The aim of the present study was to assess the suitability of mountainous areas for construction land on the basis of landslide susceptibility, to obtain the spatial distribution pattern of said suitability and to improve the existing theories and methods used to ascertain said suitability. Taking Hechuan District in Chongqing as the research area and using data relating to 754 historical landslide sites from 2000 to 2016, we selected 22 factors that influence landslides. The factors were classified into five types, namely topography and geomorphology, geological structure, meteorology and hydrology, environmental conditions and human activities. A landslide susceptibility model was constructed using the random forest algorithm, and safety factors of construction land suitability were established according to the results of landslide susceptibility, with the suitability of land for construction in mountainous areas assessed by combining the key factors (natural, social and ecological factors). The weights of the factors were determined through the use of expert approaches to classify the suitability of land for construction in the research area into five levels: prohibited, unsuitable, basically suitable, more suitable and most suitable. The results of the study show that: (1) The average accuracy of the tenfold cross-validation training set data of landslides reached 0.978; the accuracy of the test set reached 0.913; the accuracy of the confusion matrix reached 97.2%; and the area under curve (AUC) values of the training set, test set and all samples were 0.999, 0.756 and 0.989, respectively. Historical landslide events were found to be mostly concentrated in highly susceptible areas, and the landslide risk level in Hechuan District was mostly low or very low (accounting for 76.26% of the study area), although there was also a small proportion with either a high or very high risk level (9.25%). The high landslide susceptibility areas are primarily concentrated in the southern and southeastern ridge, in the valley and near water systems, with landslides occurring less frequently in the gentle hilly basin. (2) The suitability of land for construction in mountainous areas was strongly influenced by landslide susceptibility, distance from roads and distance from built-up areas; among such parameters, rainfall, elevation and lithology significantly influenced landslides in the region. (3) The land suitable for construction in the study area was highly distributed, mainly in urban areas where the three rivers meet and around small towns, with a spatial distribution pattern of high in the middle and low on both sides. Furthermore, the suitability of land for construction in Hechuan District was found to be primarily at the most suitable and more suitable levels (accounting for 84.66% of the study area), although a small proportion qualified for either the prohibited or unsuitable level (accounting for 15.72%). The present study can be extended and applied to similar mountainous areas. The landslide susceptibility map and construction land suitability map can support the spatial planning of mountainous towns, and the assessment results can assist with the development direction of mountainous towns, the layout of construction land and the siting of major infrastructure.

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.

The archaeological site prediction model can accurately identify archaeological site areas to enable better knowledge and understanding of human civilization processes and social development patterns. A total of 129 Neolithic site data in the region were collected using the Xiangyang area as the study area. An eight-factor index system of elevation, slope, slope direction, micromorphology, distance to water, slope position, planar curvature, and profile curvature was constructed. A geospatial database with a resolution of 30 m × 30 m was established. The whole sample set was built and trained in the ratio of 1:1 archaeological to nonarchaeological sites to obtain the prediction results. The average Gini coefficient was used to evaluate the influence of various archaeological site factors. The results revealed that the area under the curve values of the receiver operating characteristic curves were 1.000, 0.994, and 0.867 for the training, complete, and test datasets, respectively. Moreover, 60% of the historical, archaeological sites were located in the high-probability zone, accounting for 12% of the study area. The prediction model proposed in this study matched the spatial distribution characteristics of archaeological site locations. With the model assessed using the best samples, the results were categorized into three classes: low, average, and high. The proportion of low-, average-, and high-probability zones decreased in order. The high-probability zones were mainly located near the second and third tributaries and distributed at the low eastern hills and central hillocks. The random forest (RF) model was used to rank the importance of archaeological site variables. Elevation, slope, and micro-geomorphology were classified as the three most important variables. The RF model for archaeological site prediction has better stability and predictive ability in the case field; the model provides a new research method for archaeological site prediction and provides a reference for revealing the relationship between archaeological activities and the natural environment.