Modeling and forecasting the impact of agriculture on the ecology of water resources using neural technologies

Using artificial intelligence tools, this study examines the environmental impact of agriculture on the quality of various water resources. Using neural network modeling, nonlinear models for predicting the Water Quality Index (WQI) were constructed. A large number of models for predicting groundwater levels were built. Nonlinear relationships between environmental factors affecting water quality and groundwater levels were established. Models for predicting groundwater levels were constructed for various wells in a specific region. A model-based assessment of the impact of agriculture on the ecology of aquatic pollution was provided. A comprehensive analysis of various data was conducted. DenseNet transfer learning technology was used to create the model, and the neural network architecture was adapted for use with DenseNet in aquatic environments. To improve the quality of model performance, training technology based on the Adam and RMSprop optimizers was implemented. The results of the implementation, evaluation, and performance of the WQI water quality models are visualized. Recommendations for model selection are provided. The key features of the DenseNet transfer learning implementation for various metrics are considered for a full comprehensive assessment of the model analysis; Early stopping and Reduce learning rate technologies are used to improve convergence.