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Basant Yadav
Researcher at Cranfield University
Publications - 26
Citations - 555
Basant Yadav is an academic researcher from Cranfield University. The author has contributed to research in topics: Water table & Groundwater. The author has an hindex of 11, co-authored 21 publications receiving 354 citations. Previous affiliations of Basant Yadav include Indian Institute of Science & Indian Institutes of Technology.
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Evaluation of best management practices for sediment and nutrient loss control using SWAT model
TL;DR: In this paper, the authors evaluated and recommended the BMPs in an agriculture-based Marol watershed (5092 km2) of India, using a hydrologic model, Soil and Water Assessment Tool (SWAT).
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Discharge forecasting using an Online Sequential Extreme Learning Machine (OS-ELM) model: A case study in Neckar River, Germany
TL;DR: The frequent updating of the model in OS-ELM gave a closer reproduction of flood events and peak values with minimum error compared to SVM, ANN and GP, and was comparable to other widely used Artificial Intelligence techniques.
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Assessing the suitability of extreme learning machines (ELM) for groundwater level prediction
TL;DR: In this article, the authors employed two soft computing techniques, namely, extreme learning machine (ELM) and support vector machine (SVM), to forecast groundwater levels at two observation wells located in Canada.
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Ensemble modelling framework for groundwater level prediction in urban areas of India.
TL;DR: Analysis of various input parameters suggest, inclusion of population growth rate is positively correlated with decrease in groundwater levels, which can be useful particularly in cities where lack of pipeline/sewage/drainage lines leakage data hinders physical based modelling.
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Estimation of in-situ bioremediation system cost using a hybrid Extreme Learning Machine (ELM)-particle swarm optimization approach
TL;DR: A simulation-optimization approach to achieve an accurate and cost effective in-situ bioremediation system design for groundwater contaminated with BTEX compounds is presented and results indicate that ELM is a faster and more accurate proxy simulator than ANN and SVM.