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.
Highlights
Hydrological data such as flows and rainfall are the basic information used in the design of water resource systems
Based on the same idea by Tay and Cao (2001) and Hsu et al (2009), this study aims to explore the application of hybrid technique and to test the capability and effectiveness of the idea of hybrid modelling which combines the Self Organizing Map (SOM) with the Least Squares Support Vector Machine (LSSVM) (SOM-LSSVM)
The results indicate that the best performance can be obtained by the SOM-LSSVM model, followed by the LSSVM, Artificial Neural Network (ANN) and Autoregressive Integrated Moving Average (ARIMA) models
Summary
Hydrological data such as flows and rainfall are the basic information used in the design of water resource systems. The ability to forecast future river flow would be beneficial in the field of water management and help in the design of flood protection works in urban areas and for agricultural land. By using knowledge driven modelling, the other catchment variables such as catchment characteristics (size, shape, slope and storage characteristics of the catchment), and geomorphologic characteristics of a catchment (topography, land use patterns, vegetation and soil types that affect the infiltration) must be considered because it is hypothesized that forecasts could be improved if catchment characteristic variables which affect flow were to be included (Jain and Kumar, 2007; Dibike and Solomatine, 2001)
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