Tracing settlement dynamics in Latium vetus: Explainable machine learning perspectives from the Bronze to the Early iron age
- 1Groningen Institute of Archaeology, University of Groningen, Groningen, Netherlands
Abstract
Understanding the long-term drivers of settlement location requires disentangling ecological constraints from historically contingent choices. Here I apply a Random Forest machine-learning framework, coupled with explainable AI techniques, to investigate settlement dynamics in Latium vetus (central Italy) from the Early Bronze Age to the early Iron Age (c. 2100–725 BCE). Using 108 securely dated sites and a set of environmentally constrained pseudo-absence points, I model phase-specific patterns and evaluate predictor importance through both normalized split frequencies and SHAP values, capturing structural roles as well as the magnitude and direction of effects. The results reveal a coherent diachronic transformation in the environmental logic of settlement. Early phases are primarily structured by hydrological accessibility, whereas from MBA3 onwards topographic configuration and elevation progressively dominate, marking a shift towards morphologically distinctive and defensible locations. This transition culminates in highly canalised settlement signatures during the Final Bronze Age and RMCA phases, before the RMCAIII phase signals a landscape that is increasingly saturated, hierarchically organised and functionally diversified, despite overall political stability. While the reliance on pseudo-absences and uneven sample sizes constrain predictive robustness, the approach demonstrates the value of Random Forest and SHAP as exploratory tools to expose non-linear, threshold-like relationships and to formalise long-standing archaeological interpretations. The study shows how explainable machine learning can provide a quantitative backbone for reconstructing the emergence, consolidation and transformation of territorially structured landscapes in protohistoric central Italy.
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BibTeX
@article{alessandri2026tracing,
title = {{Tracing settlement dynamics in Latium vetus: Explainable machine learning perspectives from the Bronze to the Early iron age}},
author = {Luca Alessandri},
journal = {PLOS One},
year = {2026},
month = sep,
doi = {10.1371/journal.pone.0357054},
url = {https://doi.org/10.1371/journal.pone.0357054},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Tracing settlement dynamics in Latium vetus: Explainable machine learning perspectives from the Bronze to the Early iron age
AU - Luca Alessandri
PY - 2026
DA - 2026/09/16
JO - PLOS One
DO - 10.1371/journal.pone.0357054
UR - https://doi.org/10.1371/journal.pone.0357054
AB - Understanding the long-term drivers of settlement location requires disentangling ecological constraints from historically contingent choices. Here I apply a Random Forest machine-learning framework, coupled with explainable AI techniques, to investigate settlement dynamics in Latium vetus (central Italy) from the Early Bronze Age to the early Iron Age (c. 2100–725 BCE). Using 108 securely dated sites and a set of environmentally constrained pseudo-absence points, I model phase-specific patterns and evaluate predictor importance through both normalized split frequencies and SHAP values, capturing structural roles as well as the magnitude and direction of effects. The results reveal a coherent diachronic transformation in the environmental logic of settlement. Early phases are primarily structured by hydrological accessibility, whereas from MBA3 onwards topographic configuration and elevation progressively dominate, marking a shift towards morphologically distinctive and defensible locations. This transition culminates in highly canalised settlement signatures during the Final Bronze Age and RMCA phases, before the RMCAIII phase signals a landscape that is increasingly saturated, hierarchically organised and functionally diversified, despite overall political stability. While the reliance on pseudo-absences and uneven sample sizes constrain predictive robustness, the approach demonstrates the value of Random Forest and SHAP as exploratory tools to expose non-linear, threshold-like relationships and to formalise long-standing archaeological interpretations. The study shows how explainable machine learning can provide a quantitative backbone for reconstructing the emergence, consolidation and transformation of territorially structured landscapes in protohistoric central Italy.
SN - 1932-6203
ER -