A hybrid model of self organizing maps and least square support vector machine for river flow forecasting

Abstract

Abstract. Successful river flow forecasting is a major goal and an essential procedure that is necessary in water resource planning and management. There are many forecasting techniques used for river flow forecasting. This study proposed a hybrid model based on a combination of two methods: Self Organizing Map (SOM) and Least Squares Support Vector Machine (LSSVM) model, referred to as the SOM-LSSVM model for river flow forecasting. The hybrid model uses the SOM algorithm to cluster the entire dataset into several disjointed clusters, where the monthly river flows data with similar input pattern are grouped together from a high dimensional input space onto a low dimensional output layer. By doing this, the data with similar input patterns will be mapped to neighbouring neurons in the SOM's output layer. After the dataset has been decomposed into several disjointed clusters, an individual LSSVM is applied to forecast the river flow. The feasibility of this proposed model is evaluated with respect to the actual river flow data from the Bernam River located in Selangor, Malaysia. The performance of the SOM-LSSVM was compared with other single models such as ARIMA, ANN and LSSVM. The performance of these models was then evaluated using various performance indicators. The experimental results show that the SOM-LSSVM model outperforms the other models and performs better than ANN, LSSVM as well as ARIMA for river flow forecasting. It also indicates that the proposed model can forecast more precisely, and provides a promising alternative technique for river flow forecasting.

River Stage Forecasting Using Wavelet Packet Decomposition and Machine Learning Models

“…Over the past years, machine learning approaches have been effectively applied for modeling nonlinear hydrologic systems. Especially, artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS) and support vector machine (SVM) have been acknowledged as successful tools for modeling complex hydrologic systems (Noori et al 2011;Othman and Naseri 2011;Ismail et al 2012;Jothiprakash and Magar 2012;Kim et al 2013;Seo et al 2013aSeo et al , 2013bSeo et al , 2013cSudheer et al 2014).…”

Capability of Artificial Neural Network for Forward Conversion of Geodetic Coordinates $$(\phi ,\lambda ,h)$$ ( ϕ , λ , h ) to Cartesian Coordinates (X, Y, Z)

“…It is well acknowledged that one of the contributing factors affecting the estimation accuracy of ANN is related to the quality of datasets used in model-building and selection of appropriate inputs parameters (Dreiseitl and OhnoMachado 2002;Ismail et al 2012). Therefore, to ensure that the obtained geodetic coordinate (ϕ, λ, h) data from the GPS receivers are reliable and accurate, several factors such as checking of overhead obstruction, observation period, observation principles and techniques as suggested by many researchers were considered (Yakubu and Kumi-Boateng 2011).…”

Comment on "A hybrid model of self organizing maps and least square support vector machine for river flow forecasting" by Ismail et al. (2012)

“…are introduced in the text as the linear function and the Sigmoid function Table 1. The result for training and testing using a hybrid model of SOM-LSSVM for different map sizes (Ismail et al, 2012). respectively, while according to Eq.…”

“…In general, as an unsupervised learning method, the self-organizing map is a kind of artificial neural network (ANN) model for clustering and classification of input data, prediction, and also data mining (Kohonen, 1998;Alhoniemi et al, 1999;Vesanto and Alhoniemi, 2000). Ismail et al (2012) carried out an inclusive study to improve the forecasting of river flow by using four different methods, i.e., SOM-LSSVM, autoregressive integrated moving average (ARIMA), ANN, and least squares support vector machine (LSSVM) models. However, the main contribution of this study was to improve the efficiency of the river flow prediction by employing a self-organizing map (SOM) model for clustering input data and coupling this method with LSSVM model.…”

“…There are lots of forecasting techniques that have successfully been utilized by previously conducted studies in water resource management and water engineering. The study of Ismail et al (2012), which was published in the journal Hydrology and Earth System Sciences in 2012, was a valuable piece of research that investigated the combination of two effective methods (self-organizing map and least squares support vector machine) for river flow forecasting. The goal was to make a comparison between the performances of self organizing map and least square support vector machine (SOM-LSSVM), autoregressive integrated moving average (ARIMA), artificial neural network (ANN) and least squares support vector machine (LSSVM) models for river flow prediction.…”