Maximum Entropy-Based Model of High-Threat Landslide Disaster Distribution in Zhaoqing, China

Yuan, Shaoxiong; Huang, Guangqing; Xiong, Haixian; Gong, Quanmei; Wang, Jun

doi:10.2991/jrarc.2017.7.3.2

Cited by 5 publications

(5 citation statements)

References 14 publications

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“…Yuan et al constructed a maximum entropy model with 16 environmental factors, and then they considered NDVI, rainfall variance and elevation as the main environmental factors affecting landslide hazard ( 25 ). Sun et al constructed ENM of severe fever with thrombocytopenia syndrome (SFTS) using MaxEnt, and they found yearly average temperature, altitude, yearly average relative humidity and yearly accumulated precipitation accounted for 94.1% contribution for ENM ( 17 ).…”

Section: Discussionmentioning

confidence: 99%

Factors associated with hemorrhagic fever with renal syndrome based maximum entropy model in Zhejiang Province, China

Zhang

Zhang²,

Liu

et al. 2022

Front. Med.

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BackgroundHemorrhagic fever with renal syndrome (HFRS) is a serious public health problem in China. The geographic distribution has went throughout China, among which Zhejiang Province is an important epidemic area. Since 1963, more than 110,000 cases have been reported.MethodsWe collected the meteorological factors and socioeconomic indicators of Zhejiang Province, and constructed the HFRS ecological niche model of Zhejiang Province based on the algorithm of maximum entropy.ResultsModel AUC from 2009 to 2018, is 0.806–0.901. The high incidence of epidemics in Zhejiang Province is mainly concentrated in the eastern, western and central regions of Zhejiang Province. The contribution of digital elevation model ranged from 2009 to 2018 from 4.22 to 26.0%. The contribution of average temperature ranges from 6.26 to 19.65%, Gross Domestic Product contribution from 7.53 to 21.25%, and average land surface temperature contribution with the highest being 16.73% in 2011. In addition, the average contribution of DMSP/OLS, 20-8 precipitation and 8-20 precipitation were all in the range of 9%. All-day precipitation increases with the increase of rainfall, and the effect curve peaks at 1,250 mm, then decreases rapidly, and a small peak appears again at 1,500 mm. Average temperature response curve shows an inverted v-shape, where the incidence peaks at 17.8°C. The response curve of HFRS for GDP and DMSP/OLS shows a positive correlation.ConclusionThe incidence of HFRS in Zhejiang Province peaked in areas where the average temperature was 17.8°C, which reminds that in the areas where temperature is suitable, personal protection should be taken when going out as to avoid contact with rodents. The impact of GDP and DMSP/OLS on HFRS is positively correlated. Most cities have good medical conditions, but we should consider whether there are under-diagnosed cases in economically underdeveloped areas.

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Section: Discussionmentioning

confidence: 99%

Factors associated with hemorrhagic fever with renal syndrome based maximum entropy model in Zhejiang Province, China

Zhang

Zhang²,

Liu

et al. 2022

Front. Med.

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“…The cause, nature, and handling of mapping errors (uncertainties) has been the subject of extensive research (Maffini et al, 1989;Openshaw, 1989;Hunter and Goodchild, 1996;Hunter, 1999;Wechsler, 1999;Ardizzone et al, 2002;Shi, 2010;Zufle et al, 2017). Uncertainty and uncertainty handling is context dependent.…”

Section: Discussionmentioning

confidence: 99%

“…It means we do not know and/or we do not have substantiating data sources or ability to conduct field surveys, particularly at a continental scale. MaxEnt is a widely used technique in biological species distribution modeling with recent and growing interest in its use for landslide susceptibility modelling due to its predictive success compared with other methodologies in "presence" only scenarios (Convertino et al, 2013;Park, 2014;Lombardo et al, 2016;Kornejady et al, 2017;Yuan et al, 2017;Gál et al, 2018). The MaxEnt model renders information for those debris flow predisposing factors that provide the greatest contribution to the susceptibility analysis.…”

Section: Methodsmentioning

confidence: 99%

Impact of spatial data uncertainty in debris flow susceptibility analysis

Kurilla

Fubelli

2021

Preprint

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Abstract. In a study of debris flow susceptibility on the European continent, an analysis of the impact between known location and a location accuracy offset for 99 debris flows, demonstrates the impact of uncertainty in defining appropriate predisposing factors, and consequent analysis for areas of susceptibility. The dominant predisposing environmental factors, as determined through Maximum Entropy modeling, are presented, and analyzed with respect to the values found at debris flow event points versus a buffered distance of locational uncertainty around each point. Five Maximum Entropy susceptibility models are developed utilizing the original debris flow inventory of points, randomly generated points, and two models utilizing a subset of points with an uncertainty of 5 km, 1 km, and a model utilizing only points with a known location of “exact”. The AUCs are 0.891, 0.893, 0.896, 0.921, and 0.93, respectively. The “exact” model, with the highest AUC, is ignored in final analyses due to the small number of points, and localized distribution, and hence susceptibility results likely non-representational of the continent. Each model is analyzed with respect to the AUC, highest contributing factors, factor classes, susceptibility impact, and comparisons of the susceptibility distributions and susceptibility value differences. Based on model comparisons, geographic extent and context of this study, the models utilizing points with a location uncertainty of less than or equal to 5 km best represent debris flow susceptibility of the continent of Europe. A novel representation of the uncertainty is expressed, and included in a final susceptibility map, as an overlay of standard deviation and mean of susceptibility values for the two best models, providing additional insight for subsequent action.

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“…The MaxEnt model output is a maximum likelihood estimate of relative probability of presence. MaxEnt is a widely used technique in biological species distribution modeling with recent and growing interest in its use for landslide susceptibility modeling due to its predictive success compared with other methodologies in "presence" only scenarios [27,[36][37][38][39][40]. The MaxEnt model renders information for those debris flow predisposing factors that provide the greatest contribution to the susceptibility analysis.…”

Section: Methodsmentioning

confidence: 99%

Impact and a Novel Representation of Spatial Data Uncertainty in Debris Flow Susceptibility Analysis

Kurilla

Fubelli

2022

Applied Sciences

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In a study of debris flow susceptibility on the European continent, an analysis of the impact between known location and a location accuracy offset for 99 debris flows demonstrates the impact of uncertainty in defining appropriate predisposing factors and consequent analysis for areas of susceptibility. The dominant predisposing environmental factors, as determined through Maximum Entropy modeling, are presented and analyzed with respect to the values found at debris flow event points versus a buffered distance of locational uncertainty around each point. Maximum Entropy susceptibility models are developed utilizing the original debris flow inventory of points, randomly generated points, and two models utilizing a subset of points with an uncertainty of 5 km, 1 km, and a model utilizing only points with a known location of “exact”. The AUCs are 0.891, 0.893, 0.896, 0.921, and 0.93, respectively. The “exact” model, with the highest AUC, is ignored in final analyses due to the small number of points and localized distribution, and hence susceptibility results are likely non-representational of the continent. Each model is analyzed with respect to the AUC, highest contributing factors, factor classes, susceptibility impact, and comparisons of the susceptibility distributions and susceptibility value differences. Based on model comparisons, geographic extent, and the context of this study, the models utilizing points with a location uncertainty of less than or equal to 5 km best represent debris flow susceptibility for the continent of Europe. A novel representation of the uncertainty is expressed and included in a final susceptibility map, as an overlay of standard deviation and mean of susceptibility values for the two best models, providing additional insight for subsequent action.

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Maximum Entropy-Based Model of High-Threat Landslide Disaster Distribution in Zhaoqing, China

Cited by 5 publications

References 14 publications

Factors associated with hemorrhagic fever with renal syndrome based maximum entropy model in Zhejiang Province, China

Factors associated with hemorrhagic fever with renal syndrome based maximum entropy model in Zhejiang Province, China

Impact of spatial data uncertainty in debris flow susceptibility analysis

Impact and a Novel Representation of Spatial Data Uncertainty in Debris Flow Susceptibility Analysis

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