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A New Framework for Evaluation of Rainfall Temporal Variability through Principal Component Analysis, Hybrid Adaptive Neuro-Fuzzy Inference System, and Innovative Trend Analysis Methodology

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Abstract

In this research, a new framework has been introduced for rainfall temporal variability evaluation by using combination of monthly rainfall data sets in three synoptic stations, Principal Component Analysis (PCA), Adaptive Neuro Fuzzy Inference System (ANFIS), Grasshopper Optimization Algorithm (GOA), and Innovative Trend Analysis (ITA) methodology. The first five components were chosen as inputs of the soft-computing models, based on PCA. The GOA was used for training the ANFIS model, in order to estimate the monthly rainfall. The outputs of the ANFIS-GOA were compared to the rainfall estimates by ANFIS-Particle Swarm Optimization (ANFIS-PSO) and ANFIS-Genetic Algorithm (ANFIS-GA). Moreover, various statistical indices, such as mean absolute error (MAE), percent bias (PBIAS) and Nash-Sutcliffe Efficiency (NSE) were used to evaluate the soft-computing models’ performance. Results indicated that ANFIS-GOA had higher accuracy in estimating the rainfall (values of MAE, NSE and PBIAS were 0.21, 0.92 and 0.16 for Mehrabad station, 0.16, 0.94 and 0.14 for Semnan station and 0.24, 0.91 and 0.17 for Noshahr station, respectively) in the testing phase. These values showed significant improvements (67.8%, 21% and 40% for Mehrabad station, 69.2%, 17.5% and 33.3% for Semnan station and 57.1%, 21.3% and 37% for Noshahr station) versus indices related to standalone ANFIS model, which reflected the supremacy and higher accuracy of ANFIS-GOA model in rainfall prediction for different climatic conditions. It was also concluded that the ANFIS-GOA, ANFIS-PSO, and ANFIS-GA models performed superior to the standalone ANFIS-based model, respectively. Furthermore, possible trends in monthly rainfall have been detected by ITA, which is a new graphical model. Results showed significant decreasing trends in January and July for all the rainfall values in Mehrabad station. By comparison of the results obtained from ANFIS and the hybrid models with observed data, it was also concluded that the trends of observed data were close to the ANFIS-GOA predictions.

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Authors and Affiliations

  1. Department of Water Engineering and Hydraulic Structures, Faculty of Civil Engineering, Semnan University, Semnan, 35131-19111, Iran

    Alireza Farrokhi, Saeed Farzin & Sayed-Farhad Mousavi

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  1. Alireza Farrokhi
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  2. Saeed Farzin
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  3. Sayed-Farhad Mousavi
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Correspondence to Saeed Farzin.

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Farrokhi, A., Farzin, S. & Mousavi, SF. A New Framework for Evaluation of Rainfall Temporal Variability through Principal Component Analysis, Hybrid Adaptive Neuro-Fuzzy Inference System, and Innovative Trend Analysis Methodology. Water Resour Manage 34, 3363–3385 (2020). https://doi.org/10.1007/s11269-020-02618-0

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