Journal Article10.1080/09715010.2009.10514970
Soft computing tools in rainfall-runoff modeling
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TL;DR: The primary aim of this paper is to review the recent works on Rainfall-Runoff modeling using soft computing techniques.
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Abstract: The use of rainfall-runoff models in the decision making process of water resources planning and management has become increasingly indispensable. Rainfall-runoff modeling in the broad sense started at the end of 19th century and till today there are various types of models based on their mechanism, input data and other modeling requirements. These type of models range from physical, conceptual, empirical models and more sophisticated models like Artificial Neural Network (ANN), Adaptive Neuro Fuzzy Inference System (ANFIS), Genetic Programming (GP), Model Tree (MT), Support Vector Machine (SVM) and recently Chaos theory. The primary aim of this paper is to review the recent works on Rainfall-Runoff modeling using soft computing techniques.
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Citations
Intermittent reservoir daily-inflow prediction using lumped and distributed data multi-linear regression models
TL;DR: In this article, multi-linear regression (MLR) approach is used to construct intermittent reservoir daily inflow forecasting system and the results are also compared with autoregressive integrated moving average (ARIMA) models.
Reservoir inflow forecasting with a modified coactive neuro-fuzzy inference system: a case study for a semi-arid region
Mohammed Falah Allawi,Othman Jaafar,Firdaus Mohamad Hamzah,Nuruol Syuhadaa Mohd,Ravinesh C. Deo,Ahmed El-Shafie +5 more
TL;DR: In this paper, a modified coactive neuro-fuzzy inference system (CANFIS) method is proposed to improve the mathematical procedure, thus enabling a better detection of the high nonlinearity patterns found in the reservoir inflow training data, leading to a consequent update of the membership rules and the induction of the centreweighted set rather than the global weighted set used in feature extraction.
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Behavioural analysis of a time series–A chaotic approach
TL;DR: In this article, the authors used correlation dimension method (CDM) based on Grassberger-Procaccia algorithm for studying the chaotic behaviour of Indian rainfall data and found that CDM is an efficient method for behavioural study of a time series and provided first hand information on the number of dimensions to be considered for time series prediction modelling.
Application of artificial neural network and adaptive neuro-fuzzy inference system in streamflow forecasting
Mehdi Vafakhah,Saeid Janizadeh +1 more
- 01 Jan 2021
TL;DR: Estimation of flood peak discharge and runoff volume is one of the major challenges in watershed management and the results showed that the ANFIS model has better performance than the ANN model for predicting the flood peak discharged and also runoff volume.
11
•Dissertation
Comparison of rainfall runoff models for the Florentine catchment
Jan Gert Rinsema
- 01 Jan 2014
TL;DR: In this paper, a stepwise model for the selection of a rainfall runoff model for a single catchment in Tasmania, Australia is presented. But the model selection is not uniform for all the catchments and can lead to the wrong selection of the model for each catchment.
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