Sheng-Jun Jin

A hybrid model for agricultural pollution load forecasting by integrating meteorological data and soil properties: an XGBoost-LSTM approach

A hybrid forecasting model that fuses eXtreme Gradient Boosting for spatial feature importance evaluation with Long Short-Term Memory (LSTM) networks for sequential load prediction is proposed that provides a robust tool for proactive nutrient runoff management in data-sparse agricultural contexts.