Evaluating the Efficiency of K nearest Neighbor and Fuzzy C-Means Clustering Based Methods in the Outputs of Hydrological Models

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Delpasand Rozhin; FATHABADI ABOALHASAN; ROUHANI HAMED; SEYEDIAN SEYED MORTEZA

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Journal: Watershed Management Research Year:2019 | Volume:32 | Issue:3 (124) Page(s): 63-77

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Title

Evaluating the Efficiency of K nearest Neighbor and Fuzzy C-Means Clustering Based Methods in the Outputs of Hydrological Models

Author(s)

Delpasand Rozhin | FATHABADI ABOALHASAN | ROUHANI HAMED | SEYEDIAN SEYED MORTEZA | Issue Writer Certificate

Keywords

GR5J modelQ1

KasilianQ2

model combiningQ1

rainfall-runoffQ1

SACRAMENTO modelQ1

Abstract

Because of incomplete model input and imperfections of the model structure there is no single hydrological model that has the best performance in different conditions and present outputs without uncertainty. In this situation by combining individual modelʼ s outputs, the strengths of each single model are used to make a new model that performs better than each single model. The efficiency of nonparametric K nearest neighbor and the fuzzy C-means clustering based methods were compared with BGA (Bates Granger Averaging), GRA (Granger Ramanathan Averaging), AICA (Akaike Information Criterion), BICA(Bayes Information Criterion), equal weights averaging and lasso methods in averaging output of hydrological models GR5J, SimHyd, SACRAMENTO and SMAR. Firstly, using the amount of rainfall, evapotranspiration, temperature, and the daily discharge of the Kasilian Watershed in Pol Sefid city at the Bon Koh Station was simulated by each hydrological model. Then different model averaging methods were used to combine the output of each single model. Results indicated that for the calibration period, the GR5J and SACRAMENTO, and the correlation coefficient, Nash Sutcliffe efficiency and RMSE were 0. 83, 0. 69 and 0. 24, respectively. The SimHyd and GR5J models performed better for the validation period; the correlation coefficient, Nash Sutcliffe efficiency and RMSE were 0. 73, 0. 27 and 0. 52, respectively. The lasso and GRA model averaging had the best performance for the calibration period, and for the validation data equal weights averaging and BGA had the best performance. For calibration data K nearest neighbor performed better than fuzzy K means clustering based method and the best performance for two methods was obtained at 20 neighbors and for validation data fuzzy K means clustering based method performed better and its observed model performance was improved as the number of neighbors was increased.

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APA: Copy

Delpasand, Rozhin, FATHABADI, ABOALHASAN, ROUHANI, HAMED, & SEYEDIAN, SEYED MORTEZA. (2019). Evaluating the Efficiency of K nearest Neighbor and Fuzzy C-Means Clustering Based Methods in the Outputs of Hydrological Models. WATERSHED MANAGEMENT RESEARCHES (PAJOUHESH-VA-SAZANDEGI), 32(3 (124) ), 63-77. SID. https://sid.ir/paper/377277/en

Vancouver: Copy

Delpasand Rozhin, FATHABADI ABOALHASAN, ROUHANI HAMED, SEYEDIAN SEYED MORTEZA. Evaluating the Efficiency of K nearest Neighbor and Fuzzy C-Means Clustering Based Methods in the Outputs of Hydrological Models. WATERSHED MANAGEMENT RESEARCHES (PAJOUHESH-VA-SAZANDEGI)[Internet]. 2019;32(3 (124) ):63-77. Available from: https://sid.ir/paper/377277/en

IEEE: Copy

Rozhin Delpasand, ABOALHASAN FATHABADI, HAMED ROUHANI, and SEYED MORTEZA SEYEDIAN, “Evaluating the Efficiency of K nearest Neighbor and Fuzzy C-Means Clustering Based Methods in the Outputs of Hydrological Models,” WATERSHED MANAGEMENT RESEARCHES (PAJOUHESH-VA-SAZANDEGI), vol. 32, no. 3 (124) , pp. 63–77, 2019, [Online]. Available: https://sid.ir/paper/377277/en

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