Groundwater level prediction based on data decomposition and ten “plant” algorithm optimization using RELM

Abstract

[Objective] Improving the accuracy of groundwater level time series prediction is of great significance for scientifically predicting the change trends of groundwater levels and ensuring the rational development and utilization of groundwater resources. The aim is to improve the accuracy of groundwater level time series prediction and address the issues of large computational scale and high complexity in data decomposition-based combination time series prediction models. [Methods] A WPT-decomposed IWO/FPA/TGA/SFO/CPA/DO/IVYA/AO/MGO/LEA-RELM prediction model was proposed, combining Regularized Extreme Learning Machine(RELM) with ten “plant” optimization algorithms, including Wavelet Packet Decomposition(WPT), Invasive Weed Optimization(IWO), Flower Pollination Algorithm(FPA), Tree Growth Algorithm(TGA), Sunflower Optimization(SFO), Carnivorous Plant Algorithm(CPA), Dandelion Optimization(DO), Ivy Algorithm(IVYA), Artemisinin Optimization(AO), Moss Growth Optimization(MGO), and Lotus Effect Optimization Algorithm(LEA). This model was validated using groundwater level time series prediction examples from six locations in Yunnan Province, including Xicheng, Nanzhuang, Lin'an, Wenlan, Zhelinzhai, and Botanical Garden. First, the example's groundwater level time series were decomposed into trend and fluctuation components using one-level WPT. Based on these components, a RELM hyperparameter optimization objective function for the example was established. Then, the ten “plant” algorithms were used to optimize the objective function to determine the best hyperparameters. Finally, the optimal hyperparameters were used to establish IWO/FPA/TGA/SFO/CPA/DO/IVYA/AO/MGO/LEA-RELM models to predict and reconstruct the trend and fluctuation components of the example's groundwater level time series. [Results] The result showed that IVYA, CPA, and FPA outperformed IWO, AO, SFO, DO in optimization performance, and significantly outperformed LEA, MGO, and TGA. The IVYA-RELM, CPA-RELM, and FPA-RELM models achieved a MEAn absolute percentage error(MAPE) of 0.003 0% to 0.000 4%, a MEA absolute error(MAE) of 0.038 9 m to 0.006 3 m, and a coefficient of determination(DC) of 0.997 7 to 0.999 8, which outperformed other comparison models and demonstrated excellent prediction performance. [Conclusion] The result indicate that the optimization performance of the ten “plant” algorithms is highly consistent with the fitting and prediction accuracy rankings of the ten combined models. Overall, the stronger the optimization ability of the algorithms, the higher the fitting and prediction accuracy, and the better the performance of the combined models. The WPT decomposition, with fewer components and strong regularity, is a simple and efficient decomposition method.

Keywords

groundwater level prediction / wavelet packet decomposition / ten “plant” algorithms / regularized extreme learning machine / example objective function / hyperparameter optimization / influencing factors