A
Ashu Jain
Researcher at Indian Institute of Technology Kanpur
Publications - 49
Citations - 3299
Ashu Jain is an academic researcher from Indian Institute of Technology Kanpur. The author has contributed to research in topics: Artificial neural network & Hydrological modelling. The author has an hindex of 18, co-authored 43 publications receiving 2984 citations. Previous affiliations of Ashu Jain include Jawaharlal Nehru Technological University, Hyderabad.
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Review: Methods used for the development of neural networks for the prediction of water resource variables in river systems: Current status and future directions
TL;DR: Despite a significant amount of research activity on the use of ANNs for prediction and forecasting of water resources variables in river systems, little of this is focused on methodological issues and there is still a need for the development of robust ANN model development approaches.
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Hybrid neural network models for hydrologic time series forecasting
Ashu Jain,Avadhnam Madhav Kumar +1 more
TL;DR: The results obtained in this study suggest that the approach of combining the strengths of the conventional and ANN techniques provides a robust modelling framework capable of capturing the non-linear nature of the complex time series and thus producing more accurate forecasts.
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A comparative analysis of training methods for artificial neural network rainfall-runoff models
Sanaga Srinivasulu,Ashu Jain +1 more
TL;DR: It has been found that the RGA trained ANN model significantly outperformed the ANN model trained using BPA, and was also able to overcome certain limitations of the ANN rainfall-runoff model trained with BPA reported by many researchers in the past.
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Short-Term Water Demand Forecast Modelling at IIT Kanpur Using Artificial Neural Networks
TL;DR: In this paper, the authors investigated the use of artificial neural networks (ANN) for forecasting short-term water demand at the Indian Institute of Technology, Kanpur campus, and found that the water demand is better correlated with the rainfall occurrence rather than the amount of rainfall itself.
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Development of effective and efficient rainfall-runoff models using integration of deterministic, real-coded genetic algorithms and artificial neural network techniques
Ashu Jain,Sanaga Srinivasulu +1 more
TL;DR: In this paper, a new approach employing real-coded genetic algorithms (GAs) to train ANN rainfall-runoff models, which are able to overcome low-magnitude flows while developing artificial neural network (ANN) rainfall runoff models trained using popular back propagation (BP) method, is presented.