An application of artificial intelligence for rainfall-runoff modeling

Abstract

This study proposes an application of two techniques of artificial intelligence (AI) for rainfall-runoff modeling: the artificial neural networks (ANN) and the evolutionary computation (EC). Two different ANN techniques, the feed forward back propagation (FFBP) and generalized regression neural network (GRNN) methods are compared with one EC method, Gene Expression Programming (GEP) which is a new evolutionary algorithm that evolves computer programs. The daily hydrometeorological data of three rainfall stations and one streamflow station for Juniata River Basin in Pennsylvania state of USA are taken into consideration in the model development. Statistical parameters such as average, standard deviation, coefficient of variation, skewness, minimum and maximum values, as well as criteria such as mean square error (MSE) and determination coefficient (R 2 ) are used to measure the performance of the models. The results indicate that the proposed genetic programming (GP) formulation performs quite well compared to results obtained by ANNs and is quite practical for use. It is concluded from the results that GEP can be proposed as an alternative to ANN models.

Streamflow Modelling: A Primer on Applications, Approaches and Challenges

“…Comparison of ANNs and GP in daily rainfall-runoff modelling has indicated that the two approaches can yield results of similar quality (Savic et al, 1999;Sivapragasam et al, 2007). Aytek and Alp (2008) compared Gene Expression Programming (GEP) (Ferreira, 2006), a recent extension of GP, to ANNs and also found similar results between the two methods, indicating that GEP is a promising rainfall-runoff modelling tool.…”

Appropriateness of Neural Networks in Climate Prediction and Interpolations: A Comprehensive Literature Review

“…Liu [181]. Solaimani, has used Artificial Neural Network with feed forward back propagation and found the Neural Network method is more appropriate and efficient to predict the river runoff than classical regression model [182].…”

Improving probabilistic flood forecasting through a data assimilation scheme based on genetic programming

“…Other comparisons have been conducted in the following cases: to identify the unit hydrograph of an urban basin, where the results were improved by using GP to obtain mathematical expressions that correct the model errors (Rabunal et al, 2007); to simulate rainfall-runoff processes, enabling GP techniques to provide a useful tool in solving problems in hydrology by means of a simple and explicit model (Aytek and Alp, 2008); to improve the reliability of hydrologic prediction, showing that GP models can be used to predict the model uncertainty (Parasuraman and Elshorbagy, 2008); and finally, to develop a flow prediction method, where the usefulness of a GP model as an effective algorithm to forecast the long-term discharges was demonstrated (Savic et al, 1999;Wang et al, 2009b). In addition to the aforementioned cases, the GP technique was also applied to real-time forecasting (Khu et al, 2001;Kisi and Shiri, 2011).…”