Geography - Research Publications

Permanent URI for this collectionhttps://kr.cup.edu.in/handle/32116/93

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Author: search.filters.author.Mishra, Varun NarayanHas files: No

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    Spatial variations of LST and NDVI in Muzaffarpur district, Bihar using Google earth engine (GEE) during 1990-2020
    (Association of Agrometeorologists, 2023-05-30T00:00:00) Sajan, Bhartendu; Kanga, Shruti; Singh, Suraj Kumar; Mishra, Varun Narayan; Durin, Bojan
    The aim of this study is to analyze land cover changes and their effects on land surface temperature (LST) and normalized difference vegetation index (NDVI) in Muzaffarpur district, Bihar, India. The research utilized Landsat 5 and 8 satellite images taken every five years from 1990 to 2020 to classify seven land cover types, namely built-up areas, wetlands, fallow lands, croplands, vegetation, and water bodies, using the Artificial Neural Network technique in ENVI 5.1. The resulting land cover maps reveal a significant decrease in cropland area during the studied period, while fallow land area decreased from 48.06% to 35.79%. Analysis of LST and NDVI data showed a strong negative correlation (R2 <-0.0057) for all years, except for a weak positive correlation (R2 > 0.006). NDVI values were highest in agricultural lands with the lowest LST values, while fallow land areas showed the opposite trend. The study suggests that vegetation and fallow land are crucial determinants of the spatial and temporal variations in NDVI and LST, relative to urban and water cover categories. � 2023, Association of Agrometeorologists. All rights reserved.
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    Assessment of groundwater potential modeling using support vector machine optimization based on Bayesian multi-objective hyperparameter algorithm
    (Elsevier Ltd, 2022-11-25T00:00:00) Anh, Duong Tran; Pandey, Manish; Mishra, Varun Narayan; Singh, Kiran Kumari; Ahmadi, Kourosh; Janizadeh, Saeid; Tran, Thanh Thai; Linh, Nguyen Thi Thuy; Dang, Nguyen Mai
    Today, water supply in order to achieve sustainable development goals is one of the most important concerns and challenges in most countries. For this reason, accurate identification of areas with groundwater potential is one of the important tools in the protection, management and exploitation of water resources. Accordingly, the present study was conducted with the aim of modeling and predicting groundwater potential in Markazi province, Iran using Multivariate adaptive regression spline (MARS) and Support vector machine (SVM) machine learning models and using two random search (RS) and Bayesian optimization hyperparameter algorithms to optimize the parameters of the SVM model. For this purpose, 18 variables affecting the groundwater potential and 3482 spring locations were used to model the groundwater potential. Data for modeling were divided into two categories of training (70%) and validation (30%). The receiver operating characteristics (ROC) were used to evaluate the performance of the models. The results of evaluation models showed that using hyperparameters random search and Bayesian optimization were improved SVM accuracy in training and validation stages. Bayesian optimization methods are very efficient because they are consciously choosing the parameters of the model that this strategy improves the performance of the model. Evaluating accuracy in the validation stage showed that the AUC value is for MARS, SVM, RS-SVM and B-SVM models 87.40%, 88.25%, 90.73% and 91.73%, respectively. The results of assessment variables importance showed elevation, precipitation in the coldest month, soil and slope variables have the most importance in modeling groundwater potential, while aspect, profile curvature and TWI variables, have the least importance in predicting groundwater potential in Markazi province. � 2022 Elsevier B.V.
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    Cellular Automata-Based Artificial Neural Network Model for Assessing Past, Present, and Future Land Use/Land Cover Dynamics
    (MDPI, 2022-11-08T00:00:00) Sajan, Bhartendu; Mishra, Varun Narayan; Kanga, Shruti; Meraj, Gowhar; Singh, Suraj Kumar; Kumar, Pankaj
    Land use and land cover change (LULCC) is among the most apparent natural landscape processes impacted by anthropogenic activities, particularly in fast-growing regions. In India, at present, due to the impacts of anthropogenic climate change, supplemented by the fast pace of developmental activities, the areas providing the highest agricultural yields are facing the threat of either extinction or change in land use. This study assesses the LULCC in the fastest-changing landscape region of the Indian state of Bihar, District Muzaffarpur. This district is known for its litchi cultivation, which, over the last few years, has been observed to be increasing in acreage at the behest of a decrease in natural vegetation. In this study, we aim to assess the past, present and future changes in LULC of the Muzaffarpur district using support vector classification and CA-ANN (cellular automata-artificial neural network) algorithms. For assessing the present and past LULC of the study area, we used Landsat Satellite data for 1990, 2000, 2010, and 2020. It was observed that between 1990 and 2020, the area under vegetation, wetlands, water body, and fallow land decreased by 44.28%, 34.82%, 25.56%, and 5.63%, respectively. At the same time, the area under built-up, litchi plantation, and cropland increased by 1451.30%, 181.91%, and 5.66%, respectively. Extensive ground truthing was carried out to assess the accuracy of the LULC for 2020, whereas historical google earth images were used for 1990, 2000, and 2010, through the use of overall accuracy and kappa coefficient indices. The kappa coefficients for the final LULC for the years 1990, 2000, 2010, and 2020 were 0.79, 0.75, 0.87, and 0.85, respectively. For forecasting the future LULC, first, the LULC of 1990 and 2010 were used to predict the landscape for 2020 using the CA-ANN model. After calibrating and validating the CA-ANN outputs, LULC for 2030 and 2050 were generated. The generated future LULC scenarios were validated using kappa index statistics by comparing the forecast outcomes with the original LULC data for 2020. It was observed that in both 2030 and 2050, built-up and vegetation would be the major transitioning LULC. In 2030 and 2050, built-up will increase by 13.15% and 108.69%, respectively, compared to its area in 2020; whereas vegetation is expected to decrease by 14.30% in 2030 and 32.84% in 2050 compared to its area in 2020. Overall, this study depicted a decline in the natural landscape and a sudden increase in the built-up and cash-crop area. If such trends continue, the future scenario of LULC will also demonstrate the same pattern. This study will help formulate better land use management policy in the study area, and the overall state of Bihar, which is considered to be the poorest state of India and the most vulnerable to natural calamities. It also demonstrates the ability of the CA-ANN model to forecast future events and comprehend spatiotemporal LULC dynamics. � 2022 by the authors.