Use of Meta-Heuristic Techniques in Rainfall-Runoff Modelling
نویسندگان
چکیده
Each year, extreme floods, which appear to be occurring more frequently in recent years (owing to climate change), lead to enormous economic damage and human suffering around the world. It is therefore imperative to be able to accurately predict both the occurrence time and magnitude of peak discharge in advance of an impending flood event. The use of meta-heuristic techniques in rainfall-runoff modeling is a growing field of endeavor in water resources management. These techniques can be used to calibrate data-driven rainfall-runoff models to improve forecasting accuracies. This Special Issue of the journal Water is designed to fill the analytical void by including papers concerning advances in the contemporary use of meta-heuristic techniques in rainfall-runoff modeling. The information and analyses can contribute to the development and implementation of effective hydrological predictions, and thus, of appropriate precautionary measures.
منابع مشابه
Rainfall-runoff modelling using artificial neural networks (ANNs): modelling and understanding
In recent years, artificial neural networks (ANNs) have become one of the most promising tools in order to model complex hydrological processes such as the rainfall-runoff process. In many studies, ANNs have demonstrated superior results compared to alternative methods. ANNs are able to map underlying relationship between input and output data without prior understanding of the process under in...
متن کاملPii: S0022-1694(00)00344-9
This study compares the accuracy of the short-term rainfall forecasts obtained with time-series analysis techniques, using past rainfall depths as the only input information. The techniques proposed here are linear stochastic auto-regressive movingaverage (ARMA) models, artificial neural networks (ANN) and the non-parametric nearest-neighbours method. The rainfall forecasts obtained using the c...
متن کاملNeural networks and non-parametric methods for improving real- time flood forecasting through conceptual hydrological models
Time-series analysis techniques for improving the real-time flood forecasts issued by a deterministic lumped rainfall-runoff model are presented. Such techniques are applied for forecasting the short-term future rainfall to be used as real-time input in a rainfall-runoff model and for updating the discharge predictions provided by the model. Along with traditional linear stochastic models, both...
متن کاملSoft computing approach for rainfall-runoff modelling: A review
Enormous cost and manpower utilization encountered in constructing a water resource project demands a great deal of attention in devising precise Rainfall-Runoff models for its successful performance. These models are dependent on the physiographic, climatic and biotic characteristics of the basin. These factors sometimes induce either a linear, non-linear or highly complex behaviour among the ...
متن کاملMonthly runoff forecasting by means of artificial neural networks (ANNs)
Over the last decade or so, artificial neural networks (ANNs) have become one of the most promising tools formodelling hydrological processes such as rainfall runoff processes. However, the employment of a single model doesnot seem to be an appropriate approach for modelling such a complex, nonlinear, and discontinuous process thatvaries in space and time. For this reason, this study aims at de...
متن کامل