Machine Learning-Driven Archaeological Site Prediction in the Central Part of Jharkhand, India Using Multi-parametric Geospatial Data

The central part of Jharkhand, India, harbours a complex history shaped by ancient civilisations, notably Buddhism, Jainism, and Brahmanism, necessitating a meticulous identification of potential archaeological sites. This study employs a cutting-edge machine learning approach to predict the suitability of archaeological sites in the region, marking a significant evolution in the documentation of such sites. Machine learning-based integration of 12 geoenvironmental datasets using a random forest model reveals a nuanced spatial distribution of potential archaeological sites, categorised into four suitability zones: high, moderately high, moderately low, and low. The region with the best-anticipated suitability comprises around 20.33% of the research area, whereas the area with the lowest expected suitability comprises nearly 41.81%. High suitability zones, characterised by gentle terrain, open vegetation, fertile soils, and water proximity, suggest conditions conducive to human habitation and archaeological preservation. Conversely, low suitability zones exhibit rugged terrain, dense vegetation, poor soil quality, limited water availability, and remoteness from natural resources, indicating potential hindrances to human occupation and archaeological preservation. The model exhibited high predictive accuracy, as evidenced by the ROC–AUC score of 88.3%, enhancing its reliability. Specific locations within the study area demonstrate varying degrees of suitability, providing valuable insights for archaeological site management, cultural heritage preservation, and land-use planning, which will support the restoration and conservation plan of the heritage sites. Furthermore, this machine learning-based archaeological site prediction study underscores its potential applicability in historically rich regions globally, showcasing its significance in uncovering and preserving our shared human history.