scholarly journals Detecting Areas Vulnerable to Flooding Using Hydrological-Topographic Factors and Logistic Regression

2021 ◽  
Vol 11 (12) ◽  
pp. 5652
Author(s):  
Jae-Yeong Lee ◽  
Ji-Sung Kim

As a result of rapid urbanization and population movement, flooding in urban areas has become one of the most common types of natural disaster, causing huge losses of both life and property. To mitigate and prevent the damage caused by the recent increase in floods, a number of measures are required, such as installing flood prevention facilities, or specially managing areas vulnerable to flooding. In this study, we presented a technique for determining areas susceptible to flooding using hydrological-topographic characteristics for the purpose of managing flood vulnerable areas. To begin, we collected digital topographic maps and stormwater drainage system data regarding the study area. Using the collected data, surface, locational, and resistant factors were analyzed. In addition, the maximum 1-h rainfall data were collected as an inducing factor and assigned to all grids through spatial interpolation. Next, a logistic regression analysis was performed by inputting hydrological-topographic factors and historical inundation trace maps for each grid as independent and dependent variables, respectively, through which a model for calculating the flood vulnerability of the study area was established. The performance of the model was evaluated by analyzing the receiver operating characteristics (ROC) curve of flood vulnerability and inundation trace maps, and it was found to be improved when the rainfall that changes according to flood events was also considered. The method presented in this study can be used not only to reasonably and efficiently select target sites for flood prevention facilities, but also to pre-detect areas vulnerable to flooding by using real-time rainfall forecasting.

2014 ◽  
Vol 71 (5) ◽  
pp. 653-660 ◽  
Author(s):  
M. Jung ◽  
H. Kim ◽  
K. J. B. Mallari ◽  
G. Pak ◽  
J. Yoon

Both water quantity and quality are impacted by climate change. In addition, rapid urbanization has also brought an immeasurable loss of life and property resulting from floods. Hence, there is a need to predict changes in rainfall events to effectively design stormwater infrastructure to protect urban areas from disaster. This study develops a framework for predicting future short duration rainfall intensity and examining the effects of climate change on urban runoff in the Gunja Drainage Basin. Non-stationarities in rainfall records are first analysed using trend analysis to extrapolate future climate change scenarios. The US Environmental Protection Agency Storm Water Management Model (SWMM) was used for single event simulation of runoff quantity from the study area. For the 1-hour and 24-hour durations, statistically significant upward trends were observed. Although the 10-minute duration was only nearly significant at the 90% level, the steepest slope was observed for this short duration. Moreover, it was observed that the simulated peak discharge from SWMM increases as the short duration rainfall intensity increases. The proposed framework is thought to provide a means to review the current design of stormwater infrastructures to determine their capacity, along with consideration of climate change impact.


2007 ◽  
Vol 7 ◽  
pp. 113 ◽  
Author(s):  
Raj Man Shrestha

The increase of population in Kathmandu valley is bringing a considerable change in cropping system. Rapid urbanization and introduction of new agriculture technology have encouraged the valley’s farmers to change their cropping patterns from traditional (low value crops) to new crops (high value crops). According to numerous studies made in Nepal, the change is seen considerably in winter crops than in summer crops and the land under cultivation of green leafy vegetables is increasing rapidly in the urban and semi-urban areas. An average growth of population at 3 % in the valley during the period 1951-2001 has resulted in the rapid expansion of area under urban coverage (24.6 % growth per year from 1984 - 2000) has made agriculture land of Katmandu valley to decline per year by 2.04 % (836.27 ha per year). If this trend of decline in agriculture land in Kathmandu valley continues in future too, it is expected that there will be no agriculture land left over by two and half decades in the valley. The planners should take note of this fact that if fertile land of Katmandu valley is to be preserved for agriculture necessary planning is urgently needed. <i>Nepal Journal of Science and Technology</i> Vol. 7, 2006


Land ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. 318
Author(s):  
B. G. J. S. Sonneveld ◽  
M. D. Houessou ◽  
G. J. M. van den Boom ◽  
A. Aoudji

In the context of rapid urbanization, poorer residents in cities across low- and middle-income countries increasingly experience food and nutrition deficiencies. The United Nations has highlighted urban agriculture (UA) as a viable solution to food insecurity, by empowering the urban poor to produce their own fresh foods and make some profit from surplus production. Despite its potential role in reducing poverty and food insecurity, there appears to be little political will to support urban agriculture. This is seen in unclear political mandates that are sustained by information gaps on selection criteria for UA sites. The research reported here addresses this issue in the form of a decision-making support tool that assesses the suitability of cadastral units and informal plots for allotment gardens in urban and peri-urban areas. The tool was developed and tested for three rapidly expanding cities in Benin, a low-income country in West Africa, based on an ordered logit model that relates a set of 300 expert assessments on site suitability to georeferenced information on biophysical and socio-economic characteristics. Soil, land use, groundwater depth, vicinity to market and women’s safety were significant factors in the assessment. Scaled up across all cadastral units and informal sites, the tool generated detailed baseline maps on site suitability and availability of areas. Its capacity to support policymakers in selecting appropriate sites comes to the fore by reporting changes in site suitability under scenarios of improved soil fertility and enhanced safety for women.


Water ◽  
2020 ◽  
Vol 12 (9) ◽  
pp. 2647
Author(s):  
Esteban Sañudo ◽  
Luis Cea ◽  
Jerónimo Puertas

Dual urban drainage models allow users to simulate pluvial urban flooding by analysing the interaction between the sewer network (minor drainage system) and the overland flow (major drainage system). This work presents a free distribution dual drainage model linking the models Iber and Storm Water Management Model (SWMM), which are a 2D overland flow model and a 1D sewer network model, respectively. The linking methodology consists in a step by step calling process from Iber to a Dynamic-link Library (DLL) that contains the functions in which the SWMM code is split. The work involves the validation of the model in a simplified urban street, in a full-scale urban drainage physical model and in a real urban settlement. The three study cases have been carefully chosen to show and validate the main capabilities of the model. Therefore, the model is developed as a tool that considers the main hydrological and hydraulic processes during a rainfall event in an urban basin, allowing the user to plan, evaluate and design new or existing urban drainage systems in a realistic way.


2014 ◽  
Vol 5 (3) ◽  
pp. 30-34 ◽  
Author(s):  
Balkishan Sharma ◽  
Ravikant Jain

Objective: The clinical diagnostic tests are generally used to identify the presence of a disease. The cutoff value of a diagnostic test should be chosen to maximize the advantage that accrues from testing a population of human and others. When a diagnostic test is to be used in a clinical condition, there may be an opportunity to improve the test by changing the cutoff value. To enhance the accuracy of diagnosis is to develop new tests by using a proper statistical technique with optimum sensitivity and specificity. Method: Mean±2SD method, Logistic Regression Analysis, Receivers Operating Characteristics (ROC) curve analysis and Discriminant Analysis (DA) have been discussed with their respective applications. Results: The study highlighted some important methods to determine the cutoff points for a diagnostic test. The traditional method is to identify the cut-off values is Mean±2SD method. Logistic Regression Analysis, Receivers Operating Characteristics (ROC) curve analysis and Discriminant Analysis (DA) have been proved to be beneficial statistical tools for determination of cut-off points.Conclusion: There may be an opportunity to improve the test by changing the cut-off value with the help of a correctly identified statistical technique in a clinical condition when a diagnostic test is to be used. The traditional method is to identify the cut-off values is Mean ± 2SD method. It was evidenced in certain conditions that logistic regression is found to be a good predictor and the validity of the same can be confirmed by identifying the area under the ROC curve. Abbreviations: ROC-Receiver operating characteristics and DA-Discriminant Analysis. Asian Journal of Medical Science, Volume-5(3) 2014: 30-34 http://dx.doi.org/10.3126/ajms.v5i3.9296      


2018 ◽  
Vol 8 (1) ◽  
pp. 16 ◽  
Author(s):  
Irina Matijosaitiene ◽  
Peng Zhao ◽  
Sylvain Jaume ◽  
Joseph Gilkey Jr

Predicting the exact urban places where crime is most likely to occur is one of the greatest interests for Police Departments. Therefore, the goal of the research presented in this paper is to identify specific urban areas where a crime could happen in Manhattan, NY for every hour of a day. The outputs from this research are the following: (i) predicted land uses that generates the top three most committed crimes in Manhattan, by using machine learning (random forest and logistic regression), (ii) identifying the exact hours when most of the assaults are committed, together with hot spots during these hours, by applying time series and hot spot analysis, (iii) built hourly prediction models for assaults based on the land use, by deploying logistic regression. Assault, as a physical attack on someone, according to criminal law, is identified as the third most committed crime in Manhattan. Land use (residential, commercial, recreational, mixed use etc.) is assigned to every area or lot in Manhattan, determining the actual use or activities within each particular lot. While plotting assaults on the map for every hour, this investigation has identified that the hot spots where assaults occur were ‘moving’ and not confined to specific lots within Manhattan. This raises a number of questions: Why are hot spots of assaults not static in an urban environment? What makes them ‘move’—is it a particular urban pattern? Is the ‘movement’ of hot spots related to human activities during the day and night? Answering these questions helps to build the initial frame for assault prediction within every hour of a day. Knowing a specific land use vulnerability to assault during each exact hour can assist the police departments to allocate forces during those hours in risky areas. For the analysis, the study is using two datasets: a crime dataset with geographical locations of crime, date and time, and a geographic dataset about land uses with land use codes for every lot, each obtained from open databases. The study joins two datasets based on the spatial location and classifies data into 24 classes, based on the time range when the assault occurred. Machine learning methods reveal the effect of land uses on larceny, harassment and assault, the three most committed crimes in Manhattan. Finally, logistic regression provides hourly prediction models and unveils the type of land use where assaults could occur during each hour for both day and night.


2021 ◽  
pp. 174077452110101
Author(s):  
Jennifer Proper ◽  
John Connett ◽  
Thomas Murray

Background: Bayesian response-adaptive designs, which data adaptively alter the allocation ratio in favor of the better performing treatment, are often criticized for engendering a non-trivial probability of a subject imbalance in favor of the inferior treatment, inflating type I error rate, and increasing sample size requirements. The implementation of these designs using the Thompson sampling methods has generally assumed a simple beta-binomial probability model in the literature; however, the effect of these choices on the resulting design operating characteristics relative to other reasonable alternatives has not been fully examined. Motivated by the Advanced R2 Eperfusion STrategies for Refractory Cardiac Arrest trial, we posit that a logistic probability model coupled with an urn or permuted block randomization method will alleviate some of the practical limitations engendered by the conventional implementation of a two-arm Bayesian response-adaptive design with binary outcomes. In this article, we discuss up to what extent this solution works and when it does not. Methods: A computer simulation study was performed to evaluate the relative merits of a Bayesian response-adaptive design for the Advanced R2 Eperfusion STrategies for Refractory Cardiac Arrest trial using the Thompson sampling methods based on a logistic regression probability model coupled with either an urn or permuted block randomization method that limits deviations from the evolving target allocation ratio. The different implementations of the response-adaptive design were evaluated for type I error rate control across various null response rates and power, among other performance metrics. Results: The logistic regression probability model engenders smaller average sample sizes with similar power, better control over type I error rate, and more favorable treatment arm sample size distributions than the conventional beta-binomial probability model, and designs using the alternative randomization methods have a negligible chance of a sample size imbalance in the wrong direction. Conclusion: Pairing the logistic regression probability model with either of the alternative randomization methods results in a much improved response-adaptive design in regard to important operating characteristics, including type I error rate control and the risk of a sample size imbalance in favor of the inferior treatment.


Entropy ◽  
2018 ◽  
Vol 20 (11) ◽  
pp. 884 ◽  
Author(s):  
Tingyu Zhang ◽  
Ling Han ◽  
Wei Chen ◽  
Himan Shahabi

The main purpose of the present study is to apply three classification models, namely, the index of entropy (IOE) model, the logistic regression (LR) model, and the support vector machine (SVM) model by radial basis function (RBF), to produce landslide susceptibility maps for the Fugu County of Shaanxi Province, China. Firstly, landslide locations were extracted from field investigation and aerial photographs, and a total of 194 landslide polygons were transformed into points to produce a landslide inventory map. Secondly, the landslide points were randomly split into two groups (70/30) for training and validation purposes, respectively. Then, 10 landslide explanatory variables, such as slope aspect, slope angle, altitude, lithology, mean annual precipitation, distance to roads, distance to rivers, distance to faults, land use, and normalized difference vegetation index (NDVI), were selected and the potential multicollinearity problems between these factors were detected by the Pearson Correlation Coefficient (PCC), the variance inflation factor (VIF), and tolerance (TOL). Subsequently, the landslide susceptibility maps for the study region were obtained using the IOE model, the LR–IOE, and the SVM–IOE model. Finally, the performance of these three models was verified and compared using the receiver operating characteristics (ROC) curve. The success rate results showed that the LR–IOE model has the highest accuracy (90.11%), followed by the IOE model (87.43%) and the SVM–IOE model (86.53%). Similarly, the AUC values also showed that the prediction accuracy expresses a similar result, with the LR–IOE model having the highest accuracy (81.84%), followed by the IOE model (76.86%) and the SVM–IOE model (76.61%). Thus, the landslide susceptibility map (LSM) for the study region can provide an effective reference for the Fugu County government to properly address land planning and mitigate landslide risk.


2018 ◽  
Vol 16 (4) ◽  
pp. 296-306
Author(s):  
Justin T McDaniel ◽  
Robert J McDermott ◽  
Mary P Martinasek ◽  
Robin M White

Objective We sought to determine variables associated with asthma among children from military and non-military families. Methods We performed secondary data analysis on the 2016 Behavioral Risk Factor Surveillance System. Parents with and without military experience ( n = 61,079) were asked whether a child ever had asthma and currently has asthma. We used two multiple logistic regression models to determine the influence of rurality and geographic region on “ever” and “current” asthma in children of military and non-military families, while controlling for socio-demographic and behavioral variables. Results Overall childhood asthma prevalence for children in military families was lower than non-military families (ever, 9.7% vs. 12.9%; currently, 6.2% vs. 8.2%) in 2016. However, multiple logistic regression showed variation in “ever” and “current” asthma among children of military and non-military families by rurality and race. Discussion Developers of public health asthma interventions should consider targeting African-American children of military families living in urban areas. This population is approximately twice as likely to have asthma as Caucasian children of non-military families.


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