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Motivated by the growing significance of solar energy, the paper presents the findings of the maiden study on the long-term seasonal performance assessment of three PV module technologies—monocrystalline (m-Si), polycrystalline (p-Si), and amorphous silicon (a-Si) housed in a 1 MW solar plant installed at a local steppe climate in Gandhinagar, Gujarat, India using the key performance indicators (KPIs), daily power generation, final yield (Y f), reference yield (Y r), total energy loss (T EL) and performance ratio (PR). Notably, the am-Si PV modules were found to be the best performing technology, with an average PR of 71.26% and the lowest average total energy loss of 631 h during the monitored period. Moreover, PV systems have become more popular as a source of green, clean energy; yet, they have low base-load energy sustainability. Explainable AI (XAI) aids in understanding predictions made by the machine learning models. The importance of XAI lies in the comprehension and trust which is embedded into the results of machine learning algorithms. Therefore, the present work builds a comprehensive XAI model for predicting KPIs and assesses the performance of several learning algorithms using R-value, MAE, the number of iterations, and execution time. The Levenberg-Marquardt algorithm predicts the KPIs for p-Si, am-Si, and m-Si with prediction accuracies of 98.63%, 98.58%, and 90.09%, respectively. In summary, this study not only advances our understanding of PV module performance in a steppe climate context but also contributes to the broader field by integrating an XAI model for transparent and reliable predictions of the KPIs. [Display omitted] • Comparison of m-Si, p-Si, and a-Si PV modules in a 1 MW solar plant, Gandhinagar, India, showed long-term performance. • a-Si PV modules outperformed with a 71.26% PR and the lowest 631 hours energy loss, surpassing m-Si and p-Si technologies. • Study underscores XAI's role in ML predictions, developing a robust model to predict KPIs and evaluate learning algorithms. • Levenberg-Marquardt showed high accuracy: 98.63% for p-Si, 98.58% for a-Si, and 90.09% for m-Si PV modules. [ABSTRACT FROM AUTHOR]

Anemia is one of the most pressing public health issues in the world with iron deficiency a major public health issue worldwide. The highest prevalence of anemia is in developing countries. The complete blood count is a blood test used to diagnose the prevalence of anemia. While earlier studies have framed the problem of diagnosis as a binary classification problem, this paper frames it as a multi class (three classes) classification problem with mild, moderate and severe classes. The three classes for the anemia classification (mild, moderate, severe) are so chosen as the world health organization (WHO) guidelines formalize this categorization based on the Haemoglobin (HGB) values of the chosen sample of patients in the Complete Blood Count (CBC) patient data set. Complete blood count test data was collected in an outpatient clinical setting in India. We used Feature selection with Majority voting to identify the key attributes in the input patient data set. In addition, since the original data set was imbalanced we used Synthetic Minority Oversampling Technique (SMOTE) to balance the data set. Four data sets including the original data set were used to perform the data experiments. Six standard machine learning algorithms were utilised to test our four data sets, performing multi class classification. Benchmarking these algorithms was performed and tabulated using both10 fold cross validation and hold out methods. The experimental results indicated that multilayer perceptron network was predominantly giving good recall values across mild and moderate class which are early and middle stages of the disease. With a good prediction model at early stages, medical intervention can provide preventive measure from further deterioration into severe stage or recommend the use of supplements to overcome this problem. [ABSTRACT FROM AUTHOR]

Tyre condition monitoring system (TCMS) is an emerging electronic safety system present in most of the new generation vehicles. It is intended to warn the driver when wheels are unbalanced or inflation pressure in a tyre goes below a certain recommended value. In India only limited vehicles having this critical safety system installed because it is highly expensive. The existing TCMS monitor only the tyre pressure using direct or indirect methods. In recent days the indirect TCMS using vibration signals and machine learning techniques are gaining momentum. This paper reports the effect of wheel balancing on a proposed affordable indirect TCMS. The present study goes one step forward to include the wheel balancing in the TCMS, thus increasing the scope of TCMS. The study was carried out using vibration signal data acquired from a low cost accelerometer placed on the wheel hub. The features required were taken out from the acquired data with the help of statistical feature extraction techniques. Features selection and feature classification were processed with J48 decision tree algorithm. The effect of wheel balancing in classification accuracy is clearly explained. [ABSTRACT FROM AUTHOR]

Air is one of the most fundamental constituents for the sustenance of life on earth. The meteorological, traffic factors, consumption of nonrenewable energy sources, and industrial parameters are steadily increasing air pollution. These factors affect the welfare and prosperity of life on earth; therefore, the nature of air quality in our environment needs to be monitored continuously. The Air Quality Index (AQI), which indicates air quality, is influenced by several individual factors such as the accumulation of NO2, CO, O3, PM2.5, SO2, and PM10. This research paper aims to predict and forecast the AQI with Machine Learning (ML) techniques, namely linear regression and time series analysis. Primarily, Multilinear Regression (MLR) model, supervised machine learning, is developed to predict AQI. NO2, Ozone(O2), PM 2.5, and SO2 sensor output collected from Central Pollution Control Board (CPCB), Chennai region, India, fed as input features and optimized AQI calculated from sensor's output set as a target to train the regression model. The obtained model parameters are validated with new and unseen sensor output. The Key Performance Indices (KPI) like coefficient of determination, root mean square error, and mean absolute error were calculated to validate the model accuracy. The K-cross-fold validation for testing data of MLR was obtained as around 92%. Secondly, the Auto-Regressive Integrated Moving Average (ARIMA) time series model is applied to forecast the AQI. The obtained model parameters were validated with unseen data with a timestamp. The forecasted AQI value of the next 15 days lies in a 95 % confidence interval zone. The model accuracy of test data was obtained as more than 80%. [ABSTRACT FROM AUTHOR]

The change in price of a group of goods and services is reflected in terms of consumer price index (CPI), making it one of the most important economic indicators. This is also the mostly used measure of inflation. Forecasted CPI values help the Government to take corrective measures to control the economic conditions of the country. This paper implements and examines two machine learning models such as artificial neural network (ANN) and ANN model optimized with particle swarm optimization (PSO) known as ANN‐PSO to assess the accuracy in predictability of CPI. The data set for four groups such as food and beverages, housing, clothing, and footwear used for the calculation of all India CPI has been taken from the official website of the Government of India. The mean absolute percentage error (MAPE) has been used as the validator for model accuracy. The MAPE calculated for all experiments are less than 10% which indicates that the ANN‐PSO models used are highly accurate for prediction of CPI of India. [ABSTRACT FROM AUTHOR]

In recent times, active research is going on for bridging the gap between computer vision and natural language. In this paper, we attempt to address the problem of Hindi video captioning. In a linguistically diverse country like India, it is important to provide a means which can help in understanding the visual entities in native languages. In this work, we employ a hybrid attention mechanism by extending the soft temporal attention mechanism with a semantic attention to make the system able to decide when to focus on visual context vector and semantic input. The visual context vector of the input video is extracted using 3D convolutional neural network (3D CNN) and a Long Short-Term Memory (LSTM) recurrent network with attention module is used for decoding the encoded context vector. We experimented on a dataset built in-house for Hindi video captioning by translating M S R - V T T dataset followed by post-editing. Our system achieves 0.369 CIDEr score and 0.393 METEOR score and outperformed other baseline models including RMN (Reasoning Module Networks)-based model. [ABSTRACT FROM AUTHOR]

Electrical energy is one of the key components for the development and sustainability of any nation. India is a developing country and blessed with a huge amount of renewable energy resources still there are various remote areas where the grid supply is rarely available. As electrical energy is the basic requirement, therefore it must be taken up on priority to exploit the available renewable energy resources integrated with storage devices like fuel cells and batteries for power generation and help the planners in providing the energy-efficient and alternative solution. This solution will not only meet electricity demand but also helps reduce greenhouse gas emissions as a result the efficient, sustainable and eco-friendly solution can be achieved which would contribute a lot to the smart grid environment. In this paper, a modified grey wolf optimizer approach is utilized to develop a hybrid microgrid based on available renewable energy resources considering modern power grid interactions. The proposed approach would be able to provide a robust and efficient microgrid that utilizes solar photovoltaic technology and wind energy conversion system. This approach integrates renewable resources with the meta-heuristic optimization algorithm for optimal dispatch of energy in grid-connected hybrid microgrid system. The proposed approach is mainly aimed to provide the optimal sizing of renewable energy-based microgrids based on the load profile according to time of use. To validate the proposed approach, a comparative study is also conducted through a case study and shows a significant savings of 30.88% and 49.99% of the rolling cost in comparison with fuzzy logic and mixed integer linear programming-based energy management system respectively. • The article proposes a hybrid MG with renewable energy sources and energy storage systems (battery and fuel cell). • Develop a novel energy management system using modified grey wolf optimizer (MGWO) with multi objective functions. • Analyzes the load demand and the cost of electricity from the power grid for effective designing of MG at test locations. • A significant savings of 30.88% and 49.99% of the rolling cost in comparison with FL and MILP based EMS respectively. [ABSTRACT FROM AUTHOR]

Thyroid hormones are essential for all the metabolic and reproductive activities with significance to growth, and neuron development in the human body. The thyroid hormone dysfunction has many ill consequences, affecting the human population; thereby being a global epidemic. It is noticed that every one in 10 persons suffer from different thyroid disorders in India. In recent years, many researchers have implemented various disease predictive models based on Information and Communications Technology (ICT). Increasing the accuracy of disease classification is a critical and challenging task. To increase the accuracy of classification, in this paper, we propose a hybrid optimization algorithm‐based feature selection design for thyroid disease classifier with rough type‐2 fuzzy support vector machine. This work uses the hybrid optimization algorithm, which combines the firefly algorithm (FA) and butterfly optimization algorithm (BOA) to select the top‐n features. The proposed hybrid firefly butterfly optimization‐rough type‐2 fuzzy support vector machine (HFBO‐RT2FSVM) is evaluated with several key metrics such as specificity, accuracy, and sensitivity. We compare our approach with well‐known benchmark methods such as improved grey wolf optimization linear support vector machine (IGWO Linear SVM) and mixed‐kernel support vector machine (MKSVM) methods. From the experimental evaluations, we justify that our technique improves the accuracy by large thereby precise in identifying the thyroid disease. HFBO‐RT2FSVM model attained an accuracy of 99.28%, having specificity and sensitivity of 98 and 99.2%, respectively. [ABSTRACT FROM AUTHOR]

Crop yield prediction is an important aspect of agriculture. The timely and accurate crop yield predictions can be of great help for policy makers and farmers in planning and decision making. Generally, statistical models are employed to predict the crop yield which is time consuming and tedious. Emerging trends of deep learning and machine learning has come up as a major breakthrough in the arena. Deep learning models have the inherent ability to perform feature extraction in large dataset thus more suitable for predictions. In this paper, a deep learning-based Recurrent Neural Network (RNN) model is employed to predict wheat crop yield of northern region of India. The present study also employed LSTM to unravel the vanishing gradient problem inherent in RNN model. Experiments were conducted using 43 years benchmark dataset and proposed model results were compared with three machine learning models. Evidently, the results obtained from RNN-LSTM model(RMSE: 147.12,MAE: 60.50), Artificial Neural Network(RMSE: 732.14,MAE: 623.13), Random Forest (RMSE: 540.88, MAE: 449.36) and Multivariate Linear Regression (RMSE: 915.64,MAE: 796.07), proved the efficacy of model. Also, predicted crop yield values were found to be more close to true values for RNN-LSTM model proving efficiency of the proposed work. [ABSTRACT FROM AUTHOR]

The outbreak of Coronavirus 2019 (COVID‐19) has impacted everyday lives globally. The number of positive cases is growing and India is now one of the most affected countries. This paper builds predictive models that can predict the number of positive cases with higher accuracy. Regression‐based, Decision tree‐based, and Random forest‐based models have been built on the data from China and are validated on India's sample. The model is found to be effective and will be able to predict the positive number of cases in the future with minimal error. The developed machine learning model can work in real‐time and can effectively predict the number of positive cases. Key measures and suggestions have been put forward considering the effect of lockdown. [ABSTRACT FROM AUTHOR]

This study presents the uprising of leaf chlorophyll estimation from traditional mechanical method to machine learning-based method. Earlier chlorophyll estimation techniques such as Spectrophotometer and Soil Plant Analysis Development (SPAD) meter demand cost, time, labour, skill, and expertise. A small-scale tea farmer may not afford these devices. The present study reports a low-cost digital method to predict the tea leaf chlorophyll using 1-D Convolutional Neural Network (1-D CNN). After capturing the tea leaf images using a digital camera in a natural light condition, a total of 12 different colour features were extracted from tea leaf images. A SPAD was used to estimate the original chlorophyll value of the tea leaves. The paper shows the correlation of original tea leaf chlorophyll with the extracted colour features of the tea leaf images. Apart from 1-D CNN, the Multiple Linear Regression (MLR) and K-Nearest Neighbor (KNN) were also applied to predict the tea leaf chlorophyll and compared their results with the 1-D CNN. The 1-D CNN model outperformed with an accuracy of 81.1%, Mean Absolute Error (MAE) of 3.01, and Root Mean Square Error (RMSE) of 4.18. The investigation system is very simple and cost-effective. It can be used in tea farming as a digital SPAD for faster and accurate leaf chlorophyll estimation in an easy way. [ABSTRACT FROM AUTHOR]

• Landscape ecological vulnerability was assessed using bagging ANN-MLP and RF models. • B-MLP model was found best fit model for LEV assessment based on ROC curve, recall, precision, F1-score and accuracy. • Largest area under very high LEV was recorded in the southern part of the plains. • Rainfall, soil, vegetation and LULC were found influencing factors for LEV. • Plantation, embankments and geotextile technology are suggested to check erosion. Riverbank erosion is one of the most catastrophic hazards that renders floodplains vulnerable across the world vulnerable. It creates a significant negative impact on the environment and socio-economic life. This paper attempts to assess the landscape ecological vulnerability (LEV) to riverbank erosion in the Middle Brahmaputra floodplains of Assam, India by employing two machine learning models, namely artificial neural network-multilayer perceptron (ANN-MLP) and random forest (RF). Bagging ensembles were created for both ANN-MLP and RF (B-MLP and B-RF) to generate LEV zones. A total of eleven site-specific parameters were considered for the study. The receiver operating characteristic (ROC) based area under curve (AUC), accuracy, precision, recall and F1-score were used to validate the models and judge the models' performance. A sensitivity analysis was performed to deduce the most influential LEV parameters. The results revealed that B-MLP was the better-performing model compared to B-RF based on all five validation metrics. The largest area was found under very high vulnerability zone followed by very low, low, high and moderate vulnerability zones, based on both ensemble models. The western part of the floodplains was found to be more vulnerable than the eastern part. Moreover, the southern bank faced more vulnerability in comparison to the northern bank. The factors namely rainfall, soil type, vegetation and land /use land cover (LULC) influenced bank erosion vulnerability. This research provides evidence that the study area is under severe threat to riverbank erosion and urgently requires the implementation of effective mitigation measures. The study might benefit policymakers and local stakeholders to protect the floodplains from bank erosion and reduce vulnerability. [ABSTRACT FROM AUTHOR]

How far the de-linking and morphological and hydrological degradation of tie channels connecting river to the wetland are caused for the areal and hydrological transformation of a wetland was not received enough attention in previous literature. This paper tried to explain this about the confluence reach of the Dwarka and Brahmni rivers in Moribund deltaic India. In order to explain the linkages 16 tie channels' change depicting variables and seven wetland area and hydrological transformation (consistency, hydro-period, water depth, water richness etc.) related variables were taken. Ordinary least square (OLS) regression was applied for explain the linkage. Machine learning approaches were applied for water richness mapping. Tie channel evolution was digitized from the Survey of India (SOI) toposheet (1974) and historical Google Earth images. The result revealed that, the tie channel witnessed morphological and hydrological degradations like channel constriction, channel clogging, flow lowering etc. and these were further identified as some vectors of areal shrinkage and growing hydrological insecurity in the linked wetlands. The total wetland area declined from 44.89 km2 to 16.86 km2 from 1991 to 2021. The rate of areal loss, shallowing WD, growing inconsistency of water presence, narrowing HP, and weakening WR was found high in recent times due to de-linking and degradations of tie channels. This approach of explanation of hydrological insecurity of wetland in relation to tie channel degradations, de-linking and findings are quite unique. So, re-linking and morphological correction of the tie channels would be a good policy for wetland restoration. • The floodplain wetland area was reduced by 37.55% between 1991 and 2021. • Tie channels were either lost or degraded losing sufficient water-carrying capacity. • Consistency, hydro-period, wetland depth declined in relation to tie channel loss. • De-linking of tie channel deteriorated water richness zone in wetlands. • Re-linking of tie channel could improve the hydrological condition of the wetland. [ABSTRACT FROM AUTHOR]

There have been research attempts to automate the process of lithology identification from the well-logs using machine learning algorithms. However, a single algorithm cannot be applied on a universal scale due to the nonstationary nature of geological data sets. The notion of changes in the well-log signal must be considered to resolve the issue. This paper presents a multistage method using multivariate change detection and statistical machine learning to (a) identify the significant changes in the well-logs, (b) map multivariate change in log signal with the lithofacies classes, (c) enhance the data set by removing the highly populated class, (d) using statistical machine learning tools, i.e., Support Vector Machine (SVM) to classify target classes. We apply the algorithm to the data collected from a mostly shale-contained subsurface environment in the Krishna-Godavari (KG) basin, India. The primary objective of the research is to locate thin layers of hydrocarbon-contained sand in the subsurface while dealing with challenges such as intra-well and inter-well heterogeneity and a severe class imbalance. The algorithm satisfactorily performs in lithofacies classification with significantly better performance than the standard machine learning algorithm. • Lithofacies identification using a multi-stage change detection approach • Lithofacies identification in a non-stationary subsurface • Lithofacies classification in an unbalanced condition [ABSTRACT FROM AUTHOR]

Since the first fully homomorphic encryption scheme was published in 2009, many papers have been published on fully homomorphic encryption and its applications. Machine learning is one of the most interesting applications and has drawn a lot of attention from researchers. To better represent and understand the field of Homomorphic Encryption in Machine Learning (HEML), this paper utilizes automated citation and topic analysis to characterize the HEML research literature over the years and provide the bibliometrics assessments for this burgeoning field. This is conducted by using a bibliometric statistical analysis approach. We make use of web-based literature databases and automated tools to present the development of HEML. This allows us to target several popular topics for in-depth discussion. To achieve these goals, we have chosen the well-established Scopus literature database and analyzed them through keyword counts and Scopus relevance searches. The results show a relative increase in the number of papers published each year that involve both homomorphic cryptography and machine learning. Using text mining of articles titles, we have found that cloud computing is a popular topic in this field, which also includes neural networks, big data, and the Internet of Things. The analysis results show that China, the US, and India have generated almost half of all the research contributions in HEML. The citation statistics, keyword statistics, and topic analyses give us a quick overview of the development of the field, which can be of great help to new researchers. It is also possible to apply our methodology to other research areas, and we see great value in this approach. [ABSTRACT FROM AUTHOR]

Large-scale tree planting has been advocated for decades in many countries as a cost-effective strategy to mitigate carbon, rejuvenate degraded landscapes, and support local livelihoods. Recent studies, however, suggest limited ecological and livelihood impacts of tree planting programs, indicating possible limits to tree planting as a natural climate solution. In this paper, we explore these carrying capacity-like limits by evaluating the site suitability of forestry landscapes of the northern Indian state of Himachal Pradesh using theory/expert-based rules and machine learning algorithms. We find that the state can only achieve a maximum of 31.54% forest cover due to socio-ecological and biophysical constraints, much less than India's goal of bringing 66% of the total geographical area of Himalayan states into forest cover. The low availability of suitable lands for growing trees, together with poor plantation site selection by forest range officers limit the use of tree planting as a sole climate mitigation strategy. To approach theoretical planting limits, we propose an ePSA (e-Plantation Site Assistant) recommendation system based on our site-suitability results to assist forest rangers in selecting suitable sites for planting trees. An initial deployment of the recommender system suggests its potential utility in shaping the long-term success of tree plantations as an effective carbon strategy in northern India and beyond. Overall, we argue that there is a need to realign over-ambitious national and international tree planting targets with actual limits from site characteristics to avoid massive wastage of funds and to obtain feasible carbon mitigation outcomes. [ABSTRACT FROM AUTHOR]

• Feature engineering based framework for location-sensitive disaster event detection. • Augmentation of context-free and context-based features with place of occurrence. • Evaluation with unsupervised machine learning algorithm with 09 performance metrics. • AHP-TOPSIS based selection of an efficient machine learning algorithm and feature set. • Location-augmented context-based features outperformed traditional textual features. Disaster event detection aims to identify events like terrorist attacks, fire incidents, stampede incidents, building collapse, etc., reported in the online news articles or social media. Place of occurrence of disaster event is a significant feature associated with events for location-sensitive disaster event detection. Efficient feature selection and their augmentation with location information can contribute towards the evolution of traditional approaches and their adoption for location-sensitive disaster event detection leading to improvement in the overall process as a whole. Since the evaluation of event detection techniques deliberates various intrinsic and extrinsic performance metrics, the decision-making for the selection of feature sets is treated as a Multiple-Criteria Decision Making (MCDM) problem. This paper proposes a framework, GeoClust , that is based on feature engineering of traditional textual features in order to enhance their capability for improved location-sensitive disaster event detection. The framework augments context-free and context-based textual feature sets with feature sets of place of occurrence of the events and evaluates their performance using unsupervised machine learning algorithms for various performance metrics. Finally, the best feature set is selected using AHP-TOPSIS technique of MCDM in order to tune the system for automatic and efficient location-sensitive disaster event detection in real-time. Extensive set of experiments have been performed in order to evaluate the framework on a dataset of online news articles reporting disaster events about terrorist attacks, fire incidents, stampede incidents, building collapse and maoist attacks happened at different locations in India. The results show that the location-augmented feature sets significantly improve performance of location-sensitive disaster event detection as compared with traditional feature sets. The results also demonstrate that the context-based feature sets with location-augmentation are ranked higher than the context-free feature sets in MCDM analysis. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has dramatically altered global energy consumption, particularly affecting investment in renewable energy projects. In India, strict shelter-in-place orders enforced during March 2020 have since led to a considerable change in public and private sector investments in planned renewable energy installations. In this paper, we attempt to highlight trends in energy consumption and installed renewable energy capacity noted in India during a period concurrent with the shelter-in-place orders. We discuss recent policy measures and additions to installed renewable energy capacity, and propose key policy recommendations that may help the sector adopt a growth trajectory similar to one noted pre-pandemic. This paper is organized into four main parts. In the first section, we draw focus to India's renewable energy policies and pay special emphasis on recent interventions and campaigns targeted towards achieving high growth rates in the sector. We briefly discuss the need for effective public-private partnerships in order to meet these targets. In the second part, we quantitatively characterise the growth of renewables in India. We present an overview of several mechanisms and missions the government has launched in line with their policy to mitigate the environmental impact of India's energy mix. In the third part, we analyse the decrease in electricity demand in India from 24 March to 30 June 2020, a period concurrent with shelter-at-home orders issued by the Government. We also characterise changes in installed renewable energy capacity between March to December 2016–2020 to provide causal evidence of the effect of the pandemic on the growth of renewables. In this section, we also compile and analyse data on state-wise stressed assets across renewable energy generators in the country. Lastly, in the fourth and final portion of this paper, we highlight policy recommendations that may help the sector overcome logistical and financial bottlenecks in the short-term. We do this with the hope of outlining key measures that decision makers may employ to achieve pre-COVID sectoral growth in the long term. Our recommendations cover three different policy instruments: investment subsidies, operational subsidies, and recommendations for DISCOMs. • The increase in Indian renewable energy capacity installations between February to May 2020 hasseen an increment of 0.75%. • Daily electricity demand has decreased by 18.8% after the announcement of the shelter at home orders. • Immediate effects of the pandemic on the Indian renewable energy sector have been quantified. • A 3 tier system may be adopted to regulate incentives among renewable energy power producers. • Key policy recommendations to improve the fitness of the renewable energy sector have been discussed. [ABSTRACT FROM AUTHOR]

• Shot-term reservoir inflow prediction accounting for hydrological and climate indices. • ML-based Ensemble methods to quantify forecasting uncertainty. • The Ensemble model performed better than the standalone ML models. • Two reservoirs of India and USA were chosen to test the robustness of ML models. Short and long range reservoir inflow forecast is essential for efficient real time operational planning, scheduling of hydroelectric power system and management of water resources. Large-scale climate phenomenon indices have a strong influence on hydrological processes under complex weather conditions, and it should be considered to forecast reservoir inflow for efficient dam operation strategies. This study aims to explore the relevance of large-scale climate phenomenon indices in improving the reservoir inflow prediction at short-term time scales. This paper presents a simple and effective framework to combine various data-driven machine learning (ML) algorithms for short-range reservoir inflow forecasting. 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 (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 ensemble method to quantify forecasting uncertainty and to improve the model performance by combining the inflow results of the single ML model and the highest vote is chosen based on the weights assigned to the single ML model. The developed framework was examined in two distinct reservoirs located in India and California, USA. The ensemble model consistently outperformed the standalone RF, GBR, KNN, and LSTM in predicting high (flood control and monsoon seasons) and low flows (runoff and non-monsoon seasons) of both study reservoirs. The demonstrated short-term reservoir forecasting model allows reservoir operators to adapt and add regional hydrological and large-scale climate indices in real-time decision-making. The presented framework can be applied for any reservoir inflow forecasting. [ABSTRACT FROM AUTHOR]

Coal exploration in the Indian scenario is challenging due to plenty of carbon contents and dirt bands within a coal seam. Manual interpretation of geophysical logging data in such conditions is time-consuming and tedious due to the non-linear behaviors of well log signals. This paper intends to apply supervised machine learning techniques such as Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), Multi-Layer Perceptron (MLP), and Extreme Gradient Boosting technique (XGBoost) for meticulously interpreting such banded coal seams from geophysical logs. To investigate the efficacy of the above-mentioned five ML techniques in the Indian scenario, we have considered Gamma-ray, Density, and Resistivity logs from four boreholes drilled in Talcher coalfield, Eastern India. ML model training results indicate that all the prediction models have scored more than 88% accuracy scores in classifying carbonaceous and non-coal lithofacies. Receiver Operating Characteristics (ROC) curves of different ML models suggest that the obtained area under the curve (AUC) is positive and above the main diagonal for all lithotypes. Finalized ML models with appropriate hyper-parameters are also applied on the nearby drilled wells, and the outputs are validated against geological core data. It is found that all ML models have acquired an overall accuracy score greater than 80% in all three test wells, conveying that ML techniques are a potential solution for dealing with banded coal seam problems in the Indian scenario. • Lithology prediction from Well logs using Machine learning (ML) algorithms. • All ML models have secured accuracy scores more than 88% in the training phase. • Predicted lithology from ML algorithms is validated with geological core data. • ML models have obtained more than 80% accuracy scores in all three test wells. • ML techniques are the potential solutions for tackling banded coal seam problems. [ABSTRACT FROM AUTHOR]

The agricultural sector in India accounts for a significant part of the country's GDP and is the primary income source for many farmers in rural areas. While it creates employment opportunities and offers food security for the entire nation, the lack of infrastructure and resources might be limiting its potential to thrive further. One of the aspects addressed in this paper is low yield production. With the aid of a sensor-based irrigation model, data is collected and analyzed in the cloud to enable real-time monitoring. It is then integrated with an Android application for displaying results in an user-friendly interface. Through the application, farmers can control the farm manually, or with a timer in minutes. The Machine Learning model predicts the suitable crops, in accordance with varying weather parameters. The application has a classified portal for farmers and customers to buy/sell directly, eliminating any involvement of mediators. One of the novelties in this research includes monitoring/controlling farm equipment and predicting field crops from a locally installed LCD display and keypad present in farmer's respective homes. The proposed work aims to create an energy-efficient, user-friendly framework for the agricultural workforce, yielding better crop production, improving farmers' living standards, and contributing effectively to the nation's economic growth. The prototype shows a reduction of water usage in fields by more than 60%. In order to incorporate the model with the best behavior in Android Application, different Machine Learning algorithms have been studied, among which Random Forest has been selected with a test accuracy of 91.59%. [ABSTRACT FROM AUTHOR]

• Stuying DSM products for spatializing the STICS model (simulated yield and drainage) • Comparisons of STICS outputs using observed vs DSM estimated STICS soil parameters. • DSM products outperformed the soil map for providing the STICS soil parameters. • Few yearly and the all-year-aggregated simulations were impacted by DSM errors. • Coupling DSM with a crop model: an interesting solution for Digital Soil Assessment. Digital Soil Mapping (DSM) can be an alternative data source for spatializing crop models over large areas. The objective of the paper was to evaluate the impact of DSM products and their uncertainties on a crop model's outputs in an 80 km2 catchment in south India. We used a crop model called STICS and evaluated two essential soil functions: the biomass production (through simulated yield) and water regulation (via calculated drainage). The simulation was conducted at 217 sites using soil parameters obtained from a DSM approach using either Random Forest or Random Forest Kriging. We first analysed the individual STICS simulations, i.e., at two cropping seasons for 14 individual years, and then pooled the simulations across years, per site and crop season. The results show that i) DSM products outperformed a classical soil map in providing spatial estimates of STICS soil parameters, ii) although each soil parameters were estimated separately, the correlations between soil parameters were globally preserved, ii) Errors on STICS' yearly outputs induced by DSM estimations of soil parameters were globally low but were important for the few years with high impacts of soil variations, iii) The statistics of the STICS simulations across years were also affected by DSM errors with the same order of magnitude as the errors on soil inputs and iv) The impact of DSM errors was variable across the studied soil parameters. These results demonstrated that coupling DSM with a crop model could be a better alternative to the classical Digital Soil Assessment techniques. As such, it will deserve more work in the future. [ABSTRACT FROM AUTHOR]

Agriculture is one of the most important sectors in the Indian context. It is one of the highest employing sectors in the Indian scenario. Unlike other sectors agriculture is highly dependent on the quality and the quantity of both the external factors like rainfall, climate, pH of the soil, fertilizers and insecticides used, and internal factors like the quality of seeds. This paper predicts the production of crops as a function of rainfall for four Indian States. This knowledge can be implemented in generating a rough overview of how the production is based on rainfall and how much can a specific crop production for the amount of rainfall it receives. Two crops each belonging to four different states are chosen and the best regression model for the crop of the state is chosen. There is no research done solely on how rainfall affects crops of particular states. The proposed method of evaluation is better than other existing methods of evaluation as it evaluates all the regression techniques (Linear Regression, Polynomial Regression, Support Vector Regression, Decision Tree Regression, Random Forest, and XGBRegression) for two crops of four individual states. For balanced evaluation, two states of North India and two states of South India are selected. The regression techniques are evaluated based on their Mean Squared Error. • Crops of same species have different production rates within a same country. • Performance of each algorithm is different for crops within state. • Rainfall is the key determining factor for crop production in India. • Production of crop is affected by environment more than the genotype. • Predicting the production provides high impetus for developing economies. [ABSTRACT FROM AUTHOR]

Land Use Land Cover (LULC) change assessment and prediction are essential for optimised water resources planning and management. This paper attempts to intercompare the different LULC change modelling techniques (two-hybrid and two traditional models) to predict the future LULC change. These include Multi-Layer Perceptron Markov Chain (MLP_MC), Logistic Regression-Markov Model (LR-MC), and two hybrid models, namely, Multi-Layer Perceptron Markov Chain Cellular Automata (MLP_MC_CA), Logistic Regression Markov Model Cellular Automata (LR_MC_CA) models. These models were tested on Nagavali River Basin (NRB), a river basin in Southern India, which has seen significant land-use changes over the past two decades. Over the past two decades, the study region experienced dominant changes on the order of 17.42% and 15.22% decrease in scrubland and forest, respectively. At the same time, the agricultural land cover is increased by 35.28%. For the LULC prediction, the model was initially trained using the relevant driver variables and LULC maps of 2010 and 2015. The calibrated model was validated using the 2020 LULC map. The statistical results in terms of Kappa values and chi-square results reveal that the hybrid model MLP-MC-CA has a better agreement (Kappa coefficient 0.902) compared to the other models. Further, it is also observed that the CA-based models have a better ability to capture spatial connections. After combining the MLP_MC model with the Cellular Automata, the former model was improved by 10.8% in terms of the overall Kappa coefficient. The best model for LULC prediction over the next decade (LULC map of 2030) showed that the forest area would decrease by 9.02%, and the agricultural land would increase by 8.74%. Further, the results from the study indicate that the hybrid machine learning models provide a promising alternative for land-use change prediction. • Intercomparison of different Machine Learning Methods for Land Use Land Cover Prediction. • Hybrid model based on MLP_MC_CA outperforms other non-hybrid and traditional methods. • LULC 2030 for NRB showed that the forest area would decrease by 9.02%, and the agricultural land would increase by 8.74%. [ABSTRACT FROM AUTHOR]