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This paper explores the different insider employee-led cyber frauds (IECF) based on the recent large-scale fraud events of prominent Indian banking institutions. Examining the different types of fraud and appropriate control measures will protect the banking industry from fraudsters. In this study, we identify and classify Cyber Fraud (CF), map the severity of the fraud on a scale of priority, test the mitigation effectiveness, and propose optimal mitigation measures. The identification and classification of CF losses were based on a literature review and focus group discussions with risk and vigilance officers and cyber cell experts. The CF was analyzed using secondary data. We predicted and prioritized CF based on machine learning-derived Random Forest (RF). An efficient fraud mitigation model was developed based on an offender-victim-centric approach. Mitigation is advised both before and after fraud occurs. Through the findings of this research, banks and fraud investigators can prevent CF by detecting it quickly and controlling it on time. This study proposes a structured, sustainable CF mitigation plan that protects banks, employees, regulators, customers, and the economy, thus saving time, resources, and money. Further, these mitigation measures will improve the reputation of the Indian banking industry and ensure its survival. [ABSTRACT FROM AUTHOR]

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

Gully erosion is a severe environmental issue that poses threats to agriculture, human safety, habitats, infrastructure, and soil integrity. Selecting the right machine learning model is vital for accurate Gully Erosion Susceptibility Mapping (GESM) due to varying environmental hazard performance. This study employed a comparative analysis of two machine learning techniques, Random Forest (RF) and Extreme Gradient Boost (XGBoost), to develop a highly precise GESM for the Silabati watershed (India). The analysis incorporated 24 controlling factors and examined a dataset of 460 sample points, with equal representation of gullies and non-gullies. Variance Inflation Factors (VIF) and Information Gain Ratio (IGR) techniques were applied to assess multicollinearity test among the controlling factors. Lithology, elevation, distance from the road, LULC, geomorphology, rainfall, drainage density, and coarse fragments emerged as crucial factors in determining GESM. Statistical tests, including the Kappa index, Root Mean Square Error (RMSE), Accuracy (ACC), Mean Absolute Error (MAE), Coefficient of Determination (R2), and Receiver Operating Characteristic (ROC), were employed to evaluate the RF and XGB models on training and testing data. Both models demonstrated strong performance, with the XGBoost and RF models achieving ROC values of 84.1% and 83.2%, respectively. The applied quantile classification method resulted in the creation of five distinct GESMs, categorized as very high (VH), high (H), moderate (M), low (L), and very low (VL). We found that the very high GESM areas of the watershed were 4.10% in the XGBoost model and 4.61%. in the RF model. Out of the 26 sub-watersheds, the results have identified five sub-watersheds as highly prioritized for sustainable management. Therefore, the present study provides an accurate gully erosion identification using advanced models, offering valuable insights for policymakers to proper management implementation. [ABSTRACT FROM AUTHOR]

Snow cover information is essential for pursuing seasonal variation studies in Himalayan river basins. This study aims to investigate the seasonal variation of snow cover in the Sutlej river basin (Tibet to Bhakra dam in India) over three different seasons: Monsoon (June–September), winter (October-January), and summer (February-May) during the period 2010–2021. Landsat 7 and 8 Surface Reflectance (SR) data is used to develop 108 land use land cover (LULC) maps for 12 years, with three seasons per year and three machine-learning models. The study has conducted on the Google Earth Engine (GEE) platform, employing Random Forest (RF), Classification and Regression Trees (CART), and Support Vector Machine (SVM) models to classify the Landsat satellite data and assess the seasonal snow cover variation during the three seasons. The results show that among the three machine learning models, the RF model exhibits the highest average overall accuracy at 98.75%, followed by the CART model at 98.10%, and the SVM model with the lowest accuracy at 97.15%. In terms of snow cover area variability; there is a decline trend in summer snow cover and an increase in monsoon snow cover over the past three years (2019–2021). However, an increasing trend emerges when considering the decadal changes in all three seasons. In addition, the maximum percentages of snow cover area observed as 67.61%, 46.78%, and 30.58% in the summer period of 2013, the winter period of 2019, and the monsoon period of 2021, respectively. Similarly, the minimum percentage of snow cover is 23.22%, 11.36%, and 13.01%, observed in the summer period of 2014, the winter period of 2011, and the monsoon period of 2012, respectively. A comprehensive assessment procedure on temporal and seasonal snow cover variation in a large river basin have been presented in this work, which will help to plan and manage the sustainable water resources in study region. [ABSTRACT FROM AUTHOR]

The present study investigates the implementation of machine learning models on crop data to predict crop yield in Rajasthan state, India. The key objective of the study is to identify which machine learning model performs are better to provide the most accurate predictions. For this purpose, two machine learning models (decision tree and random forest regression) were implemented, and gradient boosting regression was used as an optimization algorithm. The result clarifies that using gradient boosting regression can reduce the yield prediction mean square error to 6%. Additionally, for the present data set, random forest regression performed better than other models. We reported the machine learning model's performance using Mean Squared Error, Mean Absolute Error and R-squared and identified that after the inclusion of gradient boosting regression, the accuracy increased to 92.77%. The MAE value decreased from 26.20 Mg/ha to 21.58 Mg/ha. The results indicate that machine learning models can improve the prediction of crop yield. [ABSTRACT FROM AUTHOR]

Monitoring of river water is necessary to reveal its quality and pollution level so that we can protect human health and the environment. The present study explored the water quality of the Narmada River in India. To evaluate the water quality of the Narmada River, water samples were collected from 13 sites during the pre- and post-monsoon seasons, and were analyzed for different physicochemical parameters. The results from the analysis were used for the development of the entropy-river water quality index (ERWQI). The ERWQI was used to estimate the Narmada river water quality for two different uses: drinking after disinfection (ERWQId) and bathing (ERWQIb). The machine-learning-based classification models, namely the Logistic regression (LR), Support Vector (SV), K-Nearest Neighbor (KNN), Random Forest (RF), and Gradient Boosting (GB) models were examined to predict and classify ERWQI. The precision, recall, F1 score, and confusion matrix were used to evaluate the performance of the model. The findings of this study identified the LR model as the most accurate classification model with the highest accuracy score for both the ERWQId and ERWQIb. Moreover, this study also revealed that the water quality of the Narmada River was unsuitable for drinking after disinfection and hence, before any further use it requires treatment through conventional or an advanced techniques. However, the ERWQIb of the Narmada River was categorized as excellent to fair. This study has broad implications for the classification of river water quality and can provide some very useful information to monitoring agencies and policymakers. [ABSTRACT FROM AUTHOR]

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

The popularity of cutting‐edge machine learning ensemble approaches has solved many climate change research and prediction issues. The six top‐performing GCMs obtained from Technique for Order Preference by Similarity to an Ideal Solution were ensembled using seven machine learning ensemble methods such as Random Forest Regressor (RFR), Support Vector Regressor (SVR), Linear Regression (LR), Adaptive Boosting Regressor (AdaBoost), Extreme Gradient Boosting Regressor (XGBR), Extra Tree Regressor (ETR), Multi‐Layer Perceptron neural network (MLP) and simple Arithmetic Mean (AM) over the diverse geo‐climatic basins. Precipitation is best simulated by EC‐Earth3 and BCC‐CSM2‐MR. Maximum temperature by MPI‐ESM1‐2‐HR, EC‐Earth3‐Veg, INM‐CM5‐0 and MPI‐ESM1‐2‐LR. Minimum temperature by INM‐CM5‐0 and MPI‐ESM1‐2‐LR model. The MME of XGBR and RFR stand out for their superior performance across all six basins, with exceptional performance over the per‐humid basins, while AdaBoost, SVR and the AM underperform. Examining the interseasonal variability of the simulated MMEs over the basins highlights the reliability of these MME models. The anticipated change in maximum and minimum temperature in the SSP245 and SSP585 in the future horizon corroborates the undeniable rise in temperature by all the MMEs with a dramatic change in future temperature in AM and AdaBoost in precipitation with a factor of two rises in the far future over the recent past. Though climate change is expected to increase precipitation, atmospheric stabilization over the Ghats will affect the spatiotemporal distribution of precipitation. We recommend a comprehensive testing and validation approach to generate ensembles in regional investigations involving complicated and diverse precipitation mechanisms. [ABSTRACT FROM AUTHOR]

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

The rapidly evolving high-throughput sequencing (HTS) technologies generate voluminous genomic and metagenomic sequences, which can help classify the microbial communities with high accuracy in many ecosystems. Conventionally, the rule-based binning techniques are used to classify the contigs or scaffolds based on either sequence composition or sequence similarity. However, the accurate classification of the microbial communities remains a major challenge due to massive data volumes at hand as well as a requirement of efficient binning methods and classification algorithms. Therefore, we attempted here to implement iterative K-Means clustering for the initial binning of metagenomics sequences and applied various machine learning algorithms (MLAs) to classify the newly identified unknown microbes. The cluster annotation was achieved through the BLAST program of NCBI, which resulted in the grouping of assembled scaffolds into five classes, i.e., bacteria, archaea, eukaryota, viruses and others. The annotated cluster sequences were used to train machine learning algorithms (MLAs) to develop prediction models to classify unknown metagenomic sequences. In this study, we used metagenomic datasets of samples collected from the Ganga (Kanpur and Farakka) and the Yamuna (Delhi) rivers in India for clustering and training the MLA models. Further, the performance of MLAs was evaluated by 10-fold cross validation. The results revealed that the developed model based on the Random Forest had a superior performance compared to the other considered learning algorithms. The proposed method can be used for annotating the metagenomic scaffolds/contigs being complementary to existing methods of metagenomic data analysis. An offline predictor source code with the best prediction model is available at (https://github.com/Nalinikanta7/metagenomics). [ABSTRACT FROM AUTHOR]

Limestone mining is a significant economic activity in India, accounting for around 10% of the GDP however, it has certain negative environmental consequences. The objective of this study is to determine the spatial distribution area of captive limestone mines using remote sensing datasets, spectral index, and machine learning algorithms and compare their area estimation with industrial field survey reports for the financial year 2019. The study area includes a limestone resource area of 2226.16 ha with an excavation area of 487.10 ha in 2019. In the present research, we used a high-resolution Sentinel-2A satellite dataset to map and compute the active mining area by implementing the Normalised Vegetation Index (NDVI), Iterative Self-Organizing Data Analysis Technique (ISODATA), K-Nearest Neighbours (KNN), and Random Forest (RF) algorithms in the QGIS 3.18 software tool. The RF classifier estimated a limestone mine area of 379.57 ha with user accuracy (UA) of 97.25% and producer accuracy (PA) of 99.18% with a kappa coefficient value of 0.957. The mine area was estimated at 417.47 ha with a UA of 98.99% and PA of 99.10% and kappa value of 0.947 of the KNN method, The NDVI method estimated 469.92 ha with a UA of 93.63% and PA of 92.04% and kappa value 0.685. This research confirmed that the RF classifier well performed in classification with overall accuracy (OA) of 95.79% to KNN (OA of 94.78%), NDVI (OA of 79.84%) classifiers, and ISODATA poor in classification with OA of 64.16%. This research assists limestone mine owners and environmental engineers in making environmentally sustainable decisions, eco-friendly mine design, and monitoring. [ABSTRACT FROM AUTHOR]

Publicly available EO datasets offer new possibilities to generate biodiversity information at the community composition level, an essential biodiversity variable, beyond forest type. We demonstrated the potential of Sentinel-2, GEDI LiDAR canopy height and ALOS-DEM in discriminating and classifying tropical tree communities in the Western Himalayas, India. For this, tree communities were first identified based on the ordination of field data and subsequently classified using satellite data applying machine learning, i.e. random forest (RF). From the three forest types in the study area, eight distinct tree communities were identified for which classification accuracy increased from single date (75.17%) to multi-date images (85.33%) and further by applying feature selection (88.17%). Whereas the best classification accuracy of 94.66% was achieved when canopy height and topographic variables were also considered. The findings suggest that RF is suitable for mapping tree communities by combining Sentinel-2 with GEDI and DEM parameters. [ABSTRACT FROM AUTHOR]

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

Climate change poses a major threat to global food security. Agricultural systems that rely on monsoon rainfall are especially vulnerable to changes in climate variability. This paper uses machine learning to deepen understanding of how monsoon variability impacts agricultural productivity. We demonstrate that random forest modelling is effective in representing rice production variability in response to monsoon weather variability. Our random forest modelling found monsoon weather predictors explain similar levels of detrended anomaly variation in both rice yield (33%) and area harvested (35%). The role of weather in explaining harvested rice area highlights that production area changes are an important pathway through which weather extremes impact agricultural productivity, which may exacerbate losses that occur through changes in per-area yields. We find that downwelling shortwave radiation flux is the most important weather variable in explaining variation in yield anomalies, with proportion of area under irrigation being the most important predictor overall. Machine learning modelling is capable of representing crop-climate variability in monsoonal agriculture and reveals additional information compared to traditional parametric models. For example, non-linear yield and area responses of irrigation, monsoon onset and season length all match biophysical expectations. Overall, we find that random forest modelling can reveal complex non-linearities and interactions between climate and rice production variability. [ABSTRACT FROM AUTHOR]

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

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

In 2014, the State of Telangana in southern India began repairing and restoring more than 46,000 irrigation water tanks (artificial reservoirs) under the Mission Kakatiya project with an investment in excess of USD 2 billion. In this study, we attempted to map the temporal changes that have occurred in cropland areas and water bodies as a result of the project, using remote sensing imagery and applying land use/land cover (LULC) mapping algorithms. We used 16-day time series data from Landsat 8 to study the spatial distribution of changes in water bodies and cropland areas over the 2013–18 period. Ground survey information was used to assess the pixel-based accuracy of the Landsat-derived data. The areas served by these tanks were identified on the basis of training data and Random Forest algorithms using Google Earth Engine. Our spatial analysis revealed a substantial increase in cropped area under irrigation and expansion of water bodies over the study period. We observed a 20% increase in total tank area in 2017–18 and total cropland and irrigated area expansion of the order of 0.6 M ha and 0.2 M ha, respectively. A comparison of ground survey data and four LULC classes derived from Landsat temporal imagery showed an overall accuracy of 87%, significantly correlated with national agriculture statistics. Periodic monitoring based on remote sensing has proved to be an effective method of capturing LULC changes resulting from the Mission Kakatiya interventions. Higher-resolution satellite data can further improve the accuracy of estimates. [ABSTRACT FROM AUTHOR]

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

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

Study on pearl millet discrimination in mixed cropping environment was carried out as it forms the major coarse cereal crop in India. The study aims at determining the capabilities of Sentinel1A SAR data and Machine Learning (ML) algorithms for pearl millet discrimination. Six ML algorithms, that is, k-nearest neighbour (k-NN), Decision Tree (DT), support vector machine (SVM), random forest (RF), multi-layer perceptron (MLP) and Naïve Bayes (NB) were evaluated and compared. Meta-classifier using majority voting technique was proposed by ensembling classifiers (RF, SVM and NB) which were diverse and have comparable accuracies. The diversity assessment was conducted using Cochran Q statistics for ensembling purpose. RF classifier (83.76%) using VH polarization produced better result indicating importance of cross-polarization in crop classification. Further, meta-classifier proposed in the study improved the accuracy to 88.15% (approximately by 5%) and class accuracies of pearl millet resulting in improved discrimination of pearl millet in mixed cropping environment. [ABSTRACT FROM AUTHOR]

The study focuses on the preparation of landslide susceptibility maps in the Kali River valley, Kumaun Himalaya using three machine learning algorithms, namely K-nearest neighbour (KNN), random forest (RF) and extreme gradient boosting (XGB). Fifteen landslide conditioning factors (LCFs) were selected and an inventory of 368 landslides was used for the analysis. Multicollinearity analysis using the variation inflation factor, tolerance and Pearson correlation coefficient (PCC) depicted less to no similarity between all factors. Evaluation of variable importance suggests LCFs such as slope, elevation and distance to thrust contributed significantly and consistently for all three models. Model accuracy was determined and compared using the area under the receiver operating characteristic curve and other statistical signifiers like accuracy, sensitivity, F-measure, accuracy, specificity and recall. The results show that the ensemble algorithms, XGB and RF, yield higher accuracy of approximately 85% compared to the KNN model with 81% accuracy. [ABSTRACT FROM AUTHOR]

Soybean mapping in Indian context is challenging owing to its short growing period coinciding with the monsoon clouds, inter-cropping and smallholders' land. This study proposes an approach for mapping soybean by integrating object-based image analysis with machine learning (ML) based classification using temporal Sentinel-1 SAR (S-1) and Sentinel-2 optical (S-2) data. Field objects were delineated with scale-optimized multi-resolution segmentation using historical S-2 data. Object-based temporal VH-backscatter and NDVI were extracted for training, validation and testing of the three ML models. Validation results showed the outperformance of Extreme gradient boosting (OB-XGBoost) over Random Forest (OB-RF) and Support vector machine (OB-SVM) with an overall accuracy (OA) of 92.50, 91.08 and 90.1, respectively. Testing of OB-XGBoost model resulted in OA, kappa statistics, and F-score (soybean) of 86.12%, 0.82, and 87.23%, respectively. The soybean map produced by the proposed methodology has shown better representation in terms of homogeneity and uniformity than the pixel-based classification. [ABSTRACT FROM AUTHOR]

For the various climatic zones of India, machine learning (ML) models are created in the current work to forecast monthly-average diffuse solar radiation (DSR). The long-term solar radiation data are taken from Indian Meteorological Department (IMD), Pune, provided for 21 cities that span all of India's climatic zones. The diffusion coefficient and diffuse fraction are the two groups of ML models with dual input parameters (sunshine ratio and clearness index) that are built and compared (each category has seven models). To create ML models, two well-known ML techniques, random forest (RF) and k-nearest neighbours (KNN), are used. The proposed ML models are compared with well-known models that are found in the literature. The ML models are ranked according to their overall and within predictive power using the Global Performance Indicator (GPI). It is discovered that KNN models generally outperform RF models. The results reveal that in diffusion coefficient models perform well than diffuse fraction models. Moreover, functional form 2 is the best followed by form 6. The ML models created here can be effectively used to accurately forecast DSR in various climates. [ABSTRACT FROM AUTHOR]

We develop an enhanced accident occurrence prediction model which depends on the heterogeneous ensemble learning to tackle the topic of a accident period prediction in the early stages of the tragedy using millions of the traffic accident information's from the India. In order to start with, we concentrate on the early stages of development of accidents and choose few useful data from five categories: location, the traffic, climate, objects, and the time field. Further, we implement data cleansing, processing of outlier, and the missing value of processing to raise the quality of the data. Data mining methods can support in foreseeing the factors that are influential in concern to make severe damages. The research has significant factors that are closely connected through the severity of accidents on thruways are identified by Random Forest. Top elements influencing unintentional seriousness include temperature, distance, wind Chills, moisture, direction of wind and visibility. The main aim of this research work is to give a architecture to anticipate road crashes gathering data from the social media handles and the open access data, by implementing a ensembled Deep Learning Model. After which the result shows decent outcomes as a resort to the problem and fulfills the objective of prediction model based on algorithms and deep Learning models. [ABSTRACT FROM AUTHOR]

Over recent decades, ambiguous ground vibration induced by blasting operation can cause extensive damage to structures, lives and fields in and around mine premises. As a consequence, it is indispensable to measure the ambiguous ground vibration intensity levels for assessing and reduce their perilous impact. In this investigation, estimation and evaluation of blast-induced ground vibration in terms of peak particle velocity (PPV) through the ensemble machine learning intelligent algorithms were carried out. One hundred and 21 experimental and blasting events were monitored to collect the realtime field data in Mine-A, India. The collected data was randomly split into training and testing to generate models. Eight input parameters include number of holes, burden, spacing, hole diameter, hole depth, top stemming, maximum explosive charge per delay and the distance were selected for development of ensemble machine learning algorithms. An eXtreme gradient boosting (XGBoost) and random forest (RF) ensemble model, Decision Tree were developed to assess the PPV levels. In addition to that, four empirical predictor models proposed by the US Bureau of Mines, Langefors-Kihlstrom, Central Mining Research Institute, and Bureau of Indian Standards were applied to derive a relation between PPV and its influencing parameters. The accuracy and efficiency of developed models can be determined by performance evaluation metrics chosen as the coefficient of determination (R²), and root mean square error (RMSE). Among all models, yielded results evidence that the Decision Tree ensemble model with the R² of 0.9549, and RMSE of 0.0444 was more precise optimum model to assess the PPV. Besides, a sensitivity analysis method was applied in this current study to know the role of the input parameters in estimating PPV. The determined results inferred that burden, number of holes and top stemming are more influenced parameters on the intensity of PPV levels. [ABSTRACT FROM AUTHOR]

We use a rich set of transaction data from a large retailer in India and a dataset on bribe payments to train random forest and XGBoost models using empirical measures guided by Benford's Law, a commonly used tool in forensic analytics. We evaluate the performance around the 2016 Indian Demonetization, which affects the distribution of legal tender notes in India, and find that models using only pre-2016 data or post-2016 data for both training and testing data had F1 score ranges around 90%, suggesting that these models and Benford's law criteria contain meaningful information for detecting bribe payments. However, the performance for models trained in one regime and tested in another falls dramatically to less than 10%, highlighting the role of the institutional setting when using financial data analytics in an environment subject to regime shifts. [ABSTRACT FROM AUTHOR]

As homegrown air travel is getting an ever-increasing number of well-known these days in India with different air ticket booking channels coming up on the web, voyagers are attempting to comprehend how these aircraft organizations settle on choices in regards to ticket costs over the long run. These days, aircraft enterprises are involving complex procedures and techniques to appoint airfare costs in a unique style. These procedures are thinking about a few monetary, showcasing, business and social elements are firmly associated with a definitive airfare cost. Due to the incredible multifaceted nature of the assessing models applied by the transporters, it is really trying for a client to purchase an air ticket at the most negligible expense, since the expense changes effectively. Subsequently, a couple of techniques arranged to offer the fitting chance to the client to buy an air ticket by expecting the airfare cost, are proposed lately. Most of those techniques are utilizing complex expectation models from the computational knowledge research field known as Machine Learning (ML). In this project we are making a Graphical user Interface using tkinter which shows the required ticket prices of flights of aeroplane, Helicopters based upon historical data which uses machine learning algorithms to predict the airfare more accurately. This framework will give individuals the thought regarding the patterns that costs follow and furthermore give an anticipated cost esteem which they can allude to prior to booking their flight passes to set aside cash. This sort of framework or administration can be given to the clients by flight booking organizations which will assist the clients with booking their tickets as needs be. [ABSTRACT FROM AUTHOR]

Precise and reliable runoff forecasting is crucial for water resources planning and management. The present study was conducted to test the applicability of different data-driven techniques including artificial neural networks (ANN), support vector machine (SVM), random forest (RF) and M5P models for runoff forecasting for the lead time of 1 day and 2 days in the Koyna River basin, India. The best input variables for the development of the models were selected by applying the Gamma test (GT). Two different scenarios were considered to select the input variables for 2 days ahead runoff forecasting. In the first scenario, the output of 1 day ahead runoff (t + 1) was not used as an input while it was also used as an input along with other input variables for the development of the models in the second scenario. For 2 days ahead runoff forecasting, the models developed by adopting the second scenario performed more accurately than that of the first scenario. The RF model performed the best for 1 day ahead runoff forecasting with root mean square error (RMSE), coefficient of efficiency (CE), correlation coefficient (r) and coefficient of determination (R2) values of 168.94 m3/s, 0.67, 0.84 and 0.704, respectively, during the test period. For 2 days ahead runoff forecasting, RF and ANN models performed the best in the first and second scenario, respectively. In 2 days ahead runoff forecasting, RMSE, CE, r and R2 values were observed to be 169.72 m3/s, 0.67, 0.84, 0.7023 and 148.55 m3/s, 0.74, 0.87, 0.76 in the first and second scenarios, respectively, during the test period. Finally, the results revealed that the addition of 1 day ahead runoff forecast increased the forecast accuracy of 2 days ahead runoff forecasts. In addition, the dependability of the various models was determined using the uncertainty analysis. [ABSTRACT FROM AUTHOR]

Multi-Model Ensembles (MMEs) are used for improving the performance of GCM simulations. This study evaluates the performance of MMEs of precipitation, maximum temperature and minimum temperature over a tropical river basin in India developed by various techniques like arithmetic mean, Multiple Linear Regression (MLR), Support Vector Machine (SVM), Extra Tree Regressor (ETR), Random Forest (RF) and long short-term memory (LSTM). The 21 General Circulation Models (GCMs) from National Aeronautics Space Administration (NASA) Earth Exchange Global Daily Downscaled Projections (NEX-GDDP) dataset and 13 GCMs of Coupled Model Inter-comparison Project, Phase 6 (CMIP6) are used for this purpose. The results of the study reveal that the application of a LSTM model for ensembling performs significantly better than models in the case of precipitation with a coefficient of determination (R2) value of 0.9. In case of temperature, all the machine learning (ML) methods showed equally good performance, with RF and LSTM performing consistently well in all the cases of temperature with R2 value ranging from 0.82 to 0.93. Hence, based on this study RF and LSTM methods are recommended for creation of MMEs in the basin. In general, all ML approaches performed better than mean ensemble approach. [ABSTRACT FROM AUTHOR]

This study aims to develop an assumption-free data-driven model to accurately forecast COVID-19 spread. Towards this end, we firstly employed Bayesian optimization to tune the Gaussian process regression (GPR) hyperparameters to develop an efficient GPR-based model for forecasting the recovered and confirmed COVID-19 cases in two highly impacted countries, India and Brazil. However, machine learning models do not consider the time dependency in the COVID-19 data series. Here, dynamic information has been taken into account to alleviate this limitation by introducing lagged measurements in constructing the investigated machine learning models. Additionally, we assessed the contribution of the incorporated features to the COVID-19 prediction using the Random Forest algorithm. Results reveal that significant improvement can be obtained using the proposed dynamic machine learning models. In addition, the results highlighted the superior performance of the dynamic GPR compared to the other models (i.e., Support vector regression, Boosted trees, Bagged trees, Decision tree, Random Forest, and XGBoost) by achieving an averaged mean absolute percentage error of around 0.1%. Finally, we provided the confidence level of the predicted results based on the dynamic GPR model and showed that the predictions are within the 95% confidence interval. This study presents a promising shallow and simple approach for predicting COVID-19 spread. [ABSTRACT FROM AUTHOR]

This study compares several advanced machine learning models to obtain the most accurate method for predicting the aeration efficiency (E20) through the Parshall flume. The required dataset is obtained from the laboratory tests using different flumes fabricated in National Institute Technology Kurukshetra, India. Besides, the potential of K Nearest Neighbor (KNN), Random Forest Regression (RFR), and Decision Tree Regression (DTR) models are evaluated to predict the aeration efficiency. In this way, several input combinations (e.g. M1-M15) are provided using the laboratory parameters (e.g. W/L, S/L, Fr, and Re). Different predictive models are obtained based on those input combinations and machine learning models proposed in the present study. The predictive models are assessed based on several performance metrics and visual indicators. Results show that the KNN-M11 model ( R M S E t e s t i n g = 0.002 , R t e s t i n g 2 = 0.929), which includes W/L, S/L, and Fr as predictive variables outperforms the other predictive models. Furthermore, an enhancement is observed in KNN model estimation accuracy compared to the previously developed empirical models. In general, the predictive model dominated in the present study provides adequate performance in predicting the aeration efficiency in the Parshall flume. [ABSTRACT FROM AUTHOR]

The spatial coverage of PM 2.5 monitoring is non-uniform across India due to the limited number of ground monitoring stations. Alternatively, Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2), is an atmospheric reanalysis data used for estimating PM 2.5. MERRA-2 does not explicitly measure PM 2.5 but rather follows an empirical model. MERRA-2 data were spatiotemporally collocated with ground observation for validation across India. Significant underestimation in MERRA-2 prediction of PM 2.5 was observed over many monitoring stations ranging from −20 to 60 μg m−3. The utility of Machine Learning (ML) models to overcome this challenge was assessed. MERRA-2 aerosol and meteorological parameters were the input features used to train and test the individual ML models and compare them with the stacking technique. Initially, with 10% of randomly selected data, individual model performance was assessed to identify the best model. XGBoost (XGB) was the best model (r2 = 0.73) compared to Random Forest (RF) and LightGBM (LGBM). Stacking was then applied by keeping XGB as a meta-regressor. Stacked model results (r2 = 0.77) outperformed the best standalone estimate of XGB. Stacking technique was used to predict hourly and daily PM 2.5 in different regions across India and each monitoring station. The eastern region exhibited the best hourly prediction (r2 = 0.80) and substantial reduction in Mean Bias (MB = −0.03 μg m−3), followed by the northern region (r2 = 0.63 and MB = −0.10 μg m−3), which showed better output due to the frequent observation of PM 2.5 >100 μg m−3. Due to sparse data availability to train the ML models, the lowest performance was for the central region (r2 = 0.46 and MB = −0.60 μg m−3). Overall, India's PM 2.5 prediction was good on an hourly basis compared to a daily basis using the ML stacking technique. [Display omitted] • Ground-based PM 2.5 monitoring sites are often limited. • MERRA-2 underestimates PM 2.5 across India. • ML stacking technique outperformed XGB, RF, and LGBM in PM 2.5 estimation. • India's Eastern and northern regions exhibited the best ML prediction. [ABSTRACT FROM AUTHOR]

Groundwater is the most preferred source of water resource for the human needs. Over-exploitation of the groundwater has been led to the tremendous effect on the groundwater drought. Assessment of groundwater drought is difficult to understand due to its complexity, non-linearity feature. In this study, groundwater drought indices of the state of Bihar, India, have been modeled using machine learning technique. The prediction of SGI was done by using Artificial Neural Network (ANN) and Random Forest (RF) machine learning models. The best input combinations of Gravity Recovery and Climate experiment (GRACE) satellite water equivalent data, rainfall data and groundwater level data was used to predicted the SGI. In this study, SGI of 38 districts of Bihar was calculated using groundwater data from 2002 to 2019. The accuracy and efficiency of the RF and ANN models were measured based on the mean square error (MSE) and correlation coefficient value (r). Compared to two models are shown the RF model is a performs superior as compare to ANN model, which model superior is decided based on the correlation coefficient value (r) as 0.95 and MSE value of 0.11. The results show both ML models such as ANN and RF is showed best results with the input combination of GRACE satellite water equivalent data, rainfall and groundwater data. • Estimation of GW drought indices using ML techniques of Bihar, India. • ANN and RF models were used to predict SGI. • GRACE, rainfall and GW level data were used to predict the SGI. • SGI of 38 districts of Bihar was calculated using the GW data from 2002 to 2019. [ABSTRACT FROM AUTHOR]

• Surveys often aim to measure complex concepts with a few close-ended questions. We introduce a new method for selecting questions. • The method identifies the survey questions that best correspond to answers given in a qualitative interview about the concept. • We field many survey questions and use machine learning (ML) tools to find the questions that best predict the coded qualitative data. • We apply the method to create a five-question survey module to measure women's agency in north India. • The questions chosen are similar across three ML algorithms, and the resulting survey measure of women's agency performs well in diagnostics. Open-ended interview questions elicit rich information about people's lives, but in large-scale surveys, social scientists often need to measure complex concepts using only a few close-ended questions. We propose a new method to design a short survey measure for such cases by combining mixed-methods data collection and machine learning. We identify the best survey questions based on how well they predict a benchmark measure of the concept derived from qualitative interviews. We apply the method to create a survey module and index for women's agency. We measure agency for 209 married women in Haryana, India, first, through a semi-structured interview and, second, through a large set of close-ended questions. We use qualitative coding methods to score each woman's agency based on the interview, which we use as a benchmark measure of agency. To determine the close-ended questions most predictive of the benchmark, we apply statistical algorithms that build on LASSO and random forest but constrain how many variables are selected for the model (five in our case). The resulting five-question index is as strongly correlated with the coded qualitative interview as is an index that uses all of the candidate questions. This approach of selecting survey questions based on their statistical correspondence to coded qualitative interviews could be used to design short survey modules for many other latent constructs. [ABSTRACT FROM AUTHOR]

With the gradual declining streamflow gauging stations in many world-rivers, emphasis is given nowadays to develop remote sensing (RS)-based approaches as the next-generation hydrometry for estimating riverine ecological flow regimes (EFR). For constructing EFR based on daily-streamflow data in scantily-gauged reaches, use of RS techniques in narrow flow-width tropical rain-fed rivers is constrained with the non-availability of finer spatial satellite data at daily scale. To address these limitations, this study proposes a novel framework that integrates the enhanced spatiotemporal adaptive reflectance fusion (FUS) of the 250 m × 1-day resolution Aqua-MODIS and 30 m × 1-day resolution Landsat satellite-based remote sensing images in the near-infrared region with the machine learning algorithms. These developed frameworks are named as Artificial Neural Network-based ANNFUS, Random Forest Regression-based RFRFUS, and Support Vector Regression-based SVRFUS models, which were tested for daily-scale streamflow estimation in a typical Brahmani River Basin, India. The results reveal that by addressing the linear and nonlinear dynamism between the streamflow and satellite signals, all the developed models could simulate the streamflow very well with the Nash-Sutcliffe efficiency>0.8, Kling-Gupta efficiency>0.8, relative root mean square error (rRMSE) of 0.051–0.12, and normalized RMSE of 0.23–0.36. However, for reproducing the high, median, and low streamflow regimes, the SVRFUS model was found to be the best with the NSE>0.85 and KGE>0.8. Conclusively, the proposed approach is found to have the potential to be replicated in other world-river basins to estimate ecological flow regimes at defunct gauging stations facilitating the basin-scale aquatic environmental management. [Display omitted] • To estimate daily streamflow, advocated remote sensing-based machine learning tools. • The models are ANN, Random Forest Regression and Support Vector Regression. • For river/near-river environments, MODIS and Landsat pixels in NIR-band are fused. • Tested the proposed techniques in a narrow-width river constrained with mixels. • For streamflow regimes, Support Vector Regression models are the best. [ABSTRACT FROM AUTHOR]

The Ganga-Brahmaputra moribund deltaic floodplain region hosted many socio-ecologically precious freshwater wetland ecosystems experiencing hydrological alteration. The present study aimed to model hydrological strength (HS) to show the spatial difference and account for the degree and direction of hydrological alteration of Indian moribund deltaic wetland in three phases e.g. (1) phase I (1988–1997), (2) phase II (1998–2007) and phase III (2008–2017). Three key hydrological parameters, such as Water Presence Frequency (WPF), water depth, and hydro-period were considered for hydrological strength modelling using two ensemble Machine Learning (ML) techniques (Random Forest (RF) and XGBoost). Image algebra was employed for phasal change detection. Hydrological strength models show that around 75% of the wetland area was lost in-between phases I to III and the loss was found more intensive in moderate and weak HS zones. Existing wetland shows a clear spatial difference of HS between wetland core and periphery and river linked and delinked or not linked wetlands. Regarding the suitability of the ML models, both are acceptable, however, the XGBoost outperformed in reference to applied 15 statistical validation techniques and field evidence. HS models based on change detection clarified that more than 22% and 55% of the weak HS zone in phases II and III respectively were turned into non-wetland. The degree of alteration revealed that about 40% of wetland areas experienced a negative alteration during phases I to II, and this proportion increased to 63% in between phases II to III. Since the study figured out the spatial nature of HS, degree and direction of alteration at a spatial scale, these findings would be instrumental for adopting rational planning towards wetland conservation and restoration. • Wetlands of moribund deltaic India are highly sensitive to hydrological alteration. • ML models show that about 75% of wetland area was lost during phases I to III. • XGBoost was found as a representative hydrological strength model. • Currently, about 52% of wetland area was found under weak hydrological strength. • About 40% of wetland areas witnessed a negative degree of alteration. [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]