EVALUATION OF HYDROLOGICAL AND DATA MINING MODELS IN MONTHLY RIVER DISCHARGE SIMULATION AND PREDICTION (CASE STUDY: ARAZ-KOUSEH WATERSHED)

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GHORBANI KH.; SOHRABIAN E.; SALARIJAZI M.

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Journal: Journal of Water and Soil Conservation Year:2016 | Volume:23 | Issue:1 Page(s): 203-217

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Title

EVALUATION OF HYDROLOGICAL AND DATA MINING MODELS IN MONTHLY RIVER DISCHARGE SIMULATION AND PREDICTION (CASE STUDY: ARAZ-KOUSEH WATERSHED)

Author(s)

GHORBANI KH. | SOHRABIAN E. | SALARIJAZI M. | Issue Writer Certificate

Keywords

MONTHLY RIVER DISCHARGEQ1

ARAZ-KOUSEH WATERSHEDQ1

IHACRESQ2

KNNQ1

M5Q2

Abstract

Background and Objectives: Quantitative prediction of river discharge is one of the most important elements in the management of surface water resources, especially to take suitable decisions in occurrence of floods and drought events. Various approaches introduced in hydrology to predict river discharge among them conceptual models as well as data-driven models are the most important ones. In this study, long term recorded data sets in ARAZ-KOUSEH WATERSHED with 1678 km2 area located in northern Iran (Golestan province) were used to investigate the precision of different river discharge prediction models. The IHACRES model as a conceptual hydrological model and KNN and M5 as data mining models were selected for modeling of MONTHLY RIVER DISCHARGE and the results were compared to examine the accuracy of studied models. In some studies, the expressed models were used for daily river discharge prediction but the main objectives of this study are application of these models to predict monthly discharge for a watershed.Materials and Methods: The 29 years (1985-2013) daily rainfall and discharge data belonging to Araz-Kouseh hydrometry and meteorological stations were used to extract monthly time series for modeling. The required quantity and quality conditions of datasets for modeling were confirmed using different statistical tests. Recorded datasets were divided in two subseries, first one was used for calibration period and second one used for validation of investigated models. The results of models in calibration and validation period were analyzed considering model efficiency goodness of fit criteria.Results: The results of IHACRES conceptual hydrological model for both calibration and validation periods (correlation coefficients equal to 0.81 and 0.79 for calibration and validation periods respectively) showed suitable ability of this model to predict MONTHLY RIVER DISCHARGE. Moreover investigation of results of both M5 and KNN data mining models (correlation coefficient equal to 0.94 and 0.89 for calibration and validation periods respectively for KNN model and equal to 0.92 and 0.88 for calibration and validation periods respectively for M5 model) reveals that application of these models led to significant increase in prediction precision in comparison with IHACRES model.Conclusions: The results of this study indicate the data mining models, i.e. M5 and KNN, outperform conceptual hydrological model, i.e. IHACRES, for prediction of MONTHLY RIVER DISCHARGE considering different goodness of fit criteria. It is clear that the accuracy of the prediction of data mining models are very close to each other but the M5 model is selected as best model in this study because of its explicit equations for prediction. Furthermore, investigation of time series of predicted river discharge show data mining models had better prediction for low discharges in comparison with high discharges.

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APA: Copy

GHORBANI, KH., SOHRABIAN, E., & SALARIJAZI, M.. (2016). EVALUATION OF HYDROLOGICAL AND DATA MINING MODELS IN MONTHLY RIVER DISCHARGE SIMULATION AND PREDICTION (CASE STUDY: ARAZ-KOUSEH WATERSHED). JOURNAL OF WATER AND SOIL CONSERVATION (JOURNAL OF AGRICULTURAL SCIENCES AND NATURAL RESOURCES), 23(1), 203-217. SID. https://sid.ir/paper/156580/en

Vancouver: Copy

GHORBANI KH., SOHRABIAN E., SALARIJAZI M.. EVALUATION OF HYDROLOGICAL AND DATA MINING MODELS IN MONTHLY RIVER DISCHARGE SIMULATION AND PREDICTION (CASE STUDY: ARAZ-KOUSEH WATERSHED). JOURNAL OF WATER AND SOIL CONSERVATION (JOURNAL OF AGRICULTURAL SCIENCES AND NATURAL RESOURCES)[Internet]. 2016;23(1):203-217. Available from: https://sid.ir/paper/156580/en

IEEE: Copy

KH. GHORBANI, E. SOHRABIAN, and M. SALARIJAZI, “EVALUATION OF HYDROLOGICAL AND DATA MINING MODELS IN MONTHLY RIVER DISCHARGE SIMULATION AND PREDICTION (CASE STUDY: ARAZ-KOUSEH WATERSHED),” JOURNAL OF WATER AND SOIL CONSERVATION (JOURNAL OF AGRICULTURAL SCIENCES AND NATURAL RESOURCES), vol. 23, no. 1, pp. 203–217, 2016, [Online]. Available: https://sid.ir/paper/156580/en

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