Flood susceptibility mapping of Cheongju, South Korea based on the integration of environmental factors using various machine learning approaches.

Floods are natural occurrences that pose serious risks to human life and the environment, including significant property and infrastructure damage and subsequent socioeconomic challenges. Recent floods in Cheongju County, South Korea have been linked to river overflow. In this study, we created flood susceptibility maps of Cheongju, South Korea using machine learning techniques including support vector regression (SVR), boosted tree (BOOST), and long short-term memory (LSTM) algorithms, based on environmental factors.

Spatial modeling of geogenic indoor radon distribution in Chungcheongnam-do, South Korea using enhanced machine learning algorithms.

Prolonged inhalation of indoor radon and its progenies lead to severe health problems for housing occupants; therefore, housing developments in radon-prone areas are of great concern to local municipalities. Areas with high potential for radon exposure must be identified to implement cost-effective radon mitigation plans successfully or to prevent the construction of unsafe buildings. In this study, an indoor radon potential map of Chungcheongnam-do, South Korea, was generated using a group method of data handling (GMDH) algorithm based on local soil properties, geogenic, geochemical, as well as topographic factors.

Convolutional neural network (CNN) with metaheuristic optimization algorithms for landslide susceptibility mapping in Icheon, South Korea.

Landslides are a geological hazard that can pose a serious threat to human health and the environment of highlands or mountain slopes. Landslide susceptibility mapping is an essential tool for predicting and mitigating landslides. This study aimed to investigate the application of deep learning algorithms based on convolutional neural networks (CNNs) with metaheuristic optimization algorithms, namely the grey wolf optimizer (GWO) and imperialist competitive algorithm (ICA), to landslide susceptibility mapping.

Radon potential mapping in Jangsu-gun, South Korea using probabilistic and deep learning algorithms.

The adverse health effects associated with the inhalation and ingestion of naturally occurring radon gas produced during the uranium decay chain mean that there is a need to identify high-risk areas. This study detected radon-prone areas using a geographic information system (GIS)-based probabilistic and machine learning methods, including the frequency ratio (FR) model and a convolutional neural network (CNN). Ten influencing factors, namely elevation, slope, the topographic wetness index (TWI), valley depth, fault density, lithology, and the average soil copper (Cu), calcium oxide (Cao), ferric oxide (FeO), and lead (Pb) concentrations, were analyzed.

Spatial prediction of landslide susceptibility using hybrid support vector regression (SVR) and the adaptive neuro-fuzzy inference system (ANFIS) with various metaheuristic algorithms.

Landslides are natural and sometimes quasi-natural hazards that are destructive to natural resources and cause loss of human life every year. Hence, preparing susceptibility maps for landslide monitoring is essential to minimizing their negative effects. The main aim of the current research was to develop landslide susceptibility maps for Icheon Township, South Korea, using hybrid Machin learning and metaheuristic algorithms, that is, the bee algorithm (Bee), the adaptive neuro-fuzzy inference system (ANFIS), support vector regression (SVR), and the grey wolf optimizer (GWO), and to compare their predictive accuracy.

A Novel Ensemble Artificial Intelligence Approach for Gully Erosion Mapping in a Semi-Arid Watershed (Iran).

In this study, we introduced a novel hybrid artificial intelligence approach of rotation forest (RF) as a Meta/ensemble classifier based on alternating decision tree (ADTree) as a base classifier called RF-ADTree in order to spatially predict gully erosion at Klocheh watershed of Kurdistan province, Iran. A total of 915 gully erosion locations along with 22 gully conditioning factors were used to construct a database. Some soft computing benchmark models (SCBM) including the ADTree, the Support Vector Machine by two kernel functions such as Polynomial and Radial Base Function (SVM-Polynomial and SVM-RBF), the Logistic Regression (LR), and the Naïve Bayes Multinomial Updatable (NBMU) models were used for comparison of the designed model.

Modelling gully-erosion susceptibility in a semi-arid region, Iran: Investigation of applicability of certainty factor and maximum entropy models.

Gully erosion susceptibility mapping is a fundamental tool for land-use planning aimed at mitigating land degradation. However, the capabilities of some state-of-the-art data-mining models for developing accurate maps of gully erosion susceptibility have not yet been fully investigated. This study assessed and compared the performance of two different types of data-mining models for accurately mapping gully erosion susceptibility at a regional scale in Chavar, Ilam, Iran.

Land Subsidence Susceptibility Mapping in South Korea Using Machine Learning Algorithms.

In this study, land subsidence susceptibility was assessed for a study area in South Korea by using four machine learning models including Bayesian Logistic Regression (BLR), Support Vector Machine (SVM), Logistic Model Tree (LMT) and Alternate Decision Tree (ADTree). Eight conditioning factors were distinguished as the most important affecting factors on land subsidence of Jeong-am area, including slope angle, distance to drift, drift density, geology, distance to lineament, lineament density, land use and rock-mass rating (RMR) were applied to modelling. About 24 previously occurred land subsidence were surveyed and used as training dataset (70% of data) and validation dataset (30% of data) in the modelling process.

Groundwater productivity potential mapping using evidential belief function.

The evidential belief function (EBF) model was applied and validated for analysis of groundwater-productivity potential (GPP) in Boryeong and Pohang cities, agriculture region in Korea using geographic information systems (GIS). Data about related factors, including topography, lineament, geology, forest, soil, and groundwater data were collected and input into a spatial database. Additionally, in the Boryeong area, specific capacity (SPC) data not lower than 4.

Application of decision tree model for the ground subsidence hazard mapping near abandoned underground coal mines.

Subsidence of ground caused by underground mines poses hazards to human life and property. This study analyzed the hazard to ground subsidence using factors that can affect ground subsidence and a decision tree approach in a geographic information system (GIS). The study area was Taebaek, Gangwon-do, Korea, where many abandoned underground coal mines exist.

Macrobenthos habitat potential mapping using GIS-based artificial neural network models.

This paper proposes and tests a method of producing macrobenthos habitat potential maps in Hwangdo tidal flat, Korea based on an artificial neural network. Samples of macrobenthos were collected during field work, and eight control factors were compiled as a spatial database from remotely sensed data and GIS analysis. The macrobenthos habitat potential maps were produced using an artificial neural network model.

Application of a weights-of-evidence method and GIS to regional groundwater productivity potential mapping.

The aim of this study is to analyze the relationship among groundwater productivity data including specific capacity (SPC) and transmissivity (T) as well as its related hydrogeological factors in a bedrock aquifer, and subsequently, to produce the regional groundwater productivity potential (GPP) map for the area around Pohang City, Korea using a geographic information system (GIS) and a weights-of-evidence (WOE) model. All of the related factors, including topography, lineament, geology, forest, and soil data were collected and input into a spatial database. In addition, SPC and T data were collected from 83 and 81 well locations, respectively.

Spatial prediction of ground subsidence susceptibility using an artificial neural network.

Ground subsidence in abandoned underground coal mine areas can result in loss of life and property. We analyzed ground subsidence susceptibility (GSS) around abandoned coal mines in Jeong-am, Gangwon-do, South Korea, using artificial neural network (ANN) and geographic information system approaches. Spatial data of subsidence area, topography, and geology, as well as various ground-engineering data, were collected and used to create a raster database of relevant factors for a GSS map.

Macrobenthos habitat mapping in a tidal flat using remotely sensed data and a GIS-based probabilistic model.

This paper proposes and tests a method of producing macrofauna habitat potential maps based on a weights-of-evidence model (a probabilistic approach) for the Hwangdo tidal flat, Korea. Samples of macrobenthos were collected during field work, and we considered five mollusca species for habitat mapping. A weights-of-evidence model was used to calculate the relative weights of 10 control factors that affect the macrobenthos habitat.

Application of likelihood ratio and logistic regression models to landslide susceptibility mapping using GIS.

For landslide susceptibility mapping, this study applied and verified a Bayesian probability model, a likelihood ratio and statistical model, and logistic regression to Janghung, Korea, using a Geographic Information System (GIS). Landslide locations were identified in the study area from interpretation of IRS satellite imagery and field surveys; and a spatial database was constructed from topographic maps, soil type, forest cover, geology and land cover. The factors that influence landslide occurrence, such as slope gradient, slope aspect, and curvature of topography, were calculated from the topographic database.