Abstract
The accuracy and consistency of streamflow prediction play a significant role in several applications involving the management of hydrological resources, such as power generation, water supply, and flood mitigation. However, the nonlinear dynamics of the climatic factors jeopardize the development of efficient prediction models. Therefore, to enhance the reliability and accuracy of streamflow prediction, this paper developed a three-stage hybrid model, namely, IVL (ICEEMDAN-VMD-LSTM), which integrated improved complete ensemble empirical mode decomposition with additive noise (ICEEMDAN), variational mode decomposition (VMD), and long short-term memory (LSTM) neural network. Monthly data series of streamflow, temperature, and precipitation in the Swat River Watershed, Pakistan, from January 1971 to December 2015 was used as a case study. Firstly, the correlation analysis and the two-stage decomposition approach were employed to select suitable inputs for the proposed model. ICEEMDAN was employed as a first decomposition stage, to decompose the three data series into intrinsic mode functions (IMFs) and a residual component. In the second decomposition stage, the component of high frequency (IMF1) was decomposed by VMD, as the second decomposition. Afterward, all the components obtained through the correction analysis and the two-stage decomposition approach were predicted by using the LSTM network. Finally, the predicted results of all components were aggregated, to formulate an ensemble prediction for the original monthly streamflow series. The predicted results showed that the performance of the proposed model was superior to the other developed models, in respect of several evaluation benchmarks, demonstrating the applicability of the proposed IVL model for monthly streamflow prediction.
Highlights
Academic Editor: Paolo Madonia e accuracy and consistency of streamflow prediction play a significant role in several applications involving the management of hydrological resources, such as power generation, water supply, and flood mitigation
The results revealed the feasibility of ICEEMDAN and variational mode decomposition (VMD) approaches to improve the performance of the machine learning (ML)-data-driven models (DDMs). e three-stage hybrid prediction model enhanced the performance of the two-stage hybrid prediction models. e VMD-long short-term memory (LSTM) hybrid model presented better results than the ICEEMDANLSTM hybrid model, which indicates the superiority of the VMD technique over the ICEEMDAN technique. e standalone deep learning (DL) models (LSTM and gated recurrent unit neural network (GRU)) showed better results than the standalone random forest regression (RFR), support vector regression (SVR), and radial basis function neural network (RBF) models, which highlight the advantages of the DL models, over the other Machine learning models (MLMs), whereas the RFR ensemble model revealed better results than the SVR and RBF models. e performance of the SVR model was better than the standalone RFB model
The LSTM model was coupled in the hybrid scheme, forming a three-stage hybrid model IVL (ICEEMDAN-VMD-LSTM) to predict monthly streamflow in the Swat River Watershed, Pakistan. e input variables for model development were selected from monthly time series data of streamflow, temperature, and precipitation, by employing correlation functions and the decomposition techniques. e datasets were split into the training (70% of the total dataset) and testing (30% of the total dataset) periods
Summary
Academic Editor: Paolo Madonia e accuracy and consistency of streamflow prediction play a significant role in several applications involving the management of hydrological resources, such as power generation, water supply, and flood mitigation. PDMs consider the physical processes of the water cycle [3], whereas the DDMs are based on artificial intelligence (AI) methods and avoid considering the physical mechanisms of the watershed In other words, these AIbased models are more user-friendly compared to the PDMs [4]. Ese factors include the effects of watershed’s underlying conditions on the accuracy and integrity of data, the intricacy of rainfall-streamflow process, the spatial-temporal variation of climatological data, and the limited knowledge of streamflow patterns in the watersheds. Majority of these models necessitates a large quantity of data for training and testing, which makes these. LSTM network can be employed to model streamflow-precipitation variables due to its ability of learning long-term inputs and outputs dependencies [22]. erefore, LSTM has been successfully applied in numerous streamflow-precipitation studies [23, 24]
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