Current groundwater prediction models often exhibit low accuracy and complex parameter adjustment. To tackle these limitations, a novel prediction model, called improved Aquila optimizer bi-directional long-term and short-term memory (IAO-BiLSTM) network, is proposed. IAO-BiLSTM optimizes the hyperparameters of the BiLSTM network using an IAO algorithm. IAO incorporates three novel enhancements, including population initialization, population updating, and global best individual updating, to overcome the drawbacks of current optimization algorithms. Before making predictions, the challenge posed by the highly nonlinear and non-stationary characteristics of groundwater level signals was addressed through the application of a wavelet multi-scale analysis method. Using a landslide site in Zhejiang Province as an example, a monitoring system is established, and continuous wavelet transform, cross-wavelet transform, and wavelet coherence analysis are employed to perform multi-scale feature analysis on a 2-year dataset of rainfall and groundwater depth. The findings reveal that the groundwater depth of monitoring holes exhibits similar high energy resonating periods and phase relationships, strongly correlating with rainfall. Subsequently, IAO-BiLSTM is employed to predict groundwater depth, and its results are compared with seven popular machine learning regression models. The results demonstrate that IAO-BiLSTM achieves the highest accuracy, as evidenced by its root mean squared error of 0.25.
The wavelet multi-scale analysis method overcomes the limitation of the traditional qualitative analysis method.
The wavelet multi-scale analysis method successfully reveals the multi-scale characteristics and the most significant influencing factors.
An improved Aquila optimizer bi-directional long-term and short-term memory (IAO-BiLSTM) network can predict groundwater level reliably and effectively.
IAO-BiLSTM beats the current seven hot prediction models and showed good performance.