Volume 8, Issue 16 (2-2018)                   jwmr 2018, 8(16): 44-52 | Back to browse issues page

XML Persian Abstract Print

Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:

Study of Correlations between Climatic Parameters and Flood of the Maroon River Basin and flood Prediction by Smart Neural Network. jwmr. 2018; 8 (16) :44-52
URL: http://jwmr.sanru.ac.ir/article-1-902-en.html
Abstract:   (841 Views)
Flood is a kind of natural disaster which causes financial damages and fatality for people. Every year, especially in areas like Maroon river basin which have changes in precipitation and temperatures, along with frequent and severe floods. This study aimed to identify the climatic parameters on flood area can be efficiently artificial neural network, better methods applied in anticipation of this event. The method used in this study to predict the process, multilayer perceptron neural network and radial basis that these two neural networks with multiple regression results were compared. Therefore, climatic daily data in 16 years cycle from four stations: Idnak, Dogonbadan, Dehdasht and yasouj (23 September 1994-22 September 2009) are used. By study of correlations between climatic parameters and discharge of Maroon river, effective parameters on flood are determined and multiple regression is used because of determination of effective entrance on flood and comparing the results with the neural network. Study of the results shows that multilayer Perceptron (MLP) along with training algorithm after flowing the error have 0.73 correlation in training process and in test process is 0.68  and also measure of NRMSE in training process is 0.57 and in test process is 0.66 that known as the best model for predicting storm water. Comparing the results of regression and neural network shows that neural network have a higher correlation than the regression, thus in neural network actual data and predicted data have more conformity than accomplished regression model.
Full-Text [PDF 1355 kb]   (311 Downloads)    
Type of Study: Research | Subject: Special
Received: 2018/01/29 | Revised: 2018/02/24 | Accepted: 2018/01/29 | Published: 2018/01/29

Add your comments about this article : Your username or Email:

© 2019 All Rights Reserved | Journal of Watershed Management Research

Designed & Developed by : Yektaweb