RAINFALL RUNOFF MODELLING USING NEURAL NETWORKS: STATE-OF-THE-ART AND FUTURE RESEARCH NEEDS-Reference-Cited by-同舟云学术

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RAINFALL RUNOFF MODELLING USING NEURAL NETWORKS: STATE-OF-THE-ART AND FUTURE RESEARCH NEEDS

Published:2009-01 Issue:sup1 Volume:15 Page:52-74
ISSN:0971-5010
Container-title:ISH Journal of Hydraulic Engineering
language:en
Short-container-title:ISH Journal of Hydraulic Engineering

Author:

Jain Ashu,Maier Holger R.,Dandy Graeme C.,Sudheer K. P.

Publisher

Informa UK Limited

Subject

Fluid Flow and Transfer Processes,Water Science and Technology,Civil and Structural Engineering,Environmental Engineering

Link

http://www.tandfonline.com/doi/pdf/10.1080/09715010.2009.10514968

Reference56 articles.

1. Multi-model data fusion for river flow forecasting: an evaluation of six alternative methods based on two contrasting catchments

2. Optimal design of aquifer cleanup systems under uncertainty using a neural network and a genetic algorithm

3. Asymptotic statistical theory of overtraining and cross-validation

4. Artificial Neural Networks in Hydrology. II: Hydrologic Applications

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1. Rainfall-runoff modelling – a comparison of Artificial Neural Networks (ANNs) and Hydrologic Engineering Centre-Hydrologic Modelling System (HEC-HMS);ISH Journal of Hydraulic Engineering;2024-05-06

2. Artificial Intelligence-based model fusion approach in hydroclimatic studies;Handbook of Hydroinformatics;2023

3. A generic data-driven technique for forecasting of reservoir inflow: Application for hydropower maximization;Environmental Modelling & Software;2019-09

4. A Brief Review of Random Forests for Water Scientists and Practitioners and Their Recent History in Water Resources;Water;2019-04-30

5. Training of Artificial Neural Networks Using Information-Rich Data;Hydrology;2014-07-22

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