Multi-Step-Ahead Rainfall-Runoff Modeling: Decision Tree-Based Clustering for Hybrid Wavelet Neural- Networks Modeling

Amir Molajou (), Vahid Nourani (), Ali Davanlou Tajbakhsh (), Hossein Akbari Variani () and Mina Khosravi ()
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Amir Molajou: Iran University of Science and Technology
Vahid Nourani: University of Tabriz
Ali Davanlou Tajbakhsh: Khajeh Nasir al-Din Toosi University of Technology
Hossein Akbari Variani: Iran University of Science & Technology
Mina Khosravi: Iran University of Science & Technology

Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), 2024, vol. 38, issue 13, No 13, 5195-5214

Abstract: Abstract This paper introduces a novel hybrid approach for predicting the rainfall-runoff (r-r) phenomenon across different data division scenarios (50%-50%, 60%-40%, and 75%-25%) within two distinct watersheds, encompassing both monthly and daily scales. Additionally, the effectiveness of this newly proposed hybrid method is evaluated in multi-step ahead prediction (MSAP) scenarios. The proposed method comprises three primary steps. Initially, to address the non-stationarity of the runoff and rainfall time series, these series are decomposed into multiple sub-time series using the wavelet (WT) decomposition method. Subsequently, in the second step, the decomposed sub-series are utilized as input data for the M5 model tree, a decision tree-based model. The M5 model tree classifies the samples of decomposed runoff and rainfall time series into distinct classes. Finally, each class is modeled using an artificial neural network (ANN). The results demonstrate the superior efficiency of the proposed WT-M5-ANN method compared to other available hybrid methods. Specifically, the calculated R2 was 0.93 for the proposed WT-M5-ANN method, whereas it was 0.89 and 0.81 for the WT-ANN (WANN) and WT-M5 methods, respectively, for the Lobbs Hole Creek watershed at the daily scale.

Keywords: Artificial neural network; Multi-step ahead predicting; M5 Model tree; Rainfall-runoff modeling; Wavelet transform (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s11269-024-03908-7

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