Predicting potential locations of ancient settlements using GIS and Weights-Of-Evidence method (case study: North-East of Iran)

Koohpayma, Javad; Makki, Mohsen; Lentschke, Jan; Alavipanah, Seyed Kazem

doi:10.1016/j.jasrep.2021.103229

Cited by 4 publications

(6 citation statements)

References 16 publications

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“…the elevation of the entire Jianghan plain is almost always less than 10 m, which is more threatened by ooding, and the possibility of building a city site is minimized. Due to their need to obtain food, irrigation for agricultural production, and the idea of avoiding harm [51], the hilly area with an elevation of 10-30 m was the central location for the distribution of city sites. The ooding of rivers posed a signi cant threat to early human habitation, and city sites were built at a safe distance away from the river.…”

Section: Importance and Correlation Of Model Featuresmentioning

confidence: 99%

Predicting ancient city sites using GEE coupled with geographic element features and temporal spectral features: a case study of the Neolithic and Bronze Age of Jianghan region

Yang,

Hu,

Zou

et al. 2024

Preprint

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As an important birthplace of civilization in China, the Yangtze River Basin has the necessary to discover further and investigate the ancient remains, and the archaeological site prediction model is significant for discovering and investigating archaeological remains. In this paper, we focused on the ancient city sites of the Neolithic and Bronze Age in Jianghan region in the middle reaches of the Yangtze River, annotated the specific locations and ranges of 33 ancient city sites using the Google Earth Engine (GEE) cloud platform, and proposed a machine learning ancient city site prediction model by coupling geographic element features and temporal spectral features. Results indicated that the ancient city sites were recognizable in different geographic elements and separable in Sentinel-2 multispectral bands and spectral indices; the coupled time series spectral features could improve the ability of the model to recognize the regions of the ancient city sites, the percentage of pixels with a high probability of prediction (greater than 0.57) within the range of the ancient city sites was 80.0%, and the distribution of the ancient city sites could be obtained from the precise high probability regions. The model proposed can be used to predict the potential geographic locations of ancient city sites and indicate the key areas for future field archaeological survey work.

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Section: Importance and Correlation Of Model Featuresmentioning

confidence: 99%

Predicting ancient city sites using GEE coupled with geographic element features and temporal spectral features: a case study of the Neolithic and Bronze Age of Jianghan region

Yang,

Hu,

Zou

et al. 2024

Preprint

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“…The ancient sites in Xiangyang are mainly distributed in the middle downland, plains and the eastern low mountains and hills, while they are rarely found in the western mountainous areas. This phenomenon indicates that site selection by ancient humans was elevation-related; water is crucial for ancient humans' choice of sites, because of their accessibility to food, need for irrigation in agricultural production, and thoughts of avoiding harm and pursuing profits [40]. Historical sites were established at a safe distance away from a water system.…”

Section: Geographical Variables and The Importance Ranking Of Variablesmentioning

confidence: 99%

A Prediction Study on Archaeological Sites Based on Geographical Variables and Logistic Regression—A Case Study of the Neolithic Era and the Bronze Age of Xiangyang

Chen

et al. 2022

Sustainability

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Archaeological site predictive modeling is widely adopted in archaeological research and cultural resource management. It is conducive to archaeological excavation and reveals the progress of human social civilization. Xiangyang City is the focus of this paper. We selected eight geographical variables as the influencing variables, which are elevation, slope, aspect, micro-landform, slope position, plan curvature, profile curvature, and distance from water. With them, we randomly obtained 260 non-site points at the ratio of 1:1 between site points and non-site points based on the 260 excavated archaeological sites and constructed a sample set of geospatial data and the archaeological based on logistic regression (LR). Using 10-fold cross-validation, we trained and tested the model to select the best samples. Thus, the quantitative relationship between the archaeological sites and geographical variables was established. As a result, the Area Under the Curve (AUC) of the LR model is 0.797 and its accuracy is 0.897 in the study. A geographical detector unveils that the three influencing variables of Distance from water, elevation and Plan Curvature top the chart. The archaeological under LR is highly stable and accurate. The geographical variables constitute crucial variables in the archaeological.

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“…The location of settlements is not random but reflects human sensibility, aligning with landscape attraction and socioeconomic structure [1][2][3][4]. Thus, exploring settlement patterns and uncovering the mechanisms of human environmental strategies is crucial for interpreting past human-land relationships.…”

Section: Introductionmentioning

confidence: 99%

“…The classical statistical method of logistic regression has been widely used for predictive modeling, but it is prone to underfitting and low accuracy [2,7,10,11,15]. Newer models like maximum entropy, weight-of-evidence, and deletion/substitution/addition have also been explored to improve performance and archaeological interpretation [1,3,4,6,16]. However, these models perform weakly when handling some complex relationships.…”

Section: Introductionmentioning

confidence: 99%

“…However, these studies either only considered the influence of single factors, one at a time, or determined the weights of various geographic factors through empirical assessment using tools such as the Analytical Hierarchy Process [43]. APM based on available data appears to be relatively objective and comprehensive in evaluating environmental rules on settlement selection [4]. Using ML models offers the great advantages of incorporating a wider range of potential factors to enhance accuracy and of implementing optimal filtering objectively [44].…”

Section: Introductionmentioning

confidence: 99%

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GIS and Machine Learning Models Target Dynamic Settlement Patterns and Their Driving Mechanisms from the Neolithic to Bronze Age in the Northeastern Tibetan Plateau

Li,

Dong,

Che

et al. 2024

Remote Sensing

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Traditional GIS-based statistical models are intended to extrapolate patterns of settlements and their interactions with the environment. They contribute significantly to our knowledge of past human–land relationships. Yet, these models are often criticized for their empiricism, lopsided specific factors, and for overlooking the synergy between variables. Though largely untested, machine learning and artificial intelligence methods have the potential to overcome these shortcomings comprehensively and objectively. The northeastern Tibetan Plateau (NETP) is characterized by diverse environments and significant changes to the social system from the Neolithic to Bronze Age. In this study, this area serves as a representative case for assessing the complex relationships between settlement locations and geographic environments, taking full advantages of these new models. We have explored a novel modeling case by employing GIS and random forests to consider multiple factors, including terrain, vegetation, soil, climate, hydrology, and land suitability, to construct classification models identifying environmental variation across different cultural periods. The model exhibited strong performance and a high archaeological prediction value. Potential living maps were generated for each cultural stage, revealing distinct environmental selection strategies from the Neolithic to Bronze Age. The key environmental parameters of elevation, climate, soil erosion, and cultivated land suitability were calculated with high weights, influencing human environmental decisions synergistically. Furthermore, we conducted a quantitative analysis of temporal dynamics in climate and subsistence to understand driving mechanisms behind environmental strategies. These findings suggest that past human environmental strategies were based on the comprehensive consideration of various factors, coupled with their social economic scenario. Such subsistence-oriented activities supported human beings in overcoming elevation limitation, and thus allowed them to inhabit wider pastoral areas. This study showcases the potential of machine learning in predicting archaeological probabilities and in interpreting the environmental influence on settlement patterns.

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Predicting potential locations of ancient settlements using GIS and Weights-Of-Evidence method (case study: North-East of Iran)

Cited by 4 publications

References 16 publications

Predicting ancient city sites using GEE coupled with geographic element features and temporal spectral features: a case study of the Neolithic and Bronze Age of Jianghan region

Predicting ancient city sites using GEE coupled with geographic element features and temporal spectral features: a case study of the Neolithic and Bronze Age of Jianghan region

A Prediction Study on Archaeological Sites Based on Geographical Variables and Logistic Regression—A Case Study of the Neolithic Era and the Bronze Age of Xiangyang

GIS and Machine Learning Models Target Dynamic Settlement Patterns and Their Driving Mechanisms from the Neolithic to Bronze Age in the Northeastern Tibetan Plateau

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