Open AccessRUNOFF ESTIMATION IN URBAN CATCHMENT USING ARTIFICIAL NEURAL NETWORK MODELS
Author(s) -
Baharak Motamedvaziri,
Baharak Motamedvaziri,
Baharak Motamedvaziri,
Payam Najafi
Publication year - 2020
Publication title -
plant archives/plant archives
Language(s) - English
Resource type - Journals
eISSN - 2581-6063
pISSN - 0972-5210
DOI - 10.51470/plantarchives.2021.v21.s1.371
Subject(s) - surface runoff , artificial neural network , computer science , support vector machine , data mining , process (computing) , variance (accounting) , multivariate statistics , hydrology (agriculture) , environmental science , artificial intelligence , machine learning , ecology , engineering , geotechnical engineering , accounting , business , biology , operating system
Many types of physical models have been developed for runoff estimation with successful results. However, accurate estimation of runoff remains a challenging problem owing to the lack of field data and the complexity of its hydrological process. In this paper, a machine learning method for runoff estimation is presented as an alternative approach to the physical model. Various types of input variables and artificial neural network (ANN) architectures were examined in this study. Results showed that a two-layer network with the tansig activation function and the Levenberg–Marquardt learning algorithm performed the best. For this architecture, the most effective input vector consists of a catchment perimeter, canal length, slope, runoff coefficient, and rainfall intensity. However, results of multivariate analysis of variance indicated the significant interaction effect of input data and the ANN architecture. Thus, to create a suitable ANN model for runoff estimation, a systematic determination of the input vector is necessary