUTHOR]

This paper presents a simple and effective framework to combine various data-driven machine learning (ML) algorithms for short-range reservoir inflow forecasting, including the large-scale climate phenomenon indices addressing forecasting uncertainty. Random Forest (RF), Gradient Boosting Regressor (GBR), K-Nearest Neighbors Regressor (KNN), and Long Short-Term Memory (LSTM) were employed for predicting daily reservoir inflows considering various climate phenomenon indices (e.g., Arctic Oscillation, North Atlantic Oscillation, and Southern Oscillation Index) and hydroclimatic variables (e.g., precipitation), accounting for time-lag effects. After training the individual ML algorithm, a framework was developed to create an ensemble model using a robust weighted voting regressor (VR) method to quantify forecasting uncertainty and to improve model performances. The results of the study reveal that, for 2-day forecasts, the LSTM approach has the greatest influence on prediction accuracy, followed comparably by each model. However, none of the four models seem to be noticeably superior to the VR method, regardless of the prediction lead time. The developed framework was examined on a tropical reservoir, Bhadra reservoir Tunga-Bhadra River, located in India. [ABSTRACT FROM AUTHOR]

Steady‐state forecasting is indispensable for power system planning and operation. A forecasting model for inputs considering their historical record is a preliminary step for such type of studies. Since the historical data quality is decisive in edifice an accurate forecasting model, data preprocessing is essential. Primarily, the quality of raw data is affected by the presence of outliers, and preprocessing refers to outlier detection and correction. In this paper, an effort is made to improve the existing sliding window prediction‐based preprocessing method. The recommended reforms are the calculation of appropriate window width and a new outlier correction approach. The proposed method denoted as improved sliding window prediction‐based preprocessing is applied to the historical data of PV generation, load power, and the ambient temperature of different time‐steps collected from various places in the United States and India. Firstly, the method's efficacy through detailed result analysis demonstrating the proposed preprocessing as a better way than its precursor and k‐nearest neighbor approach is presented. Later, the improved out‐of‐sample forecasting accuracy canonizes the proposed method's concert compared to both the above techniques and the case without preprocessing. [ABSTRACT FROM AUTHOR]

Indoor air pollution (IAP) has become a serious concern for developing countries around the world. As human beings spend most of their time indoors, pollution exposure causes a significant impact on their health and well-being. Long term exposure to particulate matter (PM) leads to the risk of chronic health issues such as respiratory disease, lung cancer, cardiovascular disease. In India, around 200 million people use fuel for cooking and heating needs; out of which 0.4% use biogas; 0.1% electricity; 1.5% lignite, coal or charcoal; 2.9% kerosene; 8.9% cow dung cake; 28.6% liquified petroleum gas and 49% use firewood. Almost 70% of the Indian population lives in rural areas, and 80% of those households rely on biomass fuels for routine needs. With 1.3 million deaths per year, poor air quality is the second largest killer in India. Forecasting of indoor air quality (IAQ) can guide building occupants to take prompt actions for ventilation and management on useful time. This paper proposes prediction of IAQ using Keras optimizers and compares their prediction performance. The model is trained using real-time data collected from a cafeteria in the Chandigarh city using IoT sensor network. The main contribution of this paper is to provide a comparative study on the implementation of seven Keras Optimizers for IAQ prediction. The results show that SGD optimizer outperforms other optimizers to ensure adequate and reliable predictions with mean square error = 0.19, mean absolute error = 0.34, root mean square error = 0.43, R2 score = 0.999555, mean absolute percentage error = 1.21665%, and accuracy = 98.87%. [ABSTRACT FROM AUTHOR]

Solar photovoltaic (PV) power is emerging as one of the most viable renewable energy sources. The recent enhancements in the integration of renewable energy sources into the power grid create a dire need for reliable solar power forecasting techniques. In this paper, a new long‐term solar PV power forecasting approach using long short‐term memory (LSTM) model with Nadam optimizer is presented. The LSTM model performs better with the time‐series data as it persists information of more time steps. The experimental models are realized on a 250.25 kW installed capacity solar PV power system located at MANIT Bhopal, Madhya Pradesh, India. The proposed model is compared with two time‐series models and eight neural network models using LSTM with different optimizers. The obtained results using LSTM with Nadam optimizer present a significant improvement in the forecasting accuracy of 30.56% over autoregressive integrated moving average, 47.48% over seasonal autoregressive integrated moving average, and 1.35%, 1.43%, 3.51%, 4.88%, 11.84%, 50.69%, and 58.29% over models using RMSprop, Adam, Adamax, SGD, Adagrad, Adadelta, and Ftrl optimizer, respectively. The experimental results prove that the proposed methodology is more conclusive for solar PV power forecasting and can be employed for enhanced system planning and management. [ABSTRACT FROM AUTHOR]

In this paper, a combined approach of Principal Component Analysis (PCA)-based Extreme Learning Machine (ELM) for boiler output forecasting in a thermal power plant is presented. The input used for this prediction model is taken from the boiler unit of the Yermarus Thermal Power Station (YTPS), India. Calculation of the accurate electrical output of a boiler in an operating system requires the knowledge of hundreds of operating parameters. The dimensionality of the input dataset is reduced by applying principal component analysis using IBM@SPSS Software. In the process of principal component analysis, a dataset of 232 parameters is standardized into 16 principal components. The total dataset collected is divided into training and testing datasets. The extreme learning machine is designed for various activation functions and the number of neurons. Sigmoid and hyperbolic tangent activation functions are studied here. Its generalization performance is examined in terms of the Mean Square Error (MSE), Mean Absolute Error (MAE), Root Mean Square (RMSE), and Mean Absolute Percentage Error (MAPE). ELM and PCA–ELM are compared. In both the ELM and PCA–ELM models, when the extreme learning machine was designed with a sigmoid activation function with 100 nodes in the hidden layer, RMSE was 5.026 and 4.730, respectively. Therefore, the developed combined approach of PCA–ELM proved as a promising technique in forecasting with reduced errors and reduced time. [ABSTRACT FROM AUTHOR]

The groundwater modelling tool is an important structural tool for solving many groundwater-related problems. Visual-MODFLOW is one of such tool that uses finite-difference (FD) method to unravel the problems. In this paper, Visual-MODFLOW 2000 has been utilized for analysing the groundwater-level simulation in Purba (East) Midnapur, West Bengal, India. The data of pumping well discharge have been collected at two different time periods 2002 and 2012. The simulated pumping well records for 2012 based on the pumping well discharges measured in 2002 and the observed records in 2012 have been critically examined, compared, and correlation coefficients are found out to signify data validity. Also simulated groundwater-level data of 2019 to 2023 based on well discharge records in 2002 and 2012 have also been correlated for its justification. This paper concluded that the groundwater flow occurs from south to north direction of East Midnapur, as the saline water intrusion from the nearby sea takes place into the aquifers towards inland direction. It confirms a good agreement with future simulation data. In the real field situation, saline water encroachments have affected the aquifers and it has covered up to 50 km of location from Kalindi (near seashore) to Nandakumar (inland) location. This paper focuses on the groundwater-level forecasting in prospective groundwater managing scenarios in the region concern. [ABSTRACT FROM AUTHOR]

The number of deaths due to road accident is increasing day by day and has become an alarming global problem over the decades. India, with her rising motorization is no stranger to this global catastrophe. In this paper two relatively simple yet powerful and versatile techniques for forecasting time series data, autoregressive integrated moving average method (ARIMA) and exponential smoothing method are used to forecast the number of deaths due to road accidents in India from the year 2022-- 2031. The results based on the two methods are compared and it is found that they are in sync with each other and pre-existing literature. Furthermore, this is a unique attempt to use two time series analysis techniques on the same data and carry out a comparative analysis. The data was collected from the annual report of Ministry of Road Transport and Highways, India (2020) and Accidental Deaths & Suicides in India (ADSI) Report of National Crime Record Bureau (2021). After examining all the probable models, it is observed that ARIMA (2, 2, 2) model and exponential smoothing (M, A, N) model are suitable for the given data. Amongst the two, ARIMA (2, 2, 2) model has a lower AIC and BIC value. Thus, this comes out to be the best model as per our model selection criterion. Further, the study also reveals an upward trend of number of road accidental deaths for the upcoming 10 years in India. [ABSTRACT FROM AUTHOR]

This paper compares four prediction methods, namely random forest regressor (RFR), SARIMAX, Holt-Winters (H-W), and the support vector regression (SVR), to forecast the total CO2 emission from the paddy crop in India. The major objective of this study is to compare these four models and suggest an effective model for the prediction of total CO2 emission. Data from 1961 to 2018 has been categorised into two parts: training and test data. The study forecasts total CO2 emission from paddy crops in India from 2019 to 2025. A comparison of mean absolute percentage error (MAPE) and the mean square error (MSE) highlights the differences in accuracy among the four models. The mean absolute percentage eror (MAPE) and the mean square error (MSE) for the four methods are RFR (MAPE: 5.67; MSE: 549,900.02), SARIMAX (MAPE: 1.67; MSE:70,422.35), H-W (MAPE:0.75; MSE:16,648.58), and SVR (MAPE: 0.91; MSE: 17,832.4). The values of MAPE and MSE with the Holt-Winters (H-W) and the support vector regression (SVR) are relatively low as compared to SARIMAX and RFR. Based on these results, it can be inferred that H-W and SVR were found suitable models to forecast the total CO2 emission from paddy crops. Holt-Winters model predicted 14,364.97 for the year 2025, and SVR predicted 13,696.67 for the year 2025. The decision-maker can use these predictions to build a suitable policy for the future. This approach can be contrasted with other forecasting methods, such as the neural network, and train the model to achieve better forecast accuracy. [ABSTRACT FROM AUTHOR]

Covid 19 is a new face of death and suffering in the world nowadays. When it started in the year 2019, it came with a lot of risks and uncertainties. World Health Organization (WHO) had no clue about the treatment and measures for this disease and so people got puzzled and started locking down themselves to avoid getting infected. Interstate and inter-country travelling got seized and the worst affected sector in this pandemic became the tourism industry. In this paper, the stock prices six multi-national tourism companies that operate in India, have been forecasted using some online learning algorithm Long Short Term Memory (LSTM), Recurrent Neural Network (RNN), Convolutional Neural Network (CNN), Least absolute Shrinkage and Selection Operator (LASSO), Gated Recurrent Unit (GRU) and Random Forest (RF). As we know that predicting stock prices is difficult due to its randomness, it requires extensive study of the stock market and intervention of statistical and machine learning models. This research attempts to examine whether the forecasting before the pandemic is better than during the pandemic for each of the six leading multinational tourism companies. [ABSTRACT FROM AUTHOR]

Loosening of rockmass during its excavation in an infrastructure project is carried by rock blasting. The blast-induced ground vibrations pose a major challenge to the blasting engineers, whose main objective is to control their potential to cause any damage to the buildings in the vicinity. The research reported in this paper explains how the error in the prediction of the Peak Particle Velocity (PPV) by the United States Bureau of Mines (USBM)-based approach can be minimised using machine learning techniques. The complex correlation between the blast parameter and the PPV value has been modelled using the least square boosted decision tree approach after the selection of the best suitable feature has been selected based on the correlation matrix. The proposed model automatically maps the input blast feature (SD) with the target PPV values by aggregating the decision of various weak learners. The generalization of the proposed model has been validated through a 5-fold cross-validation approach using a dataset comprising of two hundred blast records generated by monitoring the blasts at International airport site, Navi Mumbai, India. The assessment of the prognostic ability of the proposed model demonstrates that it has outperformed the USBM-based approach for PPV prediction. The results establish that the predictions by the pro- posed model are closer to the measured values than the other regression models. [ABSTRACT FROM AUTHOR]

In recent years, the penetration of solar power at residential and utility levels has progressed exponentially. However, due to its stochastic nature, the prediction of solar global horizontal irradiance (GHI) with higher accuracy is a challenging task; but, vital for grid management: planning, scheduling & balancing. Therefore, this paper proposes an ensemble model using the extended scope of wavelet transform (WT) and bidirectional long short term memory (BiLSTM) deep learning network to forecast 24-h ahead solar GHI. The WT decomposes the input time series data into different finite intrinsic model functions (IMF) to extract the statistical features of input time series. Further, the study reduces the number of IMF series by combining the wavelet decomposed components (D1-D6) series on the basis of comprehensive experimental analysis with an aim to improve the forecasting accuracy. Next, the trained standalone BiLSTM networks are allocated to each IMF sub-series to execute the forecasting. Finally, the forecasted values of each sub-series from BiLSTM networks are reconstructed to deliver the final solar GHI forecast. The study performed monthly solar GHI forecasting for one year dataset using one month moving window mechanism for the location of Ahmedabad, Gujarat, India. For the performance comparison, the naïve predictor as a benchmark model, standalone long short term memory (LSTM), gated recurrent unit (GRU), BiLSTM and two other wavelet-based BiLSTM models are also simulated. From the results, it is observed that the proposed model outperforms other models in terms of root mean square error (RMSE) & mean absolute percentage error (MAPE), coefficient of determination (R2) and forecast skill (FS). The proposed model reduces the monthly average RMSE by range from 26.04–58.89%, 5.17–31.35%, 23.26–56.06% & 21.08–57% in comparison with benchmark, standalone BiLSTM, GRU & LSTM networks respectively. On the other hand, the monthly average MAPE is reduced by range from 9 to 51.18%, 12.59–28.14%, 30.43–59.19% & 26.54–58.92% in comparison to benchmark, standalone BiLSTM, GRU & LSTM respectively. Further, the proposed model obtained the value of R2 equal to 0.94 and forecast skill (%) of 47% with reference to the benchmark model. [ABSTRACT FROM AUTHOR]

The changing population age structure has a significant influence on the economy, society, and numerous other aspects of a country. This paper has innovatively applied the method of compositional data forecasting for the prediction of population age changes of the young (aged 0–14), the middle-aged (aged 15–64), and the elderly (aged older than 65) in China, India, and Vietnam by 2030 based on data from 1960 to 2016. To select the best-suited forecasting model, an array of data transformation approaches and forecasting models have been extensively employed, and a large number of comparisons have been made between the aforementioned methods. The best-suited model for each country is identified considering the root mean squared error and mean absolute percent error values from the compositional data. As noted in this study, first and foremost, it is predicted that by the year 2030, China will witness the disappearance of population dividend and get mired in an aging problem far more severe than that of India or Vietnam. Second, Vietnam’s trend of change in population age structure resembles that of China, but the country will sustain its good health as a whole. Finally, the working population of India demonstrates a strong rising trend, indicating that the age structure of the Indian population still remains relatively “young”. Meanwhile, the continuous rise in the proportion of elderly population and the gradual leveling off growth of the young population have nevertheless become serious problems in the world. The present paper attempts to offer crucial insights into the Asian population size, labor market and urbanization, and, moreover, provides suggestions for a sustainable global demographic development. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic continues to have major impact to health and medical infrastructure, economy, and agriculture. Prominent computational and mathematical models have been unreliable due to the complexity of the spread of infections. Moreover, lack of data collection and reporting makes modelling attempts difficult and unreliable. Hence, we need to re-look at the situation with reliable data sources and innovative forecasting models. Deep learning models such as recurrent neural networks are well suited for modelling spatiotemporal sequences. In this paper, we apply recurrent neural networks such as long short term memory (LSTM), bidirectional LSTM, and encoder-decoder LSTM models for multi-step (short-term) COVID-19 infection forecasting. We select Indian states with COVID-19 hotpots and capture the first (2020) and second (2021) wave of infections and provide two months ahead forecast. Our model predicts that the likelihood of another wave of infections in October and November 2021 is low; however, the authorities need to be vigilant given emerging variants of the virus. The accuracy of the predictions motivate the application of the method in other countries and regions. Nevertheless, the challenges in modelling remain due to the reliability of data and difficulties in capturing factors such as population density, logistics, and social aspects such as culture and lifestyle. [ABSTRACT FROM AUTHOR]

This study proposes a new high-frequency indicator to measure economic policy uncertainty in the context of India, a large emerging market economy. Based on internet search intensity data, the proposed index tends to peak around domestic and global events associated with uncertainty that may prompt economic agents to alter their decisions to spend, save, invest and hire. Using an external instrument with structural vector autoregression (SVAR-IV) framework, we provide fresh evidence on the causal impact of uncertainty on the Indian macroeconomy. We show that surprise increases in uncertainty lead to a fall in output growth and an increase in inflation. This effect is found to be mainly driven by a fall in private investments vis-à-vis consumption indicating a dominant supply-side impact of uncertainty. Lastly, taking the case of output growth, we show that adding our uncertainty index to standard forecasting models leads to better forecasting accuracy compared to other alternate indicators of macroeconomic 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]

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Crop forecasting is a formidable challenge for every nation. The Government of India has developed a number of forecasting systems. The national and state governments need such pre-harvest forecasts for various policy decisions on storage, distribution, pricing, marketing, import-export and many more. In this paper, univariate forecasting models such as random walk, random walk with drift, moving average, simple exponential smoothing and Autoregressive Integrated Moving Average (ARIMA) models are considered and analyzed for their efficiency for forecasting vegetable production in the Haryana state. The State annual data on vegetable production were divided into the training data set from 1966-67 to 2013-14 and the test data set from 2014-15 to 2018-19. Suitable models were selected on the basis of error analysis on the training data and a percent error deviation test on the test data. Model diagnostic checking was carried out on ACF and PACF in residual terms through runs above and below the median, runs up and down and Ljung-Box tests. It is inferred that ARIMA (2,1,1) was found to be optimal and that the forecast values for the years 2019-20 to 2023-24 were estimated on the basis of this model, which were 7.82,8.23,8.72,9.2 and 9.72 million tonnes for the year 2019-20 to 2023-24, respectively. The significance of the mode is that we can forecast the values using this best fit model and forecast values are very important for the policymakers and other government agencies for proper policy decision regarding food security. [ABSTRACT FROM AUTHOR]

Background: The world has historically faced and recovered from many pandemics. The most recent global pandemic that the whole world is facing is Novel Coronavirus - Covid-19. The objective of current study is to compare and forecast COVID-19 trends for Pakistan and India. Methods: The data set for this research is obtained from the World Health Organization (WHO) online repository (https://covid19.who.int/). The time period we have considered since the first corona related case and death were observed in both countries. This research paper analyzes corona related cases and deaths in Pakistan and India till 28th February 2021, a total of 578,797 cases in Pakistan and 11,096,731 cases in India has been confirmed including 128,37 and 1,570,51 deaths respectively. The Auto-Regressive Integrated Moving Average (ARIMA) model is used to forecast the variables cumulative cases and deaths. It is simple to use and more predictive than any other regression model. Results: Based on the current trend, the forecast graph reveals that the number of cumulative corona cases could reach 999,767 in Pakistan and 16,481,122 in India up to 31st December 2021. Conclusion: This research found that corona related cumulative cases and deaths are on the rise in both countries. The pandemic situation in India is worse than in Pakistan nevertheless both countries are at high risk. There is a sudden increasing pattern in the number of corona related cases in both countries. Both governments must impose effective policies to control this pandemic. [ABSTRACT FROM AUTHOR]