Influences of Risk Perception and Sense of Place on Landslide Disaster Preparedness in Southwestern China

Xu, Dingde; Li, Peng; Wang, Xuxi

doi:10.1007/s13753-018-0170-0

Cited by 130 publications

(108 citation statements)

References 53 publications

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“…Landslide is a kind of extremely dangerous geological disasters which has a significant influence on personal safety as well as property security and geological environment [131,132]. In this paper, based on LR model, three bivariate statistical models, namely EBF, WoE, and IoE models, were introduced to carry out the landslide susceptibility mapping in Muchuan County.…”

Section: Discussionmentioning

confidence: 99%

Landslide Susceptibility Mapping for the Muchuan County (China): A Comparison Between Bivariate Statistical Models (WoE, EBF, and IoE) and Their Ensembles with Logistic Regression

Wang

2019

Symmetry

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The main purpose of this study is to apply three bivariate statistical models, namely weight of evidence (WoE), evidence belief function (EBF) and index of entropy (IoE), and their ensembles with logistic regression (LR) for landslide susceptibility mapping in Muchuan County, China. First, a landslide inventory map contained 279 landslides was obtained through the field investigation and interpretation of aerial photographs. Next, the landslides were randomly divided into two parts for training and validation with the ratio of 70/30. In addition, according to the regional geological environment characteristics, twelve landslide conditioning factors were selected, including altitude, plan curvature, profile curvature, slope angle, distance to roads, distance to rivers, topographic wetness index (TWI), normalized different vegetation index (NDVI), land use, soil, and lithology. Subsequently, the landslide susceptibility mapping was carried out by the above models. Eventually, the accuracy of this research was validated by the area under the receiver operating characteristic (ROC) curve and the results indicated that the landslide susceptibility map produced by EBF-LR model has the highest accuracy (0.826), followed by IoE-LR model (0.825), WoE-LR model (0.792), EBF model (0.791), IoE model (0.778), and WoE model (0.753). The results of this study can provide references of landslide prevention and land use planning for local government.Symmetry 2019, 11, 762 2 of 24years, machine learning method has been gradually applied in landslide susceptibility mapping researches, such as artificial neural network (ANN) [17][18][19], support vector machine (SVM) [20][21][22], logistic model tree (LMT) [23,24], rotation forest (RF) [25,26], classification and regression tree (CART) [27,28], adaptive neuro-fuzzy inference systems (ANFIS) [29,30], and genetic algorithm (GA) [31,32]. Furthermore, statistical approach is another widely-used model which can also be divided into two types: bivariate and multivariate. In statistical approaches, the weight of each class of every factor was calculated by overlying the landslide inventory map and landslide conditioning factors map [33,34]. The frequently-used statistical approaches are frequency ratio (FR) [35][36][37], logistic regression (LR), evidential belief function (EBF) [38,39], weights of evidence (WoE) [40,41], certainty factor (CF) [42,43], and information value (IV) [44,45].In this paper, three bivariate statistical methods, namely weight of evidence (WoE), evidence belief function (EBF), index of entropy (IoE), coupled with logistic regression (LR) were introduced to carry out the landslide susceptibility mapping in Muchuan County, which lies in the southeast of Sichuan Province, China. All the approaches were first applied in Muchuan County and the main purpose of this article is to obtain the landslide susceptibility maps through the above models. The main difference between this study and literatures referenced above is to achieve accurate landslide susceptibility maps for...

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Section: Discussionmentioning

confidence: 99%

Landslide Susceptibility Mapping for the Muchuan County (China): A Comparison Between Bivariate Statistical Models (WoE, EBF, and IoE) and Their Ensembles with Logistic Regression

Wang

2019

Symmetry

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“…Such subjective and intrinsic motivating factors as the initiatives taken by farmers to face drought, including investing in irrigation [32], communicating with other farmers [33], and buying crop insurance [34], also affect the ability to cope with drought. These initiatives often depend on such subjective aspects as the farmer's experience of disasters, views on the mechanism of responding to disasters, and awareness of future risks from disasters [35,36]. People's subjective willingness affects their response to disasters, and thus the level of risk governance [35,37].…”

Section: Economic Factorsmentioning

confidence: 99%

Quantifying Farmers’ Initiatives and Capacity to Cope with Drought: A Case Study of Xinghe County in Semi-Arid China

Guo

Yan-rui

et al. 2019

Sustainability

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Farmers living in a state of poverty in semi-arid areas are the main victims of drought. They suffer most from drought, which makes it particularly important to assess their capacity to cope with it. The present study developed a new approach for such assessment. Famers’ resources and initiatives to deal with drought were evaluated, and appropriate coping strategies are proposed by analyzing the differences between the coping capacities of two categories of farmers; tenants (with large holdings) and smallholders. Tenants’ capacity to cope with drought was 1.7 times that of smallholders. Also, tenants showed greater initiative during droughts, whereas smallholders made greater efforts in preparing for droughts. Farmers’ resources were positively correlated to the degree of their initiative (correlation coefficient = 0.49). Farmers showed greater initiative when they had larger holdings and were less dependent on agricultural income. The state should give more financial support to subsidy-dependent farmers and encourage smallholders—whose major income comes from crops—to augment their income through livestock husbandry. Such a policy would prove more conducive to increasing the capacity of not only local farmers but also of the entire region to cope with droughts and to promote sustainable development of local agriculture.

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“…Evacuation and relocation are effective means to reduce the impact of disasters on residents, and the mechanisms driving these behaviors are a focus of academic research. The existing studies have examined several types of indicators that may affect residents' disaster preparedness (including the willingness for evacuation and relocation as well as the actual behaviors of evacuation and relocation), including individual and family socio-economic characteristics of residents (e.g., [18,30,31]), disaster information sources (e.g., [19,22,32]), disaster experiences (e.g., [24,33]), and disaster risk perception (e.g., [21,[34][35][36][37][38][39][40][41][42]). Among these indicators, sense of place is a factor that has been rarely considered in the existing research [24,43,44].…”

Section: Introductionmentioning

confidence: 99%

Disaster Risk Perception, Sense of Pace, Evacuation Willingness, and Relocation Willingness of Rural Households in Earthquake-Stricken Areas: Evidence from Sichuan Province, China

Chen

Deng

et al. 2020

IJERPH

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Based on survey data from 327 rural households in the areas affected by the Wenchuan Earthquake and Lushan Earthquake in Sichuan Province, this study systematically analyzed disaster risk perception, sense of place, evacuation willingness, and relocation willingness among residents in these earthquake-stricken areas. Further, this study constructed an ordinal logistic regression analysis to probe the correlations between residents’ disaster risk perception or sense of place and evacuation willingness and relocation willingness, respectively. The results showed that (1) faced with the threat of earthquake disasters, residents have a strong willingness to evacuate and relocate. Specifically, 93% and 78% of the residents in the Wenchuan Earthquake and Lushan Earthquake areas were willing to evacuate and relocate, respectively, whereas 4% and 17% of the residents were unwilling to evacuate and relocate, respectively. (2) Place dependence and the severity of disaster occurrence were significantly positively correlated with residents’ evacuation willingness, while the interaction term between place dependence and the severity of disaster occurrence was negatively related to residents’ evacuation willingness. Specifically, when everything else remains constant, every one-unit increase in place dependence and severity corresponds to increases in the odds of willingness to evacuate by factors of 0.042 and 0.051, respectively; every one-unit increase in place dependence × severity corresponds to a decrease in the odds of willingness to evacuation by a factor of 0.004. (3) Place identity was significantly negatively correlated with residents’ relocation willingness, while place dependence and severity of disaster occurrence were positively related to residents’ relocation willingness. The interaction term between place dependence and the severity of disaster occurrence as well as the interaction term between place identity and severity of disaster occurrence were significantly negatively correlated with residents’ relocation willingness. Specifically, every one-unit increase in place identity corresponds to a decrease in the odds of willingness to relocate by a factor of 0.034, while every one-unit increase in place dependence and severity corresponds to increases in the odds of willingness to relocate by factors of 0.041 and 0.028, respectively, and every one-unit increase in place dependence × severity and place identity × severity corresponds to decreases in the odds of willingness to relocate by factors of 0.003 and 0.003, respectively.

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Influences of Risk Perception and Sense of Place on Landslide Disaster Preparedness in Southwestern China

Cited by 130 publications

References 53 publications

Landslide Susceptibility Mapping for the Muchuan County (China): A Comparison Between Bivariate Statistical Models (WoE, EBF, and IoE) and Their Ensembles with Logistic Regression

Landslide Susceptibility Mapping for the Muchuan County (China): A Comparison Between Bivariate Statistical Models (WoE, EBF, and IoE) and Their Ensembles with Logistic Regression

Quantifying Farmers’ Initiatives and Capacity to Cope with Drought: A Case Study of Xinghe County in Semi-Arid China

Disaster Risk Perception, Sense of Pace, Evacuation Willingness, and Relocation Willingness of Rural Households in Earthquake-Stricken Areas: Evidence from Sichuan Province, China

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