Prediction of Groundwater Level and its Correlation with Land Subsidence and Groundwater Quality in Cangzhou, North China Plain, Using Time-Series Long Short-Term Memory Neural Network and Hybrid Models

• Published in: Natural resources research (New York, N.Y.) Vol. 34; no. 3; pp. 1645 - 1666
• Main Authors: Nafouanti, Mouigni Baraka, Li, Junxia, Chakira, Hamada, Nyakilla, Edwin E., Fabiani, Denice Cleophace, Gondwe, Jane Ferah, Sallah, Ismaila
• Format: Journal Article
• Language: English
• Published: New York Springer Nature B.V 01.06.2025
• Subjects: China; Contamination; Correlation; Deep learning; Drinking water; Fluorides; Forecasting; Groundwater; Groundwater levels; Groundwater quality; Land subsidence; linear models; Long short-term memory; Mathematical models; Neural networks; Numerical models; prediction; Regression analysis; Subsidence; Support vector machines; Time series; time series analysis; Water quality; water table; China

Groundwater is the primary source of drinking water in the world, but its contamination and reduction cause environmental problems. Traditional hydraulic and numerical models for assessing groundwater and land subsidence are time-consuming and expensive. Thus, this study used the long short-term mem...