جستجوی مقالات مرتبط با کلیدواژه « توزیع احتمالاتی نرمال » در نشریات گروه « آبخیزداری، بیابان، محیط زیست، مرتع »

Accurate estimation of river flows is one of the fundamental activities in water resources management of river basins. Artificial neural network (ANN) and support vector machine (SVM) are the most important data mining models that can be considered for this purpose. Due to the data-based attribute of these models, probability distribution of data may have a considerable effects on their performance in river flow simulation. In order to, Zarrineh Rud River basin was selected as a study area and the investigations were done for three hydrometric stations located in this basin. In this regard, first monthly runoff probability distribution of stations were studies based on Shapiro- Wilk test and then normalization of data distribution were done. Then the performance of ANN and SVM models in monthly river flow simulation of three stations was evaluated for initial observed and normal data. Based on the results of this study, the values of 0.71, 5.93 (m3/sec), 0.80, 6.58 (m3/sec) and 0.82, 22.9 (m3/sec) were obtained for correlation coefficient (CC) and root mean square errors (RMSE) indicators in the ANN model for Safakhaneh, Santeh and Polanian stations respectively in the testing period. In the SVM model, the values of 0.70, 6.34 (m3/sec), 0.78, 7.02 (m3/sec) and 0.79, 24.31 (m3/sec) were obtained for these indicators in the mentioned stations respectively. The results showed that in river flow simulation by ANN model values of CC increase 6%, 14% and 11% and RMSE values decrease 9%, 19% and 6% for Polanian, Santeh and Safakhaneh stations respectively in the testing period due to normalization of data probability distribution. For SVM model, due to normalization of data probability, CC value increases 10% and RMSE value decrease 16% only for Santeh station. Also the results showed that the ANN model with normal input data has high performance in estimation of monthly river flow compared to the SVM model in each of the three hydrometric stations.