Utilizing InVEST ecosystem services model combined with deep learning and fallback bargaining for effective sediment retention in Northern Iran.

This study aimed to integrate game theory and deep learning algorithms with the InVEST Ecosystem Services Model (IESM) for Sediment Retention (SR) modeling in the Kasilian watershed, Iran. The Kasilian watershed is characterized by multiple sub-watersheds, which vary in their environmental conditions and SR potential, with a total of 19 sub-watersheds. The research was carried out in four phases: mapping SR using the IESM, implementing the Fallback bargaining algorithm based on game theory, applying deep learning algorithms (CNN, LSTM, RNN), and performing statistical analysis for optimal model selection. Based on the results, the analysis of geo-environmental criteria indicated that sub-watersheds with poor conditions regarding rain erosivity, soil erodibility, LS, elevation, and land use faced greater challenges in SR. Utilizing the Fallback bargaining algorithm for sub-watershed prioritization revealed that sub-watershed 5 emerged as having the highest SR potential due to high rain erosivity and a significant LS factor. Spatial SR mapping via game theory algorithm demonstrated that northern sub-watersheds in the Kasilian watershed had greater SR potential. Deep learning algorithms were also utilized for SR distribution modeling, where the RNN model was deemed optimal, yielding error statistics of MAE: 0.05, MSE: 0.04, R: 0.79, RMSE: 0.20, and AUC: 0.97. The SR distribution patterns demonstrated that RNN and LSTM algorithms exhibited similar classification outcomes, differing from those of the CNN algorithm. The prioritization of sub-watersheds using various approaches revealed that the Fallback bargaining algorithm showed a 47% similarity with the InVEST model results. In contrast, deep learning models such as CNN, LSTM, and ARANN exhibited 84%, 79%, and 79% similarity, respectively. These findings supported SR zonation maps, reinforcing that deep learning models outperformed the game theory algorithm. The Alpha Diversity Indices (ADI) confirmed that the outputs from the LSTM and RNN models showed identical changes across all indices. Minimal variations in the other approaches suggested that all five methods yielded similar results based on diversity indices (including Taxa, Dominance, Simpson, and Equitability), indicating no significant differences among them when compared to the InVEST model in sediment modeling.