N
Navsal Kumar
Researcher at National Institute of Technology, Hamirpur
Publications - 28
Citations - 244
Navsal Kumar is an academic researcher from National Institute of Technology, Hamirpur. The author has contributed to research in topics: Evapotranspiration & Environmental science. The author has an hindex of 5, co-authored 18 publications receiving 78 citations.
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Crop water stress index for scheduling irrigation of Indian mustard (Brassica juncea) based on water use efficiency considerations
Journal ArticleDOI
Spatial and temporal variability analysis of green and blue evapotranspiration of wheat in the Egyptian Nile Delta from 1997 to 2017
Ahmed Elbeltagi,Ahmed Elbeltagi,Muhammad Rizwan Aslam,Ali Mokhtar,Ali Mokhtar,Proloy Deb,Ghali Abdullahi Abubakar,N.L. Kushwaha,Luan Peroni Venancio,Anurag Malik,Navsal Kumar,Jinsong Deng +11 more
TL;DR: In this paper, the authors focused on assessing, evaluating and managing evapotranspiration in Egyptian Nile Delta over 1997-2017 for thirteen regions, using the monthly data of minimum and maximum temperatures, precipitation and solar radiation from November to May.
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Neural computing modelling of the crop water stress index
TL;DR: In this article, two artificial neural network models viz. supervised feed-forward back propagation (FF-BP) and unsupervised Kohonen self-organizing map (K-SOM) have been developed to predict the Crop Water Stress Index (CWSI) using air temperature, relative humidity, and canopy temperature.
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Evaluation of reference evapotranspiration methods and sensitivity analysis of climatic parameters for sub-humid sub-tropical locations in western Himalayas (India)
TL;DR: In this paper, the estimation of crop evapotranspiration (ETc) uses reference evapOTranspiration as a key variable and the FAO-56 Penman-Monteith method provides accurate ET0 estimates.
Journal ArticleDOI
Modelling daily reference evapotranspiration based on stacking hybridization of ANN with meta-heuristic algorithms under diverse agro-climatic conditions
Ahmed Elbeltagi,N. L. Kushwaha,Jitendra Rajput,Dinesh Kumar Vishwakarma,Luc Cimusa Kulimushi,Manish Kumar,Jingwen Zhang,Chaitanya B. Pande,Pandurang Choudhari,Sarita Gajbhiye Meshram,Kusum Pandey,Parveen Sihag,Navsal Kumar,Ismail Abd-Elaty +13 more
TL;DR: The present study demonstrated that the AI-based hybrid meta-heuristics algorithms (ANN-M5P and ANN-Bagging) are promising pathways for ET0 estimation.