A Statistical Prediction Model for Healthcare and Landslide Sensitivity Evaluation in Coal Mining Subsidence Area

Abstract

The purpose of this study is to compare the results of the frequency ratio (FR) model with the weight of evidence (WOE) and the logical regression (LR) methods when applied to the landslide susceptibility evaluation in coal mining subsidence areas. Key geological disaster prevention and control areas are taken as the research areas. Field investigation is carried out according to the recorded landslide disaster points in the past five years, and 86 landslide disaster points are determined from the remote sensing satellite images. Furthermore, 12 factors affecting the occurrence of landslide are selected as landslide sensitivity evaluation factors. Among them, slope degree, curvature, elevation, and slope aspect are derived using the digital elevation model (DEM) through 30 m × 30 m resolution. The DEM datasets are derived from the geospatial data cloud, lithology datasets are derived from the geological lithology maps, and land use type map is derived from the current situation of national land use. The distances between roads and coal mining subsidence areas are calculated according to field investigation and remote sensing image interpretation results. In addition, the evaluation model includes an annual rainfall distribution map. Finally, the accuracy of three models is compared by ROC curve analysis. The elevation results demonstrate that the frequency ratio-logic regression (FR-LR) model takes the maximum accurateness of 0.913, subsequent to the FR model and the frequency ratio-weight of evidence (FR-WOE) model, respectively. Thus, using LR method based on the FR model has guiding significance for predicting the landslide sensitivity in coal mining. This reduces probable risks and disasters that affect human health. Subsequently, this ensures higher safety from the healthcare perspective in the mining fields.

Landslide Risk Mapping Using the Weight-of-Evidence Method in the Datong Mining Area, Qinghai Province

“…Through sensitivity subject curve (ROC) testing, the AUC of the evaluation model proposed in this study is 0.863. According to the experimental schemes of different researchers, the AUC of landslide susceptibility evaluation models is generally around 0.7-0.91 [12,[37][38][39][40]. The accuracy of the predictive model is classified into five categories, and a value above 0.7 indicates that the predictive model is good [41].…”

Landslide susceptibility evaluation based on active deformation and graph convolutional network algorithm

“…There have been plenty of excellent works in landslide susceptibility evaluation (LSE, also called as landslide susceptibility mapping, LSM), and a variety of algorithms were suggested or employed in these works. These diverse methods typically consist of logic regression (Shou and Chen, 2021;Ge et al, 2022), weights of evidence (Goyes-Penafiel and Hernandez-Rojas, 2021), fuzzy logic (Nwazelibe et al, 2023), Analytical Hierarchy Process (AHP) (Wadadar and Mukhopadhyay, 2022), Information value (Es-Smairi et al, 2022), statistical index model (Berhane and Tadesse, 2021), support vector machine (SVM) (Daviran et al, 2022), random forest (RF) (Taalab et al, 2018), convolutional neural network (CNN) (Aslam et al, 2022), recurrent neural network (Ngo et al, 2021), and ensemble learning [such as boosted regression tree-random forest (Chowdhuri et al, 2021), random forest-cusp catastrophe model (Sun et al, 2022), CNN with metaheuristic optimization (Hakim et al, 2022), and so on]. Hakim et al (2022) suggested two ensemble deep learning models including the ensemble of CNN and grey wolf optimizer (GWO) and the complex model of CNN and imperialist competitive algorithm (ICA).…”

Retracted: A Statistical Prediction Model for Healthcare and Landslide Sensitivity Evaluation in Coal Mining Subsidence Area

“…This article has been retracted by Hindawi, as publisher, following an investigation undertaken by the publisher [ 1 ]. This investigation has uncovered evidence of systematic manipulation of the publication and peer-review process.…”