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Susceptibility assessment for rainfall-induced landslides using a revised logistic regression method

NATURAL HAZARDS 2021年第1期106卷 97-117页

作者： Xing, Xinfu Wu, Chenglong Li, Jinhui Li, Xueyou Zhang, Limin He, Rongjie Shenzhen Univ Town Harbin Inst Technol Shenzhen Dept Civil & Environm Engn Shenzhen 518055 Peoples R China Sun Yat Sen Univ Sch Civil Engn Guangzhou 510275 Guangdong Peoples R China Hong Kong Univ Sci & Technol Dept Civil & Environm Engn Hong Kong Peoples R China

Landslide susceptibility is the likelihood of a landslide occurring in an area. The logistic regression (LR) method is one of the most popular methods for landslide susceptibility assessment. For rainfall-induced landslides, yearly or monthly rainfall is commonly used to establish a landslide susceptibility model by the LR method. It is a static susceptibility model, which limits the application to predict future landslide probability under potential rainfall event. This study presents a revised logistic regression method to achieve dynamic landslide susceptibility prediction under cumulative daily rainfall. Five kinds of cumulative daily rainfall are used in the landslide susceptibility assessment. The latest landslide events are used to update the landslide susceptibility model. The receiver operation characteristic curve and area under curve are utilized to evaluate the prediction reliability. The landslide susceptibility assessment in Shenzhen is taken as an illustration of the proposed method. The result indicates the method is capable to achieve a high accuracy of 91.9% when the landslide susceptibility model is updated using seven extreme rainfall events in the past 10 years. This method provides an advance prediction of the potential geo-hazards for a large area using the future rainfall forecast.

关键词： Natural disaster Landslide susceptibility Rainfall-induced landslide logistic regression method

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An Outpatient Appointment Scheduling System Based on the logistic regression method

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JOURNAL OF INFORMATION & OPTIMIZATION SCIENCES 2015年第6期36卷 549-567页

作者： Huang, Jia-Yen Natl Chin Yi Univ Technol Dept Informat Management 57Sect 2Zhongshan Rd Taichung 41170 Taiwan

In the age of rising patients' self-awareness, reducing patient waiting time is one of the most important issues for hospitals. In Taiwan, the schedule of outpatient appointment time is arranged according to experience, and several possible factors influential to the prediction accuracy are neglected, which inducing patient's uncomfortable feeling. In this paper, sample data of a medical center located in central Taiwan is collected. Three methods for predicting the outpatient appointment time were conducted and compared, including the hospital's method, the moving average method, and the logistic regression method. The experimental results show that the appointment scheduling system based on our proposed algorithm can efficiently improve service quality by increasing prediction accuracy on the outpatient appointment time.

关键词： Waiting time logistic regression method Outpatient Appointment Scheduling System

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Standardisation of rates using logistic regression: a comparison with the direct method

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BMC HEALTH SERVICES RESEARCH 2008年第1期8卷 1-7页

作者： Roalfe, Andrea K. Holder, Roger L. Wilson, Sue Univ Birmingham Sch Hlth & Populat Sci Birmingham B15 2TT W Midlands England

Background: Standardisation of rates in health services research is generally undertaken using the direct and indirect arithmetic methods. These methods can produce unreliable estimates when the calculations are based on small numbers. regression based methods are available but are rarely applied in practice. This study demonstrates the advantages of using logistic regression to obtain smoothed standardised estimates of the prevalence of rare disease in the presence of covariates. methods: Step by step worked examples of the logistic and direct methods are presented utilising data from BETS, an observational study designed to estimate the prevalence of subclinical thyroid disease in the elderly. Rates calculated by the direct method were standardised by sex and age categories, whereas rates by the logistic method were standardised by sex and age as a continuous variable. Results: The two methods produce estimates of similar magnitude when standardising by age and sex. The standard errors produced by the logistic method were lower than the conventional direct method. Conclusion: regression based standardisation is a practical alternative to the direct method. It produces more reliable estimates than the direct or indirect method when the calculations are based on small numbers. It has greater flexibility in factor selection and allows standardisation by both continuous and categorical variables. It therefore allows standardisation to be performed in situations where the direct method would give unreliable results.

关键词： Standardise Rate Subclinical Hypothyroidism logistic method logistic regression method Subclinical Hyperthyroidism

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A novel rapid approach for the stability discrimination of landslide dams

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BULLETIN OF ENGINEERING GEOLOGY AND THE ENVIRONMENT 2025年第4期84卷 1-20页

作者： Song, Chenguang MNR China Inst Geoenvironm Monitoring Guide Ctr Prevent Technol Geohazards Beijing 100081 Peoples R China

The dam failure of natural landslide dams is a hot research topic at present, and its foundation focuses on the assessment of the stability of natural landslide dams. However, due to the special formation process of the dam body, the heterogeneous composition of materials, the complexity of the internal structure and the different external morphology of the dam body, which leads to the big difference with the artificial earth-rock dams that are more studied at present, there is still a lack of high-precision rapid stability evaluation methods. Based on a comprehensive review of the global research progress on the formation conditions, stability analyses and dam failure mechanisms of landslide dams, a large-scale database containing 1561 cases of landslide dams around the world was established through literature search and case compilation. Furthermore, three composite indicators, namely, dam shape coefficient, lake surface shape coefficient, dam anti-breach coefficient, were constructed as the evaluation factors of landslide dam stability, and a natural landslide dam stability discrimination model (Ls(LVA)) was constructed based on 114 screened cases and the traditional binary logistic regression method, with a comprehensive accuracy of the model up to 86.84%. The research results can provide a complete set of technical ideas for analysing and predicting the stability of landslide dams.

关键词： Landslide dams Stability discrimination logistic regression method Global-scale database Rapid discriminating method

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Predicting the Need for Mental Treatment Across Various Age Groups Using Machine Learning Algorithm 5th

Predicting the Need for Mental Treatment Across Various Age ...

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5th International Conference on Data Science, Machine Learning and Applications

作者： Pal, Surya Kant Rawat, Himani Roy, Rita Khan, Ameer Lareb Kumari, Nisha Srivastava, Prashasya Sharda Univ Dept Math SSBSR Noida UP India GITAM GITAM Inst Technol Dept Comp Sci & Engn Visakhapatnam Andhra Pradesh India

ISBN: (纸本)9789819780334;9789819780310;9789819780303

The extent of mental health is much more than what the general public knows. Many studies have found that women, the elderly, those who had recently survived a disaster, those who worked in the industrial sector, children, adolescents, and persons with long-term medical illnesses were at a higher risk of mental difficulties. Early recognition helps patients treat them more effectively and enhance their lives. Better living conditions, healthcare, and women's empowerment are all necessary. Mental health treatment is required for every age group, from children to seniors. This research will illuminate the need for mental health treatment across different age groups. This prediction is evaluated on the publicly available dataset. The dataset consists of 1259 entries and 27 attributes. Various accuracy metrics were used to evaluate the performance of machine learning techniques in identifying mental health concerns in this study. A comparison of these methods has been done and put into practice and also found that the logistic regression method, based on a forecast accuracy of 83.07%, was the most accurate. The machine learning algorithms used in this study include K-NN Classifier, logistic regression, Decision Tree Classifier, AdaBoost and Random Forest, which are discussed with their algorithm procedure.

关键词： Mental Health Machine Learning logistic regression method Random Forest Age

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The potential of PALSAR RTC elevation data for landform semi-automatic detection and landslide susceptibility modeling

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EUROPEAN JOURNAL OF REMOTE SENSING 2019年第sup1期52卷 148-159页

作者： Correa-Munoz, N. A. Murillo-Feo, C. A. Martinez-Martinez, L. J. Univ Cauca Fac Civil Engn Popayan Colombia Univ Nacl Colombia Fac Engn Bogota Colombia Univ Nacl Colombia Dept Civil & Agr Engn Bogota Colombia Univ Nacl Colombia Fac Agr Sci Bogota Colombia

This study demonstrated the potential of methods derived from geomorphometry and regression models to evaluate landslide susceptibility in a study area located in southern Colombia. From a morphometric stance, the first step was to evaluate the quality of DEM sources by comparison to control points obtained by static-mode GPS. The PALSAR_RTC_hi data was selected for having the best accuracy of heights and was used to derivate terrain parameters at SAGA software. Then, the Principal Component Analysis selected variables with low collinearity, and we classified twelve landforms using fuzzy k-means algorithm, which was compared to a geomorphological map by using the multinomial logistic regression method in R software. We got a Kappa coincidence index of about 30%. The resulting landslide susceptibility mapping took dependent (a mask with unstable-stable cells from an existing landslide inventory) and independent variables (selected morphometric ones). The binary logistic regression showed the propensity of the area to be adversely affected by landslides. This model's performance was tested with a ROC curve over a sample, with 20% of landslide database resulting in an Area Under the Curve of 0,55. This result was contrasted with a spatial prediction model of debris flow, explaining the high frequency of avalanches.

关键词： Geomorphometry PALSAR_RTC-hi data principal components analysis landform logistic regression method landslide susceptibility

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Use of machine learning based technique to X-ray microtomographic images of concrete for phase segmentation at meso-scale

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CONSTRUCTION AND BUILDING MATERIALS 2020年 249卷 118744-118744页

作者： Saha, Sanat Kumar Pradhan, Subhasis Barai, Sudhirkumar V. Indian Inst Technol Kharagpur Dept Civil Engn Kharagpur 721302 W Bengal India Birla Inst Technol & Sci Dept Civil Engn Pilani Campus Pilani 333031 Rajasthan India

The paper discusses the technical limitation of the gray value thresholding technique to detect the voids, aggregate and mortar phases. A two-stage image processing methodology is proposed for the segmentation of the three phases of concrete using the X-ray microtomographic images. In the first stage, the gray value thresholding technique is used to detect the voids. A machine learning based technique is proposed in the second stage for the segmentation of aggregate and mortar. The training data is used to model a planar decision boundary using the logistic regression method. For this, the radial distance from the centre of the image, gray value, and gray value of the filtered embossed image features are considered. The accuracy of the model to quantify the voids is validated with the commercial software. The machine learning model based on logistic regression method exhibits very good accuracy (approximate to 95%) in detecting the aggregate. (C) 2020 Elsevier Ltd. All rights reserved.

关键词： Concrete X-ray microtomography Gray value Threshold gray value Machine learning logistic regression method

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Spiral Concentrator Interface Monitoring Through Image Processing: A Statistical Learning Approach

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IFAC-PapersOnLine 2017年第2期50卷 53-58页

作者： Nienaber, Ernst C. McCoy, John T. Auret, Lidia Department of Process Engineering Stellenbosch University Private Bag X1 Matieland7602 South Africa

Spiral concentrators are robust gravity separation devices that allow for concentration of slurry streams. Optimal splitter position (which determines recovery and grade) is dependent on the interface positions of the concentrate, middlings and/or tailings in the trough. Various image processing techniques have been proposed to automatically detect interface positions, which could be useful for spiral concentrator monitoring and control. Two methods are compared in this work: the first is the previously-described genetic algorithm optimization of the parameters of a traditional edge detection algorithm. The second uses logistic regression, a well-known statistical classifier. The performance of the two methods was compared on two data sets, for ilmenite and chromite concentration. The logistic regression method was shown to outperform the genetic algorithm approach, in terms of computational cost of training and successful interface detections on test data, for both the relatively easy ilmenite concentrate interface, and the more challenging chromite concentrate interface. © 2017

关键词： Image processing Classification (of information) Concentration (process) Edge detection Genetic algorithms Ilmenite Learning systems Multiplexing equipment Process monitoring regression analysis Edge detection algorithms Genetic algorithm approach Genetic algorithm optimizations Image processing technique logistic regression method Mineral processing Statistical classifier Statistical inference

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Landslide susceptibility mapping based on Support Vector Machine: A case study on natural slopes of Hong Kong, China

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GEOMORPHOLOGY 2008年第4期101卷 572-582页

作者： Yao, X. Tham, L. G. Dai, F. C. Chinese Acad Sci Inst Geol & Geophys Beijing 100029 Peoples R China Univ Hong Kong Dept Civil Engn Hong Kong Hong Kong Peoples R China

The Support Vector Machine (SVM) is an increasingly popular learning procedure based on statistical learning theory, and involves a training phase in which the model is trained by a training dataset of associated input and target output values. The trained model is then used to evaluate a separate set of testing data. There are two main ideas underlying the SVM for discriminant-type problems. The first is an optimum linear separating hyperplane that separates the data patterns. The second is the use of kernel functions to convert the original non-linear data patterns into the format that is linearly separable in a high-dimensional feature space. In this paper, an overview of the SVM, both one-class and two-class SVM methods, is first presented followed by its use in landslide susceptibility mapping. A study area was selected from the natural terrain of Hong Kong, and slope angle, slope aspect, elevation, profile curvature of slope, lithology, vegetation cover and topographic wetness index (TWI) were used as environmental parameters which influence the occurrence of landslides. One-class and two-class SVM models were trained and then used to map landslide susceptibility respectively. The resulting susceptibility maps obtained by the methods were compared to that obtained by the logistic regression (LR) method. It is concluded that two-class SVM possesses better prediction efficiency than logistic regression and one-class SVM. However, one-class SVM, which only requires failed cases, has an advantage over the other two methods as only "failed" case information is usually available in landslide susceptibility mapping. (C) 2008 Elsevier B.V. All rights reserved.

关键词： Landslide susceptibility mapping Support Vector Machine (SVM) One-class sample Two-class sample logistic regression method Hong Kong

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logistic regression model of fotemustine toxicity combining independent phase II studies

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CANCER 1996年第9期78卷 1980-1987页

作者： Raymond, E Haon, C Boaziz, C Coste, M HOP ST ANTOINE SERV MED INTERNE F-75571 PARIS FRANCE PROC STAT VERSAILLES FRANCE INST RECH INT SERVIER F-92415 COURBEVOIE FRANCE

BACKGROUND. To optimize fotemustine chemotherapy, the authors considered how to combine independent Phase II trials to predict the risk of first occurrence of severe toxicity as a function of initial patient characteristics. methodS. Clinical data from six Phase II trials were collected. Of the 478 patients enrolled, 442 (male/female, 259/183;age range, 15-81 years) were evaluable for toxicity (1384 cycles of chemotherapy), including 221 with malignant melanomas, 138 with primary brain tumors, 29 with lung carcinomas, 8 with head and neck carcinomas, and 46 with miscellaneous cancers. The influence of age, sex, performance status, type of tumor, number and location of metastases, and previous treatment by chemotherapy and/or radiotherapy was studied. The logistic regression method was applied to predict occurrence of leukopenia, thrombocytopenia, anemia, digestive tract, and/or hepatic toxicity. RESULTS. Univariate analysis showed that predictive factors for hematologic toxicity were age (> 50 years), type of tumor (brain < melanoma < others), number of metastatic sites (> 3), location of metastases (nonvisceral), and previous chemotherapy. Performance status and previous radiotherapy did not affect the toxicity of fotemustine. Nausea and vomiting were predictable based on the type of tumor (head and neck < lung ( brain < melanoma < others), the number of metastatic sites (> three), and visceral metastases. Hepatic disorders occurred preferentially in patients with hepatic metastases and more than three metastatic sites. Individual risk of hematologic and hepatic toxicity for patients with melanoma and primary brain tumors was predicted using logistic regression models. CONCLUSIONS. By combining clinical data from independent Phase II trials, the logistic model developed could predict the probability of fotemustine hematologic and hepatic toxicity. (C) 1996 American Cancer Society.

关键词： fotemustine nitrosourea multifactorial analysis logistic regression method chemotherapy toxicity quality of life

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