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Assessment of Landslide Hazard in Jiangxi Using Geo-information Technology

FRONTIERS IN EARTH SCIENCE 2021年 9卷

作者： Ou, Penghui Wu, Weicheng Qin, Yaozu Zhou, Xiaoting Huangfu, Wenchao Zhang, Yang Xie, Lifeng Huang, Xiaolan Fu, Xiao Li, Jie Jiang, Jingheng Zhang, Ming Liu, Yixuan Peng, Shanling Shao, Chongjian Bai, Yonghui Zhang, Xiaofeng Liu, Xiangtong Liu, Wenheng East China Univ Technol Key Lab Digital Lands & Resources Nanchang Jiangxi Peoples R China East China Univ Technol Fac Earth Sci Nanchang Jiangxi Peoples R China East China Univ Technol Sch Geophys & Measurement Control Technol Nanchang Jiangxi Peoples R China East China Univ Technol Fac Geomat Nanchang Jiangxi Peoples R China

Landslides constitute a severe environmental problem in Jiangxi, China. This research was aimed at conducting landslide hazard assessment to provide technical support for disaster reduction and prevention action in the province. Fourteen geo-environmental factors, e.g., slope, elevation, road, river, fault, lithology, rainfall, and land cover types, were selected for this study. A test was made in two cases: (1) only based on the main linear features, e.g., main rivers and roads, and (2) with detailed complete linear features including all levels of roads and rivers. After buffering of the linear features, an information value (IV) analysis was applied to quantify the distribution of the observed landslides for each subset of the 14 factors. The results were inputted into the binary logistic regression model (LRM) for landslide risk modeling, taking the known landslide points as a training set (70% of the total 9,525 points). The calculated probability of a landslide was further classified into five grades with an interval of 0.2 for hazard mapping: very high (3.70%), high (4.05%), moderate (18.72%), low (27.17%), and stable zones (46.36%). The accuracy was evaluated by AUC [the area under the receiver operating characteristic (ROC) curve] vs. the validation set (30%, the remaining landslides). The final results show that with increasing the completeness of the linear features, the modeling reliability also significantly increased. We hence concluded that the tested methodology is capable of achieving the landslide hazard prediction at regional scale, and the results may provide technical support for geohazard reduction and prevention in the studied province.

关键词： landslide information value analysis logistic regression model risk zoning Jiangxi province

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A diagnostic of influential cases based on the information complexity criteria in generalized linear mixed models

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COMMUNICATIONS IN STATISTICS-THEORY AND METHODS 2016年第13期45卷 3751-3760页

作者： Shang, Junfeng Bowling Green State Univ Dept Math & Stat 450 Math Sci Bldg Bowling Green OH 43403 USA

modeling diagnostics assess models by means of a variety of criteria. Each criterion typically performs its evaluation upon a specific inferential objective. For instance, the well-known DFBETAS in linear regression models are a modeling diagnostic which is applied to discover the influential cases in fitting a model. To facilitate the evaluation of generalized linear mixed models (GLMM), we develop a diagnostic for detecting influential cases based on the information complexity (ICOMP) criteria for detecting influential cases which substantially affect the model selection criterion ICOMP. In a given model, the diagnostic compares the ICOMP criterion between the full data set and a case-deleted data set. The computational formula of the ICOMP criterion is evaluated using the Fisher information matrix. A simulation study is accomplished and a real data set of cancer cells is analyzed using the logistic linear mixed model for illustrating the effectiveness of the proposed diagnostic in detecting the influential cases.

关键词： Case-deletion Fisher information matrix GLMM ICOMP logistic regression model

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Evaluating Parallel logistic regression models

Evaluating Parallel Logistic Regression Models

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IEEE International Conference on Big Data

作者： Haoruo Peng Ding Liang Cyrus Choi HTC Research Center Beijing China

ISBN: (纸本)9781479912940

logistic regression (LR) has been widely used in applications of machine learning, thanks to its linear model. However, when the size of training data is very large, even such a linear model can consume excessive memory and computation time. To tackle both resource and computation scalability in a big-data setting, we evaluate and compare different approaches in distributed platform, parallel algorithm, and sublinear approximation. Our empirical study provides design guidelines for choosing the most effective combination for the performance requirement of a given application.

关键词： logistic regression model Parallel Computing Sublinear Method Big Data

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Comparison of low-temperature cracks intensity on pavements with high modulus asphalt concrete and conventional asphalt concrete bases

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CONSTRUCTION AND BUILDING MATERIALS 2017年 147卷 478-487页

作者： Rys, Dawid Judycki, Jozef Pszczola, Marek Jaczewski, Mariusz Mejlun, Lukasz Gdansk Univ Technol Fac Civil & Environm Engn Dept Highway Engn Gdansk Poland

High modulus asphalt concrete (HMAC) base courses provide very good resistance to rutting and fatigue but they can increase the risk of low-temperature cracking as compared with conventional asphalt concrete (AC). The article presents the comparison of these two road materials in terms of low-temperature cracking. The statistical method based on the ordered logistic regression model was used. The analysis was based on results of field investigations, that was carried out on 80 selected road sections being in normal service in Poland. The intensity of low-temperature cracking was an analysed parameter. The results of the analysis indicated evident effect of asphalt base type on intensity of low-temperature cracking. Besides the effect of mixture type, the method included influence of climatic condition and pavement age on low-temperature cracking. The essence of the analysis was to compare the probabilities of being of pavement in the group with a given cracks intensity. It was revealed that pavements with high modulus asphalt bases had 2.45 times higher odds of being in the group of cracked pavements than pavements with conventional asphalt concrete bases. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： High modulus asphalt concrete Low-temperature cracking logistic regression model Field investigations

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A modified Logit model for assessment and validation of debris-flow susceptibility

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BULLETIN OF ENGINEERING GEOLOGY AND THE ENVIRONMENT 2019年第6期78卷 4421-4438页

作者： Wu, Saier Chen, Jian Zhou, Wendy Iqbal, Javed Yao, Leihua China Univ Geosci Sch Engn & Technol Beijing 100083 Peoples R China Colorado Sch Mines Dept Geol & Geol Engn Golden CO 80401 USA Abbottabad Univ Sci & Technol Dept Earth Sci Abbottabad Pakistan

The accuracy of a debris-flow susceptibility assessment model can not be achieved to an acceptable level without rigorously selecting/processing model input parameters and reliably validating the results with ground truth. In this study, a modified logistic regression model was applied to examine the debris-flow susceptibility in the semi-arid mountainous areas of the southeast Tibetan Plateau, taking the case of the Benzilan-Changbo segment of the Jinsha River. The logistic regression model validation was supported by a field investigation and interpreting aerial photographs. The frequency ratio method was adapted for the sensitivity analysis of ten predisposing factors, including a factor of human activities that has never been considered in previous studies for the selected study area. The model with the highest prediction accuracy (83%) was obtained by comparing 55 possible combinations of the ten factors evaluated using a receiver operating characteristic curve technique. Composite analysis and random sample testing were also conducted to verify the reliability of the susceptibility assessment. The prediction results showed that the actual debris-flow area accounted for 92% of the total predicted debris-flow area. In summary, this study reveals that: (1) the highest susceptibility area was mainly distributed in the northeast and middle riparian zones, partially scattered in the southwest. Human activity was found to be closely related to the probability of debris flow occurrences;(2) under the two situations of independent and index combination participation evaluation, the performances of the same related factors showed largely different effects on the assessment results, which reflected the interrelation and interaction of the factors causing the debris flow;(3) the accuracy of the modified logistic regression model increased by 15%, when compared with the index entropy model in a previous study, and the reasons for this were discussed in the current study. T

关键词： Semi-arid mountainous areas Debris flow logistic regression model Susceptibility Jinsha River

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Quality Estimation of Resistance Spot Weld based on logistic regression Analysis of Welding Power Signal

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INTERNATIONAL JOURNAL OF PRECISION ENGINEERING AND MANUFACTURING 2015年第13期16卷 2655-2663页

作者： Yu, Jiyoung Hyundai Steel Co Tech Res Ctr Dangjin Si 31719 Chungcheongnam South Korea

Resistance spot welds have a major effect on the stiffness and crashworthiness of a car body. Therefore, it is necessary to implement a monitoring method which predicts the weld quality of the resistance spot welding process, in order to ensure robust welds. In this study, the features and advantages of welding power signals were analyzed and a predictive model for weld quality was developed using a logistic regression model. The significance of variables, including;maximum value of welding power, electrode force, zinc coating, power drop, and so on, was examined and the multinomial logistic regression model was estimated using the significant variables. The accuracy of the predictive model for weld quality was estimated to be 96.4%. Also, the binary logistic model was estimated and a classification cutoff value was adjusted to control the prediction accuracy of the model.

关键词： Dynamic resistance logistic regression model Resistance spot welding Weld quality estimation Welding power

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Does an upper limit to river water temperature apply in all places?

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HYDROLOGICAL PROCESSES 2017年第21期31卷 3729-3739页

作者： Shaw, Stephen B. SUNY Coll Environm Sci & Forestry Dept Environm Resources Engn Syracuse NY 13210 USA

There remains continued use of non-linear, logistic regression models for predicting water temperature from air temperature. A dominant feature of these non-linear models is an upper bound on river water temperature. This upper bound is often attributed to a large increase in evaporative cooling at high air temperatures, but the exact conditions under which such an increase may occur have not been thoroughly explored. To better understand the appropriateness of the non-linear model for predicting river water temperatures, it is essential to understand the physical basis for the upper bound and when it should and should not be included in the statistical model. This paper applies and validates an energy balance model against 8 river systems spread across different climate regions of the United States. The energy balance model is then used to develop a diagram relating vapour pressure deficit and air temperature to water temperature. With knowledge of present or future vapour pressure deficit (difference between saturation and actual vapour content in the atmosphere) conditions in a given climate, the diagram can be used to predict the likelihood of an upper bound in the air-water temperature relationship. This investigation offers a fundamental physical explanation of the most appropriate form of statistical models that should be used for predicting future water temperature from air temperature in different geographic regions with different climate conditions. In general, climatic regions that have only a slight increase in vapour pressure deficit with increasing air temperature (typically humid regions) would not be expected to have an upper bound. Conversely, climatic regions in which vapour pressure deficit sharply increases with increasing air temperature (typically arid regions) would be expected to have an upper bound.

关键词： climate change logistic regression model stream water temperature vapour pressure deficit

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Habitat suitability assessment for the endangered Nilgiri Laughingthrush: A multiple logistic regression approach

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CURRENT SCIENCE 2008年第11期94卷 1487-1494页

作者： Zarri, Ashfaq A. Rahmani, Asad R. Singh, A. Kushwaha, S. P. S. Univ Florida Dept Wildlife Ecol & Conservat Gainesville FL USA Govt India Indian Inst Remote Sensing Dept Space Dehra Dun 248001 Uttar Pradesh India Bombay Nat Hist Soc Mumbai 400023 Maharashtra India

Application of remote sensing and Geographic Information System (GIS) tools has assumed an increasingly important role in conservation biology and wildlife management by providing means for modelling potential distributions of species and their habitats, unlike the conventional ground surveys. We present here a predictive model of habitat suitability for the Nilgiri Laughingthrush, Garrulax cachinnans based on a synergistic use of field surveys and digitally processed satellite imagery combined with features mapped using GIS data layers. Collateral data were created in a GIS framework based on ground surveys comprising layers such as land-use, measures of proximity to likely features of disturbance and a digital terrain model. Multiple binomial logistic regression approach was used for modelling, and the model performance was assessed by the area under the receiver operating characteristics (ROC) curve. About 320 km(2), 25.12% of the area of the Nilgiris considered for modelling was predicted to be suitable for the Nilgiri Laughingthrush. The area under the ROC curve was found to be 0.984 +/- 0.003 (R-2: 0.93 at P < 0.0001), implying, a highly effective model. The assessed suitable habitat was highly fragmented and comprised of 1352 patches (natural as well as man-made) distributed all over the study area. The smallest suitable patch identified by the model was 400 m(2) and the largest patch 17.65 km(2). Also, ca. 92% of all patches were smaller than 0.5 km(2). We presume that some suitable habitat patches may be unoccupied due to strong fidelity of the species to shola (montane wet temperate forest) patches, low colonization rates, or large inter-fragment distances. Also, larger fragments might serve as source or 'exporters' of surplus individuals to maintain sink populations throughout the rest of the range. We discuss the implication of habitat fragmentation and narrow geographical range and anthropogenic pressure for the conservation of the Nilgiri Laughingthrus

关键词： field surveys habitat suitability logistic regression model Nilgiri Laughingthrush

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Development of injury prediction models for advanced automatic collision notification based on Japanese accident data

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INTERNATIONAL JOURNAL OF CRASHWORTHINESS 2016年第2期21卷 112-119页

作者： Yoshida, S. Hasegawa, T. Tominaga, S. Nishimoto, T. Honda Res & Dev Co Ltd Automobile R&D Ctr Utsunomiya Tochigi Japan Nihon Univ Coll Sci & Technol Tokyo 101 Japan Nihon Univ Coll Engn Fukushima Japan

In this paper, injury prediction models for estimating serious injury risk of occupants were developed based on accident data available in the Japanese statistical DB. Four types of model by crash direction (frontal crash model, near-side crash model, far-side crash model, rear-end crash model) were developed. These models were developed by using a logistic regression modelling technique based on data from Japanese ITARDA (Institute for Traffic Accident Research and Data Analysis) police-reported statistics, a large database for the last decade. Risk factors of the model are delta-V, belt use, multiple impact crash and occupant's age. Serious injury risk for four crash directions was estimated by the model. A comparison has been done between estimated serious injury risk and actual injury of Japanese ITARDA in-depth accident data (micro-data). The results show that the injury prediction model has a possibility for predicting injury risk based on onboard data and its application for post-crash safety.

关键词： Advanced automatic collision notification logistic regression model injury prediction

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Exploring the factors influencing ecological land change for China's Beijing-Tianjin-Hebei Region using big data

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JOURNAL OF CLEANER PRODUCTION 2017年第Part2期142卷 677-687页

作者： Xie, Hualin He, Yafen Xie, Xue Jiangxi Univ Finance & Econ Coinnovat Ctr Inst Construct Jiangxi Ecocivilizat Nanchang 330032 Jiangxi Peoples R China Jiangxi Univ Finance & Econ Res Ctr Land Management Nanchang 330032 Jiangxi Peoples R China

With the advent of big data, such data regarding the harmonious relationship of "people-land-time" have also gradually entered into the fields of natural resource management. Ecological land is one of the important natural resources and is fundamental to maintaining ecological security. Ecological land change can lead to a series of eco-environmental problems, including water shortages, soil erosion, increased drought intensity, ecosystem damage, and biodiversity loss. Based on relevant sets of big data, including spatial land data, soil data, DEM, climatic data, and socio-economic data, this study explores the factors influencing ecological land change during the period of 2000-2005 in China's Beijing-Tianjin-Hebei Region. The results show that the factors influencing different types of ecological land change have substantial differences. For forest land coverage change, Slope type, Soil organic matter (SOM) content, Farmer's population percentage, and Landform type are the most important independent variables. However, for grassland change, altitude, distance to the primary road and GDP per capita are the most important spatial determinants. Regarding the wetland change, farmer's population percentage, GDP per capita and altitude are the most important factors influencing wetland changes. This study indicates that natural and social-economic factors can affect ecological land change in China's Beijing-Tianjin-Hebei Region. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： Ecological land Big data logistic regression model Land resource management

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