Soil moisture prediction based on integrating the CEEMDAN decomposition technique with LSTM in the proximity of Prosopis Juliflora

Human-made adaptations in plant diversity, particularly the introduction of alien invasive plant species, can result in problems such as depletion of groundwater, extraction of natural water resources, and modifications in the local biodiversity. In addition to regulating ecosystems and accurate farming, precise forecasting of soil moisture in the vicinity of these exotic plant species can greatly assist in various other agricultural applications. In this study, we present an improved approach for predicting soil moisture levels at different depths in the vicinity of Prosopis Juliflora (IAPS) by integrating long short-term memory (LSTM) and complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN). CEEMDAN is employed as a preprocessing and denoising technique to decompose the initial soil moisture data into several intrinsic mode functions (IMF). After that, LSTM is used to predict all the IMF (Intrinsic Mode Function) parts from the CEEMDAN process. The individual prediction outcomes of each IMF are combined to obtain the final prediction results. Soil moisture measurements were collected continuously in the Coimbatore area of Tamil Nadu at multi-layer soil depths of 10 cm, 30 cm, 60 cm, and 100 cm. The provided data was utilized for training models and achieving optimal accuracy in forecasting soil moisture levels one month prior. The proposed CEEMDAN-LSTM model achieved the highest R2, ranging from 0.985 to 0.995 at various depths when compared to RNN, LSTM, EMD-LSTM, EEMDLSTM models, and existing literature models. The results show that the proposed method works well at predicting soil moisture at different depths, which can help improve the planting pattern and schedule of native species to increase the area's green cover.