Predicting Effects of Conservation Practices on Runoff, Sediment, and Nutrient Loads from a Commercial Cotton Field Using Machine Learning and Deep Learning
Description
Estimating real-time sources of pollutants and evaluating the effectiveness of conservation practices in agriculture are crucial for prevention of water resources contamination. Pollutant load released from agricultural fields can be estimated using process-based model or data driven models. This study utilized machine learning (ML) models to predict water pollutant loads since they need fewer input features than process-based models. Hydro-meteorological data (e.g., temperature, rainfall, runoff) were collected from control and treatment fields (2016–2022), where cover crops and filter strips were used for pollution mitigation. Pollutant loads, including sediment, total phosphorus (TP), and total nitrogen (TN), were measured and used to train nine ML models: Multiple Linear Regression (MLR), K-Nearest Neighbors (KNN), Random Forest (RF), Extreme Gradient Boosting (XGB), Histogram Gradient Boosting (HGBt), Artificial Neural Networks (ANN), Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN), and a hybrid CNN-LSTM model. Results showed that the hybrid model best-predicted runoff in the control field (R²=0.87) and KNN in the treatment field (R²=0.82). LSTM excelled in sediment prediction for both fields, while RF and ANN were superior for TP and TN predictions, respectively. Model performance declined from runoff to sediment to nutrient loads due to error propagation. Advanced models (e.g., LSTM, CNN, hybrid) outperformed conventional ML models, showing robustness.