Application of an Interpretable Machine Learning Model to Archaeological Site Prediction: A Case Study of the Three Gorges Region in Chongqing

The construction of the Archaeological Site Prediction Model (ASPM) and quantitative research on the driving mechanisms of influencing factors are key to better understanding the multidimensional interactions between ancient humans and the environment. They also constitute an essential technical approach for guiding field archaeological survey and excavation. This study aims to develop a highly stable, accurate, and interpretable predictive model for archaeological sites in the Chongqing Three Gorges region (CQTGR). BP neural network prediction (BPNN) has been applied in various fields, but its random initial weights and thresholds often lead to suboptimal accuracy and weak interpretability. To address these issues, this study constructs BPNN optimized with a Bayesian algorithm to enhance its accuracy. Additionally, it integrates the SHAP model to quantitatively identify nonlinear interactions and threshold effects of influencing factors, thereby improving its interpretability. The results indicate that: (1) The BPNN-based prediction model outperforms other conventional models. After hyperparameter optimization using the Bayesian algorithm, the AUC (area under the ROC curve) on the test set increases by 0.0812, reaching a final value of 0.8815. This indicates that the optimization model is effective and that the model exhibits strong predictive capability. (2) Archaeological sites exhibit a tiered and linear corridor distribution pattern along the Yangtze River and its major tributaries. This pattern can be divided into five concentric tiers radiating outward from the core of the main river systems. High-probability zones are particularly clustered in the low-lying and flat river valleys. (3) The distribution of archaeological sites is comprehensively influenced by both the natural environment and human activities. Elevation and river systems are key driving factors, with NDVI and land use also exerting significant influence. The major factors demonstrate notable threshold and interaction effects. Areas where the interaction between factors exhibits positive enhancement are often the core areas of archaeological site distribution. This research not only provides a precise scientific basis for the preventive protection and monitoring of potential distribution areas of archaeological sites but also offers decision support for the spatial conservation planning of these sites.