Assessment of disaster resilience capacity of hillslope communities with high risk for geological hazards

This study presents a novel preparedness assessment method for assessing hazard mitigation and environmental planning of hillslope communities. A professional questionnaire was utilized to weight each indicator. Communities in Hsinchu, Taichung and Nantou counties with debris flow hazards were taken as study samples. Debris flow risk and landslide susceptibility for each community were determined using Geographic Information System (GIS) technology and logistic regression analysis. Thus, a novel risk assessment method for evaluating disaster resilience capacity of hillslope communities was established. This method was then applied to assess casualties caused by Typhoon Herb in 1996 and Typhoon Mindulle in 2004. Additionally, the analytical results generated by this assessment method were discussed with the aim of developing references for implementation of risk analysis, increasing the effectiveness of disaster mitigation, and reducing future loss of life and property.     

Debris flow hazard assessment with numerical simulation

Debris flow disasters are usually accompanied by serious loss of lives and properties. However, debris flows are also part of earth’s natural phenomenon, and so what is the reasonable budget to be spent on mitigation measures becomes an important issue for the budget allocation processes. This article utilizes economic concepts to propose a reasonable estimation of the hazard damage and the cost of proposed mitigation measures. The proposed method is composed of four steps, namely, delineating the area of the disaster with different return periods, itemizing the land use within those areas, calculating the hazard loss using official values, and computing the expected probable maximum loss with a probability distribution. The comparison between the assessment of hazard and the economic gains of any proposed mitigation measures can be used as a reference for future decision-making process.     

Integrated model for earthquake risk assessment using neural network and analytic hierarchy process:Aceh province,Indonesia

Catastrophic natural hazards,such as earthquake,pose serious threats to properties and human lives in urban areas.Therefore,earthquake risk assessment(ERA)is indispensable in disaster management.ERA is an integration of the extent of probability and vulnerability of assets.This study develops an integrated model by using the artificial neural network–analytic hierarchy process(ANN–AHP)model for constructing the ERA map.The aim of the study is to quantify urban population risk that may be caused by impending earthquakes.The model is applied to the city of Banda Aceh in Indonesia,a seismically active zone of Aceh province frequently affected by devastating earthquakes.ANN is used for probability mapping,whereas AHP is used to assess urban vulnerability after the hazard map is created with the aid of earthquake intensity variation thematic layering.The risk map is subsequently created by combining the probability,hazard,and vulnerability maps.Then,the risk levels of various zones are obtained.The validation process reveals that the proposed model can map the earthquake probability based on historical events with an accuracy of 84%.Furthermore,results show that the central and southeastern regions of the city have moderate to very high risk classifications,whereas the other parts of the city fall under low to very low earthquake risk classifications.The findings of this research are useful for government agencies and decision makers,particularly in estimating risk dimensions in urban areas and for the future studies to project the preparedness strategies for Banda Aceh.     

Neural network modeling for regional hazard assessment of debris flow in Lake Qionghai Watershed, China

This paper presents a neural network (NN) based model to assess the regional hazard degree of debris flows in Lake Qionghai Watershed, China. The NN model was used as an alternative for the more conventional linear model MFCAM (multi-factor composite assessment model) in order to effectively handle the nonlinearity and uncertainty inherent in the debris flow hazard analysis. The NN model was configured using a three layer structure with eight input nodes and one output node, and the number of nodes in the hidden layer was determined through an iterative process of varying the number of nodes in the hidden layer until an optimal performance was achieved. The eight variables used to represent the eight input nodes include density of debris flow gully, degree of weathering of rocks, active fault density, area percentage of slope land greater than 25° of the total land (APL25), frequency of flooding hazards, average covariance of monthly precipitation by 10 years (ACMP10), average days with rainfall >25 mm by 10 years (25D10Y), and percentage of cultivated land with slope land greater than 25° of the total cultivated land (PCL25). The output node represents the hazard-degree ranks (HDR). The model was trained with the 35 sets of data obtained from previous researches reported in literatures, and an explicit uncertainty analysis was undertaken to address the uncertainty in model training and prediction. Before the NN model is extrapolated to Lake Qionghai Watershed, a validation case, different from the above data, is conducted. In addition, the performances of the NN model and the MFCAM were compared. The NN model predicted that the HDRs of the five sub-watersheds in the Lake Qionghai Watershed were IV, IV, III, III, and IV–V, indicating that the study area covers normal hazard and severe hazard areas. Based on the NN model results, debris flow management and economic development strategies in the study are proposed for each sub-watershed.     

Spatial analysis and hazard assessment of mercury in soil around the coal-fired power plant: a case study from the city of Baoji, China

Based on systematic sampling of soil around the coal-fired power plant (CFPP), the content of Hg was determined, using atomic fluorescence spectrometry. The result shows that the content of Hg in soil is different horizontally and vertically, ranges from 0.137 to 2.105 mg/kg (the average value is 0.606 mg/kg) and is more than the average content of Hg in Shaanxi, Chinese and world soil. In this study, spatial distribution and hazard assessment of mercury in soils around a CFPP were investigated using statistics, geostatistics and geographic information system (GIS) techniques. Ordinary kriging was carried out to map the spatial patterns of mercury and disjunctive kriging was used to quantify the probability of the Hg concentration higher than the threshold. The maps show that the spatial variability of the Hg concentration in soils was apparent. These results of this study could provide valuable information for risk assessment of environmental Hg pollution and decision support. An erratum to this article can be found at     

基于数值模拟的群发性泥石流危险性评价

岷县是甘肃南部泥石流频发地区。岷县泥石流多分布于洮河干支流两岸,为群发性泥石流。为了研究群发性泥石流的运动及堆积特征,选取了甘肃岷县麻路河流域为研究区域,以流域内2012年“5·10”暴发泥石流造成重大损失的6条泥石流沟作为整体研究对象,并考虑主河对泥石流堆积物的冲刷携带,运用FLO-2D模拟降雨前主河流动情况及不同降雨频率条件下主河及泥石流的流动情况。根据野外调查结果对比2%降雨频率条件下泥石流模拟结果,验证模型的可靠性。基于模拟结果用ArcGIS进行危险性评价,识别流域内高危险泥石流沟并划定高危险居民区,统计受冲击范围,为泥石流防治和预警工作提供科学依据。     

Seismic risk assessment of buildings in urban areas: a case study for Denizli,Turkey

This study aims to carry out a seismic risk assessment for a typical mid-size city based on building inventory from a field study. Contributions were made to existing loss estimation methods for buildings. In particular, a procedure was introduced to estimate the seismic quality of buildings using a scoring scheme for the effective parameters in seismic behavior. Denizli, a typical mid-size city in Turkey, was used as a case study. The building inventory was conducted by trained observers in a selected region of Denizli that had the potential to be damaged from expected future earthquakes according to geological and geotechnical studies. Parameters that are known to have some effect on the seismic performance of the buildings during past earthquakes were collected during the inventory studies. The inventory includes data of about 3,466 buildings on 4,226 parcels. The evaluation of inventory data provided information about the distribution of building stock according to structural system, construction year, and vertical and plan irregularities. The inventory data and the proposed procedure were used to assess the building damage, and to determine casualty and shelter needs during the M6.3 and 7.0 scenario earthquakes, representing the most probable and maximum earthquakes in Denizli, respectively. The damage assessment and loss studies showed that significant casualties and economic losses can be expected in future earthquakes. Seismic risk assessment of reinforced concrete buildings also revealed the priorities among building groups. The vulnerability in decreasing order is: (1) buildings with 6 or more stories, (2) pre-1975 constructed buildings, and (3) buildings with 3–5 stories. The future studies for evaluating and reducing seismic risk for buildings should follow this priority order. All data of inventory, damage, and loss estimates were assembled in a Geographical Information System (GIS) database.     

Parametrical landslide modeling for the hydrogeological susceptibility assessment: from the Crati Valley to the Cavallerizzo landslide (Southern Italy)

The article deals with a tool for landslides susceptibility assessment as a function of the hydrogeological setting at different scales. The study has been applied to a test area located in Southern Italy. First, a 3D groundwater flow model was implemented for a large-scale area. The simulation of several groundwater conditions compared with the landslide activity map allows drawing a hydrogeological susceptibility map. Then, a slope scale analysis was carried out for the Cavallerizzo landslide. For this purpose, a 2D groundwater parametrical modeling was coupled with a slope stability analysis; the simulation was carried out by changing the values of the main hydrogeological parameters (recharge, groundwater supply level, etc.). The results enabled to connect the slope instability to some hydrogeological characteristics that are easy to survey and to monitor (e.g., rainfall, piezometrical level, and spring discharge), pointing out the hazard thresholds with regards to different triggering phenomena.     

The use of digital technologies for landslide disaster risk research and disaster risk management: progress and prospects

Landslide is a serious natural disaster next only to earthquake and flood, which will cause a great threat to people’s lives and property safety. The traditional research of landslide disaster based on experience-driven or statistical model and its assessment results are subjective , difficult to quantify, and no pertinence. As a new research method for landslide susceptibility assessment, machine learning can greatly improve the landslide susceptibility model’s accuracy by constructing statistical models. Taking Western Henan for example, the study selected 16 landslide influencing factors such as topography, geological environment, hydrological conditions, and human activities, and 11 landslide factors with the most significant influence on the landslide were selected by the recursive feature elimination (RFE) method. Five machine learning methods [Support Vector Machines (SVM), Logistic Regression (LR), Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Linear Discriminant Analysis (LDA)] were used to construct the spatial distribution model of landslide susceptibility. The models were evaluated by the receiver operating characteristic curve and statistical index. After analysis and comparison, the XGBoost model (AUC 0.8759) performed the best and was suitable for dealing with regression problems. The model had a high adaptability to landslide data. According to the landslide susceptibility map of the five models, the overall distribution can be observed. The extremely high and high susceptibility areas are distributed in the Funiu Mountain range in the southwest, the Xiaoshan Mountain range in the west, and the Yellow River Basin in the north. These areas have large terrain fluctuations, complicated geological structural environments and frequent human engineering activities. The extremely high and highly prone areas were 12043.3 km² and 3087.45 km², accounting for 47.61% and 12.20% of the total area of the study area, respectively. Our study reflects the distribution of landslide susceptibility in western Henan Province, which provides a scientific basis for regional disaster warning, prediction, and resource protection. The study has important practical significance for subsequent landslide disaster management.