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Improving the performance of random forest for estimating monthly reservoir inflow via complete ensemble empirical mode decomposition and wavelet analysis

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Abstract

Estimation of reservoir inflow is of particular importance in optimal planning and management of water resources, proper allocation of water to consumption sectors, hydrological studies, etc. This study aimed to estimate monthly inflow (Q) to the Maroon Dam reservoir located in Iran utilizing climatic data such as minimum, maximum, and mean air temperatures (Tmin, Tmax, T), reservoir evaporation (E), and rainfall (R). The impact of any of the mentioned variables was analyzed by the entropy-based pre-processing technique. The results of the pre-processing showed that the rainfall is the most important parameter affecting the reservoir inflow. Therefore, three types of input patterns were taken into consideration consisting the antecedent Q-based, antecedent R-based, and combined antecedent Q and R-based input combinations. To estimate the monthly reservoir inflow, a random forest (RF) was firstly employed as the standalone model. Then, two different types of hybrid models were proposed via coupling the RF on complete ensemble empirical mode decomposition (CEEMD) and wavelet analysis (W) in order to implement the coupled CEEMD-RF and W-RF models. It is worthwhile to mentioning that six mother wavelets were used in developing the hybrid W-RF models. Four error metrics including root mean square error (RMSE), mean absolute error (MAE), Kling-Gupta efficiency (KGE), and Willmott index (WI) were used to assess the accuracy of implemented models. The attained results indicated the superiority of proposed hybrid models over the classic RF for estimating the monthly reservoir inflow. The most precise model during the test phase was W-RF(3) utilizing the Sym(2) as the mother wavelet under a lagged Q-based pattern with error measures of RMSE = 15.011 m3/s, MAE = 10.439 m3/s, KGE = 0.832, WI = 0.773.

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Authors and Affiliations

  1. Department of Hydrology and Water Resources Engineering, Shahid Chamran University of Ahvaz, Ahvaz, Iran

    Farshad Ahmadi

  2. Water Engineering Department, Urmia University, Urmia, Iran

    Saeid Mehdizadeh

  3. Center of Excellence in Hydroinformatics, Faculty of Civil Engineering, University of Tabriz, Tabriz, Iran

    Vahid Nourani

  4. Faculty of Civil and Environmental Engineering, Near East University, Near East Boulevard, via Mersin 10, 99138, Nicosia, Turkey

    Vahid Nourani

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  1. Farshad Ahmadi
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Correspondence to Saeid Mehdizadeh.

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Ahmadi, F., Mehdizadeh, S. & Nourani, V. Improving the performance of random forest for estimating monthly reservoir inflow via complete ensemble empirical mode decomposition and wavelet analysis. Stoch Environ Res Risk Assess 36, 2753–2768 (2022). https://doi.org/10.1007/s00477-021-02159-x

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