Application of neural network and adaptive neuro-fuzzy inference systems for river flow prediction

Abstract

Appropriate outflow from a barrage should be maintained to avoid flooding on the downstream side during the rainy season. Due to the nonlinear and fuzzy behaviour of hydrological processes, and in cases of scarcity of relevant data, it is difficult to simulate the desired outflow using physically-based models. Artificial intelligence techniques, namely artificial neural networks (ANN) and an adaptive neurofuzzy inference system (ANFIS), were used in the reported study to estimate the flow at the downstream stretch of a river using flow data for upstream locations. Comparison of the performance of ANN and ANFIS was made by estimating daily outflow from a barrage located in the downstream region of Mahanadi River basin, India, using daily release data from the Hirakud Reservoir, located some distance upstream of the barrage. To obtain the best input-output mapping, five different models with various input combinations were evaluated using both techniques. The significance of the contribution of two upstream tributaries to barrage outflow estimation was also evaluated. Three feed-forward back-propagation training algorithms were used to train the models. Standard performance indices, such as correlation coefficient, index of agreement, root mean square error, modelling efficiency and percentage deviation in peak flow, were used to compare the performance of the models, as well as the training techniques. The results revealed that the neural network with conjugate gradient algorithm performs better than Levenberg-Marquardt and gradient descent algorithms. The model which considers as input the reservoir release up to three antecedent time steps produced the best results. It was found that barrage outflow could be better estimated by the ANFIS than by the ANN technique.

An Artificial Neural Network for Analyzing Overall Uniformity in Outdoor Lighting Systems

“…The gradient descent with momentum and adaptive learning rate algorithm can train any network as long as its weight, net input, and transfer functions have derivative functions [43]. This method uses back-propagation to calculate the derivative of the performance cost function with respect to the weight and bias variables of the network.…”

A method of groundwater quality assessment based on fuzzy network-CANFIS and geographic information system (GIS)

“…In recent years, artificial intelligence (AI) computational methods, such as the neuro-fuzzy systems have been increasingly applied to environmental issues (Chau 2006;Gharibi et al 2012). The neuro-fuzzy systems are the result of the combination of neural networks and fuzzy logic (Zadeh 1965;Pramanik and Panda 2009). Adaptive neuro-fuzzy inference system (ANFIS) as a multilayer feed-forward network is capable of combining the benefits of both these fields and also uses Gaussian functions for fuzzy sets, linear functions for the rule outputs and Surgeon's inference mechanism and mainly has been used for mapping input-output relationship based on available data sets Nourani et al 2011;Subbaraj and Kannapiran 2010;Ullah and Choudhury 2013).…”

“…For instance, neuro-fuzzy has been used successfully for prediction of flow through rock-fill dams (Heydari and Talaee 2011), river flow (Nayak et al 2004(Nayak et al , 2005Pramanik and Panda 2009;Kisi 2010), suspended sediment estimation (Kisi et al 2008;Cobaner et al 2009;Mirbagheri et al 2010, groundwater vulnerability (Dixon 2005, groundwater quality problems (Lu and Lo 2002;Zhou et al 2007;Hass et al 2012;Rapantova et al 2012;Jang and Chen 2015), daily evaporation (Dogan et al 2010;KarimiGooghari 2012) and rainfall-runoff modeling (Chang and Chen 2001;Gautam and Holz 2001;Xiong et al 2001;Jacquin and Shamseldin 2006). However, little research has been undertaken to study the problem of groundwater quality using ANN and GIS.…”

Hyperspectral Analysis of Soil Total Nitrogen in Subsided Land Using the Local Correlation Mazimization-Complementary Superiority Method

“…In general, many studies have confirmed that PLS was one of the most efficient methods used for constructing reliable models in wide range of fields, including in the field of hyperspectral remote sensing (Nguyen and Lee, 2006). Adaptive neuro-fuzzy inference systems (ANFIS), which combine the aspects of a fuzzy system with those of a neural network, have been widely used in many fields because of its usefulness with complex nonlinear problems (Sharma et al, 2015;Jang, 1993;Paiva et al, 2004;Abbasi and Abouec, 2008;Mukerji et al, 2009;Pramanik and Panda, 2009;Yan, 2010); ANFIS has also been applied to the hyperspectral assessment of soil properties (Tan et al, 2014). Although it is difficult to make full use of hyperspectral data because of the restriction on the number of input variables, ANFIS may be a promising technique in the field of hyperspectral remote sensing.…”