Browsing by Author "Singh, Sudhir Kumar"

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Multi-scenario based urban growth modeling and prediction using earth observation datasets towards urban policy improvement
(Taylor and Francis Ltd., 2022-10-21T00:00:00) Mustak, Sk; Baghmar, Naresh Kumar; Singh, Sudhir Kumar; Srivastava, Prashant Kumar
Urbanization is a growing challenge for city planners and policymakers who are continuously focusing on computer-based statistical models, and machine learning for a sustainable and livable city. The main objectives of this article were to develop a robust artificial intelligence-based hybrid geo-simulation model to support multi-scenario urban growth modeling for urban policy improvement. In this study, earth observation datasets, Artificial Neural Network-Multilayer Perceptron coupled with Markov Chain (MLP-Markov) and Cellular Automata and Markov Chain (CA-Markov) were applied and the best performance was measured for urban growth modeling. The result shows that the urban land use was 25.79, 31.40, 45.19, 89.22 and 147.96 square km in 1971, 1981, 1991, 2001 and 2011 which has been predicted for 2021, 2031, 2041 and 2051 based on the planned and unplanned development scenarios. The predicted urban land use of the planned development scenario is 242.10, 312.69, 363.80 and 400.72 square km while 242.91, 314.31, 366.23 and 403.98 square km of the unplanned development scenario during 2021, 2031, 2041 and 2051. The uncertainty result shows that overall agreement (84.99%) and other indices are higher, and disagreement is lower (15.01%) for MLP-Markov than the CA-Markov for the urban land use prediction. The hybrid geo-simulation models were tested over multiple urban planning indicators to understand urban growth patterns and related scenarios. The result shows that the geo-simulation model is extremely sensitive to the complex pattern of urban growth and disperse indicators over space and time. This study provides a promising guideline for urban planners and conservation scientists to implement a robust artificial intelligence-based hybrid geo-simulation model for compact, organized, and integrated land use-transportation development.HIGHLIGHTS Raipur city passing through sprawling and unplanned development due to uncontrol population growth and unmanaged development practices by local government and planning authority. Master plan of the development authority of the city failed to restrict unplanned development. Proposed urban growth model promote a compact, organized, integrated land use-transportation development and sustainable urban planning. Study, highlighted complexity in the modeling and suggested simplified Machine-based hybrid geo-simulation model for future urban growth for urban policy improvement. � 2022 Informa UK Limited, trading as Taylor & Francis Group.
Performance assessment of phased array type L-band Synthetic Aperture Radar and Landsat-8 used in image classification
(Elsevier, 2022-09-02T00:00:00) Suman, Swati; Srivastava, Prashant K.; Petropoulos, George P.; Avtar, Ram; Prasad, Rajendra; Singh, Sudhir Kumar; Mustak, S.K.; Faraslis, Ioannis N.; Gupta, Dileep Kumar
Owing to its large spatial and periodic temporal coverage, satellite remote sensing has emerged for formulating and implementing strategies for natural resources management. This study focuses on an appraisal of satellite sensors and artificial intelligence techniques such as kernels-based support vector machines (SVMs) and artificial neural networks (ANNs). These methods are used for land cover classification on multispectral and microwave satellite images acquired from Landsat-8 and Advanced Land Observing Satellite (ALOS-2) Phased Array type L-band Synthetic Aperture Radar (PALSAR) over Varanasi, India. The analysis shows comparable the performance of the microwave-classified image compared with the multispectral Landsat-8 image. ANNs and SVMs performed best with an overall accuracy of 97.3% and kappa coefficient of 0.97 for the Landsat-8 image, whereas SVM radial basis function was the best classifier for the ALOS PALSAR image with 94% overall accuracy. Other statistical indices such as kappa total disagreement and allocation disagreement scores revealed similar performances. The analysis demonstrated the ability of microwave data in land cover classification studies with satisfactory performance. These data can be used in nearly all weather and environmental conditions for broad image classification purposes when optical and infrared imagery such as Landsat are unavailable. � 2022 Elsevier Inc. All rights reserved.
Prediction of soil erosion risk using earth observation data under recent emission scenarios of CMIP6
(Taylor and Francis Ltd., 2021-08-26T00:00:00) Kumar, Nirmal; Singh, Sudhir Kumar; Dubey, Amit Kumar; Ray, Ram L.; Mustak, Sk.; Rawat, Kishan Singh
The earth observation data and CMIP6 models were used to predict plausible soil loss from the Ghaghara river basin. The decadal prediction of soil loss (28.64 ton/ha/year) was found high for SSP585 of CanESM5 during 2015�2025. However, the lower value was reported as 21.71 ton/ha/year for SSP245 of MRI-ESM2-0 during 2035�2045. The century level future rainfall erosivity factor was found lowest for SSP245, however highest for SSP585 of Access-ESM1-5, CanESM5, and IPSL-CM6A-LR. The SSP585 (Access-ESM1-5, CanESM5, and IPSL-CM6A-LR) have maximum soil erosion rate as 29.07, 28.03, and 28.0 ton/ha/year, respectively. For the SSP585, increments were observed as 35.93%, 31.04%, and 30%, respectively, compared to the baseline year (2014). Whereas, lowest was reported as 21.7 and 24.9 ton/ha/year and consequently the low increment as 1.31% and 16.55% for both scenarios of MRI-ESM2-0 compared to baseline. We observed that the soil erosion rate is aligned with the predicted rainfall erosivity factor. � 2021 Informa UK Limited, trading as Taylor & Francis Group.