JOURNAL ARTICLE

Runoff Prediction Using Artificial Neural Networks

Varnica KapoorPrachi AryaRupesh Kumar Mishra

Year: 2019 Journal:   Zenodo (CERN European Organization for Nuclear Research)   Publisher: European Organization for Nuclear Research

Abstract

The concept of rainfall-runoff variation is a non-linear event and highly tedious and continuously changing process. It involves different parameters which include i.e. mainly rainfall, soil, morphology and vegetation. The modelling for runoff prediction requires many engineering applications. The biggest obstacles in prediction of runoff are the estimation of extreme values. Unless models are not able to get the dynamics of rainfall-runoff process accurately, accurate prediction of these extremes is not possible. Various approaches have been adopted to represent rainfall-runoff process. The best among those approaches is using Artificial Neural Network techniques for the development of long -term and short-term forecasting models. Comparision is held between various approaches for runoff forecasting using ANNs.

Keywords:
Artificial neural network Surface runoff Computer science Artificial intelligence Environmental science Biology Ecology

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Topics

Hydrological Forecasting Using AI
Physical Sciences →  Environmental Science →  Environmental Engineering
Hydrology and Watershed Management Studies
Physical Sciences →  Environmental Science →  Water Science and Technology
Flood Risk Assessment and Management
Physical Sciences →  Environmental Science →  Global and Planetary Change

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