Abstract
Sediment load estimation is generally required for study and development of water resources system. In this regard, artificial neural network (ANN) is the most widely used modeling tool especially in data-constraint regions. This research attempts to combine SSA (singular spectrum analysis) with ANN, hereafter called SSA-ANN model, with expectation to improve the accuracy of sediment load predicted by the existing ANN approach. Two different catchments located in the Lower Mekong Basin (LMB) were selected for the study and the model performance was measured by several statistical indices. In comparing with ANN, the proposed SSA-ANN model shows its better performance repeatedly in both catchments. In validation stage, SSA-ANN is superior for larger Nash-Sutcliffe Efficiency about 24% in Ban Nong Kiang catchment and 7% in Nam Mae Pun Luang catchment. Other statistical measures of SSA-ANN are better than those of ANN as well. This improvement reveals the importance of SSA which filters noise containing in the raw time series and transforms the original input data to be near normal distribution which is favorable to model simulation. This coupled model is also recommended for the prediction of other water resources variables because extra input data are not required. Only additional computation, time series decomposition, is needed. The proposed technique could be potentially used to minimize the costly operation of sediment measurement in the LMB which is relatively rich in hydrometeorological records.
Highlights
Quantification of sediment load is necessary for study and development of water resources system such as reservoir storage, dam, irrigation/navigation channel, soil and water conservation measure, environmental impact assessment, etc. [1,2,3,4,5]
Other statistical measures of singular spectrum analysis (SSA)-artificial neural network (ANN) are better than those of ANN as well. This improvement reveals the importance of SSA which filters noise containing in the raw time series and transforms the original input data to be near normal distribution which is favorable to model simulation
The proposed technique could be potentially used to minimize the costly operation of sediment measurement in the Lower Mekong Basin (LMB) which is relatively rich in hydrometeorological records
Summary
Quantification of sediment load is necessary for study and development of water resources system such as reservoir storage, dam, irrigation/navigation channel, soil and water conservation measure, environmental impact assessment, etc. [1,2,3,4,5]. Sediment data are lacking for rivers in many areas of the world, especially in developing and remote regions [6]. It can be estimated with the aid of modeling approaches. The hydrologic and terrain conditions of a river basin change spatio-temporally and this causes difficulties in determining their effects on sediment erosion and transport. This drawback has encouraged the application of black box models, e.g. artificial neural network (ANN). In predicting and forecasting water resources variables, feedforward networks are almost exclusively applied [8]. The term “ANN” used in this paper is referred to feedforward artificial neural network
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