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Prashant K. Srivastava
Researcher at Banaras Hindu University
Publications - 267
Citations - 6829
Prashant K. Srivastava is an academic researcher from Banaras Hindu University. The author has contributed to research in topics: Weather Research and Forecasting Model & Environmental science. The author has an hindex of 40, co-authored 231 publications receiving 4931 citations. Previous affiliations of Prashant K. Srivastava include Allahabad University & University of Maryland, College Park.
Papers
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Selection of classification techniques for land use - land cover change investigation
TL;DR: The main objective of this study focuses on the comparison of three classification tools for Landsat images, which are maximum likelihood classification (MLC), support vector machine and artificial neural network (ANN), in order to select the best method among them.
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Machine Learning Techniques for Downscaling SMOS Satellite Soil Moisture Using MODIS Land Surface Temperature for Hydrological Application
TL;DR: In this paper, three artificial intelligence techniques along with the generalized linear model (GLM) are used to improve the spatial resolution of Soil Moisture and Ocean Salinity (SMOS) derived soil moisture, which is currently available at a very coarse scale of ~40 Km.
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Predicting Spatial and Decadal LULC Changes Through Cellular Automata Markov Chain Models Using Earth Observation Datasets and Geo-information
Sudhir Kumar Singh,Sk. Mustak,Prashant K. Srivastava,Prashant K. Srivastava,Szilárd Szabó,Tanvir Islam,Tanvir Islam +6 more
TL;DR: In this article, the authors used remote sensing and GIS tools for studying land use/land cover change and integrating the associated driving factors for deriving useful outputs. And they used the CA-Markov Chain Model (CAMCM) to identify the spatial and temporal changes that have occurred in LULC in this area.
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Integrating GIS and remote sensing for identification of groundwater potential zones in the hilly terrain of Pavagarh, Gujarat, India
TL;DR: In this paper, remote sensing, geographic information systems (GIS) and fieldwork techniques are combined in an attempt to identify groundwater potential zones in the hilly terrain of the Pavagarh region.
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Prioritization of Malesari mini-watersheds through morphometric analysis: a remote sensing and GIS perspective
TL;DR: In this paper, the authors proposed a morphometric analysis and prioritization of ten mini-watersheds of Malesari watershed, situated in Bhavnagar district of Saurashtra region of Gujarat state, India.