Monthly rainfall–runoff modelling using artificial neural networks

Abstract

Rainfall-runoff models usually present good results, but parameter calibration sometimes is tedious and subjective, and in many cases it depends on additional data surveys in the field. An alternative to the conceptual models is provided by empirical models, which relate input and output by means of an arbitrary mathematical function that bears no direct relationship to the physical characteristics of the rainfall-runoff process. This category includes the artificial neural networks (ANNs), whose implementation is the main focus of this paper. This study evaluated the capacity of ANNs to model with accuracy the monthly rainfall-runoff process. The case study was performed in the Jangada River basin, Paraná, Brazil. The results of the three ANNs that produced the best results were compared to those of a conceptual model at monthly time scale, IPHMEN. The ANNs presented the best results with highest correlation coefficients and Nash-Sutcliffe statistics and the smallest difference of volume.

Comparison of performance of twelve monthly water balance models in different climatic catchments of China

“…Besides the conceptual MWBMs, the artificial intelligence methods are also important tools to simulate monthly rainfall-runoff processes (Komorník et al, 2006;Shu and Ouarda, 2008;Wang et al, 2009;Yilmaz et al, 2011). In some studies, artificial intelligence models exhibited better performances than conceptual MWBMs (Hsu et al, 1995;Shamseldin, 1997;Machado et al, 2011;Rezaeianzadeh et al, 2013). However, artificial intelligence models have also been criticized for their lack of explanation capability, overparameterization and over-fitting (Kaastra and Boyd, 1996;Gaume and Gosset, 2003;de Vos and Rientjes, 2005).…”

Genetic algorithm and fuzzy neural networks combined with the hydrological modeling system for forecasting watershed runoff discharge

“…Artificial neural networks (ANNs) are one kind of datadriven models suitable for rainfall-runoff modeling as reported in the previous studies [9][10][11]. ANNs learning the rainfall-runoff relationship through the training process can quantitatively predict the runoff without requiring the catchment characteristics.…”

Hourly runoff forecasting for flood risk management: Application of various computational intelligence models

“…Since then, there has been an proliferation of related research including the use of radial basis function type artificial neural networks Jayawardena et al, 2006), evolutionary product unit based neural networks for hydrological time series analysis (Karunasingha et al, 2011), water level prediction using artificial neural networks (Biswas and Jayawardena, 2014), and, river flow forecasting (Tawfik et al, 1997;Abrahart and See, 2000;Imrie et al, 2000;Birikundavyi et al, 2002;Cigizoglu, 2003;Moradkhani et al, 2004;Machado et al, 2011), among others. During the last decade or so, fuzzy logic approach has been used in hydrological applications (e.g.…”