scholarly journals Using Multi-Factor Analysis to Predict Urban Flood Depth Based on Naive Bayes

Water ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 432
Author(s):  
Huiliang Wang ◽  
Hongfa Wang ◽  
Zening Wu ◽  
Yihong Zhou
Keyword(s):  
Urban Areas ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Extreme Weather Events ◽  
Urban Flooding ◽  
Urban Flood ◽  
Flood Depth ◽  
Depth Prediction ◽  
Bayes Theory ◽  
Flooded Areas

With global warming, the number of extreme weather events will increase. This scenario, combined with accelerating urbanization, increases the likelihood of urban flooding. Therefore, it is necessary to predict the characteristics of flooded areas caused by rainstorms, especially the flood depth. We applied the Naive Bayes theory to construct a model (NB model) to predict urban flood depth here in Zhengzhou. The model used 11 factors that affect the extent of flooding—rainfall, duration of rainfall, peak rainfall, the proportion of roads, woodlands, grasslands, water bodies and building, permeability, catchment area, and slope. The forecast depth of flooding from the NB model under different rainfall conditions was used to draw an urban inundation map by ArcGIS software. The results show that the probability and degree of urban flooding in Zhengzhou increases significantly after a return period of once every two years, and the flooded areas mainly occurred in older urban areas. The average root mean square error of prediction results was 0.062, which verifies the applicability and validity of our model in the depth prediction of urban floods. Our findings suggest the NB model as a feasible approach to predict urban flood depth.

scholarly journals Impact Assessment of Urban Flood on Traffic Disruption using Rainfall–Depth–Vehicle Speed Relationship

Water ◽  
2020 ◽  
Vol 12 (4) ◽  
pp. 926 ◽  
Author(s):  
Kyung-Su Choo ◽  
Dong-Ho Kang ◽  
Byung-Sik Kim
Keyword(s):  
Urban Areas ◽  
Assessment Tool ◽  
Transportation Network ◽  
Flood Damage ◽  
Target Area ◽  
Vehicle Speed ◽  
Urban Flood ◽  
Socioeconomic Impacts ◽  
Flood Depth ◽  
Driving Speed

The transportation network enables movement of people and goods and is the basis of economic activity. Recently, short-term locally heavy rains occur frequently in urban areas, causing serious obstacles to road flooding and increasing economic and social effects. Therefore, in advanced weather countries, many studies have been conducted on realistic and reliable impact forecasting by analyzing socioeconomic impacts, not just information transmission as weather forecasts. In this paper, we use the Spatial Runoff Assessment Tool (S-RAT) and Flood Inundation model (FLO-2D model) to calculate the flooding level in urban areas caused by rainfall and use the flooding rate. In addition, the rainfall–flood depth curve and the Flood–Vehicle Speed curve were presented during the analysis, and the traffic disruption map was prepared using this. The results of this study were compared with previous studies and verified by rainfall events in 2011. As a result of the verification, the result was similar to the actual flooding, and when the same rainfall occurred within the range of the target area, it was confirmed that there were sections that could not be passed and sections that could be passed smoothly. Therefore, the results suggested in this study will be helpful for the driver’s route selection by using the urban flood damage analysis and vehicle driving speed analysis.

scholarly journals Integrate Weather Radar and Monitoring Devices for Urban Flooding Surveillance

Sensors ◽  
2019 ◽  
Vol 19 (4) ◽  
pp. 825 ◽  
Author(s):  
Shih-Yen Hsu ◽  
Tai-Been Chen ◽  
Wei-Chang Du ◽  
Jyh-Horng Wu ◽  
Shih-Chieh Chen
Keyword(s):  
Real Time ◽  
Urban Areas ◽  
Weather Radar ◽  
Water Levels ◽  
Mitigation Measures ◽  
Extreme Weather Events ◽  
Television System ◽  
Urban Flood ◽  
Flood Monitoring ◽  
Image Capturing

With the increase of extreme weather events, the frequency and severity of urban flood events in the world are increasing drastically. Therefore, this study develops ARMT (automatic combined ground weather radar and CCTV (Closed Circuit Television System) images for real-time flood monitoring), which integrates real-time ground radar echo images and automatically estimates a rainfall hotspot according to the cloud intensity. Furthermore, ARMT combines CCTV image capturing, analysis, and Fourier processing, identification, water level estimation, and data transmission to provide real-time warning information. Furthermore, the hydrograph data can serve as references for relevant disaster prevention, and response personnel may take advantage of them and make judgements based on them. The ARMT was tested through historical data input, which showed its reliability to be between 83% to 92%. In addition, when applied to real-time monitoring and analysis (e.g., typhoon), it had a reliability of 79% to 93%. With the technology providing information about both images and quantified water levels in flood monitoring, decision makers can quickly better understand the on-site situation so as to make an evacuation decision before the flood disaster occurs as well as discuss appropriate mitigation measures after the disaster to reduce the adverse effects that flooding poses on urban areas.

scholarly journals Patch Similarity Convolutional Neural Network for Urban Flood Extent Mapping Using Bi-Temporal Satellite Multispectral Imagery

2019 ◽  
Vol 11 (21) ◽  
pp. 2492 ◽  
Author(s):  
Bo Peng ◽  
Zonglin Meng ◽  
Qunying Huang ◽  
Caixia Wang
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Real Time ◽  
Spectral Reflectance ◽  
Urban Areas ◽  
Flood Mapping ◽  
Urban Flooding ◽  
Urban Flood ◽  
Before And After ◽  
Flood Maps

Urban flooding is a major natural disaster that poses a serious threat to the urban environment. It is highly demanded that the flood extent can be mapped in near real-time for disaster rescue and relief missions, reconstruction efforts, and financial loss evaluation. Many efforts have been taken to identify the flooding zones with remote sensing data and image processing techniques. Unfortunately, the near real-time production of accurate flood maps over impacted urban areas has not been well investigated due to three major issues. (1) Satellite imagery with high spatial resolution over urban areas usually has nonhomogeneous background due to different types of objects such as buildings, moving vehicles, and road networks. As such, classical machine learning approaches hardly can model the spatial relationship between sample pixels in the flooding area. (2) Handcrafted features associated with the data are usually required as input for conventional flood mapping models, which may not be able to fully utilize the underlying patterns of a large number of available data. (3) High-resolution optical imagery often has varied pixel digital numbers (DNs) for the same ground objects as a result of highly inconsistent illumination conditions during a flood. Accordingly, traditional methods of flood mapping have major limitations in generalization based on testing data. To address the aforementioned issues in urban flood mapping, we developed a patch similarity convolutional neural network (PSNet) using satellite multispectral surface reflectance imagery before and after flooding with a spatial resolution of 3 meters. We used spectral reflectance instead of raw pixel DNs so that the influence of inconsistent illumination caused by varied weather conditions at the time of data collection can be greatly reduced. Such consistent spectral reflectance data also enhance the generalization capability of the proposed model. Experiments on the high resolution imagery before and after the urban flooding events (i.e., the 2017 Hurricane Harvey and the 2018 Hurricane Florence) showed that the developed PSNet can produce urban flood maps with consistently high precision, recall, F1 score, and overall accuracy compared with baseline classification models including support vector machine, decision tree, random forest, and AdaBoost, which were often poor in either precision or recall. The study paves the way to fuse bi-temporal remote sensing images for near real-time precision damage mapping associated with other types of natural hazards (e.g., wildfires and earthquakes).

scholarly journals Vulnerability Assessment of Urban Floods in Lahore, Pakistan using GIS based integrated Analytical Hierarchy Approach

2021 ◽  
Vol 58 (1) ◽  
pp. 85-96
Author(s):  
Sahar Zia ◽  
Safdar A. Shirazi ◽  
Muhammad Nasar-u-Minallah
Keyword(s):  
Land Use ◽  
Flood Risk ◽  
Urban Areas ◽  
Physical Factors ◽  
Urban Flooding ◽  
Residential Areas ◽  
Urban Flood ◽  
Risk Areas ◽  
Practical Tool ◽  
Flood Risk Mapping

Urban flooding is getting attention due to its adverse impact on urban lives in mega cities of the developing world particularly Pakistan. This study aims at finding a suitable methodology for mapping urban flooded areas to estimate urban flooding vulnerability risks in the cities of developing countries particularly Lahore, Pakistan. To detect the urban flooded vulnerability and risk areas due to natural disaster, GIS-based integrated Analytical Hierarchy Process (AHP) is applied for the case of Lahore, which is the second most populous city and capital of the Punjab, Pakistan. For the present research, the flood risk mapping is prepared by considering these significant physical factors like elevation, slope, and distribution of rainfall, land use, density of the drainage network, and soil type. Results show that the land use factor is the most significant to detect vulnerable areas near roads and commercial areas. For instance, this method of detection is 88%, 80% and 70% accurate for roads, commercial and residential areas. The methodology implemented in the present research can provide a practical tool and techniques to relevant policy and decision-makers authorities to prioritize and actions to mitigate flood risk and vulnerabilities and identify certain vulnerable urban areas, while formulating a methodology for future urban flood risk and vulnerability mitigation through an objectively simple and organizationally secure approach. 

scholarly journals Public Participatory Role in Urban Flood Risk Management of Ho Chi Minh City - Vietnam: From Awareness to Action

2019 ◽  
pp. 1-10
Author(s):  
Pham Thi Anh ◽  
Nguyen Thi Bao Ngoc
Keyword(s):  
Urban Areas ◽  
Drainage System ◽  
Ho Chi Minh City ◽  
Urban Flood ◽  
Garbage Disposal ◽  
Large Numbers ◽  
Technical Effort ◽  
And Behavior ◽  
The City ◽  
Flooded Areas

Urban flooding has become a regular phenomenon in many towns and cities in the world over the past years. Flooding in urban areas in Ho Chi Minh City poses serious challenges not only by affecting large numbers of people and properties in urban areas but also directly hindering the economic growth of the city. Despite the huge technical effort to improve the city's drainage system, which is necessitated by phenomenal growth of the city and the challenges of climate change and land subsidence, it is impossible to put and end to flooding. The human factor appears an important element in the flooding problem and the efforts of flood reduction. In this study the emphasis was laid on the issue of inappropriate garbage disposal which leads to obstruction of drainage systems. As a part of a well-planned strategy an interactive survey was conducted in about 820 households in flooding areas. The survey focused on awareness and behavior of public garbage disposal of households living in flooded areas. People have an understanding of the causes of flooding, and have a sense of environmental protection, they can contribute to reducing flooding. In addition to technological solutions, community awareness, solutions for management and sanctioning are necessary.

scholarly journals Assessment of Community Vulnerability to Different Types of Urban Floods: A Case for Lishui City, China

2020 ◽  
Vol 12 (19) ◽  
pp. 7865 ◽  
Author(s):  
Quntao Yang ◽  
Shuliang Zhang ◽  
Qiang Dai ◽  
Rui Yao
Keyword(s):  
Urban Areas ◽  
Flood Hazard ◽  
Urban Flooding ◽  
Urban Floods ◽  
Flood Vulnerability ◽  
Urban Flood ◽  
Indicator Method ◽  
Comprehensive Method ◽  
Different Types ◽  
Hydrometeorological Conditions

Urban flooding is a severe and pervasive hazard caused by climate change, urbanization, and limitations of municipal drainage systems. Cities face risks from different types of floods, depending on various geographical, environmental, and hydrometeorological conditions. In response to the growing threat of urban flooding, a better understanding of urban flood vulnerability is needed. In this study, a comprehensive method was developed to evaluate the vulnerability of different types of urban floods. First, a coupled urban flood model was built to obtain the extent of influence of various flood scenarios caused by rainfall and river levee overtopping. Second, an assessment framework for urban flood vulnerability based on an indicator method was used to evaluate the vulnerability in different flood hazard scenarios. Finally, the method was applied to Lishui City, China, and the distribution and pattern of urban flood vulnerability were studied. The results highlight the spatial variability of flooding and the vulnerability distributions of different types of urban floods. Compound floods were identified to cause more severe effects in the urban areas.

scholarly journals Comparison of dynamic flow interaction methods between pipe system and overland in urban flood analysis

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Xiaoli Hao ◽  
Yanmin Li ◽  
Shu Liu
Keyword(s):  
Surface Runoff ◽  
Urban Areas ◽  
Goodness Of Fit ◽  
Management Model ◽  
Urban Flooding ◽  
Dynamic Flow ◽  
Urban Flood ◽  
Storm Water Management Model ◽  
Pipe System ◽  
Flow Interactions

AbstractUrban flooding can be predicted by using different modeling approaches. This study considered different methods of modeling the dynamic flow interactions between pipe systems and surface flooding in urban areas. These approaches can be divided into two categories based on surface runoff collection units. This paper introduces a new hydrodynamic model that couples the storm water management model and the 2D overland model. The model’s efficiency was validated based on the aforementioned experimental dataset; agreement was verified by correlation values above 0.6. Additionally, this study used different approaches and compared their accuracy in predicting flooding patterns. The results show that the use of sub-catchments to model the collection of surface runoff was not predictive of the inundation process, indicating a lower goodness of fit with the recorded values than that of adopting cells. Moreover, to determine which method of adopting cells to collect runoff could better predict rainstorm-induced inundation, an error and correlation analysis was conducted. The analysis found low error and high correlation, suggesting that inundation can be effectively predicted by the new approaches. Ultimately, this study contributes to existing work on numerical analysis of the interaction methods of urban flooding.

scholarly journals IMPLEMENTASI TEORI NAIVE BAYES DALAM KLASIFIKASI CALON MAHASISWA BARU STMIK KHARISMA MAKASSAR

2020 ◽  
Vol 3 (2) ◽  
pp. 110-117
Author(s):  
Irayori Loelianto ◽  
Moh. Sofyan S Thayf ◽  
Husni Angriani
Keyword(s):  
Naive Bayes ◽  
Naïve Bayes ◽  
Training Data ◽  
Design Stage ◽  
Prospective Students ◽  
Classifier Design ◽  
Python Programming Language ◽  
Bayes Theory ◽  
Python Programming

STMIK KHARISMA Makassar has graduated thousands of alumni since it was founded. However, the number of students registering is uncertain every year, although from 2016 to 2019 there has been an increase in the number of registrations. The problem is the percentage of the number of prospective students registering has actually decreased significantly. The purpose of this research is to implement the Naive Bayes theory in classification of STMIK KHARISMA Makassar prospective students. This research basically uses the Naive Bayes theory as a classifier, and is made using the Python programming language. At the classifier design stage, there were a total of 499 data collected from 2016 to 2019. The data was divided by a ratio of 80:20 for training data and test data. The result from the research indicate the level of accuracy of the classifier reaches 73%.

scholarly journals The Development and Application of the Urban Flood Risk Assessment Model for Reflecting upon Urban Planning Elements

Water ◽  
2019 ◽  
Vol 11 (5) ◽  
pp. 920 ◽  
Author(s):  
Kiyong Park ◽  
Man-Hyung Lee
Keyword(s):  
Land Use ◽  
Land Use Planning ◽  
Flood Risk ◽  
Urban Areas ◽  
Assessment Model ◽  
Urban Flooding ◽  
Urban Flood ◽  
High Flood ◽  
Different Levels ◽  
Urban Flood Risk

As a city develops and expands, it is likely confronted with a variety of environmental problems. Although the impact of climate change on people has continuously increased in the past, great numbers of natural disasters in urban areas have become varied in terms of form. Among these urban disasters, urban flooding is the most frequent type, and this study focuses on urban flooding. In cities, the population and major facilities are concentrated, and to examine flooding issues in these urban areas, different levels of flooding risk are classified on 100 m × 100 m geographic grids to maximize the spatial efficiency during the flooding events and to minimize the following flooding damage. In this analysis, vulnerability and exposure tests are adopted to analyze urban flooding risks. The first method is based on land-use planning, and the building-to-land ratio. Using fuzzy approaches, the tests focus on risks. However, the latter method using the HEC-Ras model examines factors such as topology and precipitation volume. By mapping the classification of land-use and flooding, the risk of urban flooding is evaluated by grade-scales: green, yellow, orange, and red zones. There are two key findings and theoretical contributions of this study. First, the areas with a high flood risk are mainly restricted to central commercial areas where the main urban functions are concentrated. Additionally, the development density and urbanization are relatively high in these areas, in addition to the old center of urban areas. In the case of Changwon City, Euichang-gu and Seongsan-gu have increased the flood risk because of the high property value of commercial areas and high building density in these regions. Thus, land-use planning of these districts should be designed to reflect upon the different levels of flood risks, in addition to the preparation of anti-disaster facilities to mitigate flood damages in high flood risk areas. Urban flood risk analysis for individual land use districts would facilitate urban planners and managers to prioritize the areas with a high flood risk and to prepare responding preventive measures for more efficient flood management.

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