Autoregressive simulation of Zarrinehrud river basin runoff using Procrustes analysis method and artificial neural network and support vector machine models

Subject Areas : Farm water management with the aim of improving irrigation management indicators

بهروز سبحانی ¹ ( دانشیار گروه جغراقیا دانشگاه محقق اردبیلی )
Mohammad Isazadeh ² ( دانشجوی دکتری دانشگاه تبریز )
منیر شیرزاد ³ ( دانشجوی ارشد )

Received: 2016-10-16 Accepted : 2017-05-24 Published : 2017-05-22

Keywords: Procrustes analysis, flow prediction, Artificial Neural Network, Support Vector Machine,

Abstract :

Rivers flow prediction in river basins has an important role in the operation and correct management of water resources. Determining type and number of estimator models inputs is one of the important steps in rivers flow prediction. Therefore, The Procrustes analysis (PA) method for determining the number of effective inputs was used. In this study, flow prediction was done using the flow data obtained from the Safakhaneh and Santeh hydrometric stations. The Artificial Neural Network (ANN) and The Support Vector Machine (SVM) models was used for flow prediction. The best estimation of flow is done using the MLP and SVM models in Safakhaneh hydrometric station with RMSE equal to 5.68 (m3/s) and 4.85 (m3/s), respectively, and CC equal to 0.73 and 0.78, respectively. While in Santeh hydrometric station RMSE was equal to 6.44 (m3/s) and 6.36 (m3/s) respectively, and CC was equal to 0.78 and 0.79 respectively for MLP and SVM models. PA-SVM model showed better results than SVM model in estimating Safakhaneh hydrometric stations flow with RMSE equal to 5.45 (m3/s) and CC equal to 0.73 during the test period. The results also indicated that SVM and PA-SVM models estimated the flow of Santeh station with RMSE equal to 6.85 (m3/s) and 7.03 (m3/s) respectively. Basically, results indicated that the Procrustes analysis method can be used as one of the Efficient and suitable methods for determining the number of effective inputs. Comparison of the ANN and SVM results indicated that ANN model has more accuracy than SVM model. 

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