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Narendra Singh Raghuwanshi

Researcher at Indian Institute of Technology Kharagpur

Publications -  140
Citations -  5360

Narendra Singh Raghuwanshi is an academic researcher from Indian Institute of Technology Kharagpur. The author has contributed to research in topics: Evapotranspiration & Surface irrigation. The author has an hindex of 31, co-authored 136 publications receiving 4298 citations. Previous affiliations of Narendra Singh Raghuwanshi include Indian Institutes of Technology & North Eastern Regional Institute of Science and Technology.

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Wireless sensor networks for agriculture

TL;DR: The existing state-of-the-art in wireless sensor networks for agricultural applications is reviewed thoroughly and various case studies to thoroughly explore the existing solutions proposed in the literature in various categories according to their design and implementation related parameters.
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Estimating Evapotranspiration using Artificial Neural Network

TL;DR: This study investigates the utility of artificial neural networks (ANNs) for estimation of daily grass reference crop evapotranspiration (ETo) and compares the performance of ANNs with the conventional method (Penman–Monteith) used to estimate ETo.
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Identification and Prioritisation of Critical Sub-watersheds for Soil Conservation Management using the SWAT Model

TL;DR: In this article, a calibrated Soil and Water Assessment Tool (SWAT) model was verified for a small watershed (Nagwan) and used for identification and prioritisation of critical sub-watersheds to develop an effective management plan.
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Temporal Trends in Estimates of Reference Evapotranspiration over India

TL;DR: In this article, the authors studied the temporal trend of ETo along with its regionwise spatial variation, 32 years (1971-2002) monthly meteorological data were collected for 133 selected stations evenly distributed over different agro-ecological regions (AERs) of India.
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Artificial neural networks approach in evapotranspiration modeling: a review

TL;DR: In this article, the use of artificial neural networks (ANNs) in estimation of evapotranspiration has received enormous interest in the present decade and several methodologies have been reported in the literature to realize the ANN modeling of the evapOTranspiration process.