Flash Flood Susceptibility Assessment Based on Geodetector, Certainty Factor, and Logistic Regression Analyses in Fujian Province, China

Flash floods are one of the most frequent natural disasters in Fujian Province, China, and they seriously threaten the safety of infrastructure, natural ecosystems, and human life. Thus, recognition of possible flash flood locations and exploitation of more precise flash flood susceptibility maps are crucial to appropriate flash flood management in Fujian. Based on this objective, in this study, we developed a new method of flash flood susceptibility assessment. First, we utilized double standards, including the Pearson correlation coefficient (PCC) and Geodetector to screen the assessment indicator. Second, in order to consider the weight of each classification of indicator and the weights of the indicators simultaneously, we used the ensemble model of the certainty factor (CF) and logistic regression (LR) to establish a frame for the flash flood susceptibility assessment. Ultimately, we used this ensemble model (CF-LR), the standalone CF model, and the standalone LR model to prepare flash flood susceptibility maps for Fujian Province and compared their prediction performance. The results revealed the following. (1) Land use, topographic relief, and 24 h precipitation (H24_100) within a 100-year return period were the three main factors causing flash floods in Fujian Province. (2) The area under the curve (AUC) results showed that the CF-LR model had the best precision in terms of both the success rate (0.860) and the prediction rate (0.882). (3) The assessment results of all three models showed that between 22.27% and 29.35% of the study area have high and very high susceptibility levels, and these areas are mainly located in the east, south, and southeast coastal areas, and the north and west low mountain areas. The results of this study provide a scientific basis and support for flash flood prevention in Fujian Province. The proposed susceptibility assessment framework may also be helpful for other natural disaster susceptibility analyses.

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A Comparative Study of Kernel Logistic Regression, Radial Basis Function Classifier, Multinomial Naïve Bayes, and Logistic Model Tree for Flash Flood Susceptibility Mapping

Water ◽

10.3390/w12010239 ◽

2020 ◽

Vol 12 (1) ◽

pp. 239 ◽

Cited By ~ 12

Author(s):

Binh Thai Pham ◽

Tran Van Phong ◽

Huu Duy Nguyen ◽

Chongchong Qi ◽

Nadhir Al-Ansari ◽

...

Keyword(s):

Logistic Regression ◽

Radial Basis Function ◽

Basis Function ◽

Logistic Model ◽

Flash Flood ◽

Kernel Logistic Regression ◽

Flood Susceptibility ◽

Susceptibility Maps ◽

Logistic Model Tree ◽

Flood Susceptibility Mapping

Risk of flash floods is currently an important problem in many parts of Vietnam. In this study, we used four machine-learning methods, namely Kernel Logistic Regression (KLR), Radial Basis Function Classifier (RBFC), Multinomial Naïve Bayes (NBM), and Logistic Model Tree (LMT) to generate flash flood susceptibility maps at the minor part of Nghe An province of the Center region (Vietnam) where recurrent flood problems are being experienced. Performance of these four methods was evaluated to select the best method for flash flood susceptibility mapping. In the model studies, ten flash flood conditioning factors, namely soil, slope, curvature, river density, flow direction, distance from rivers, elevation, aspect, land use, and geology, were chosen based on topography and geo-environmental conditions of the site. For the validation of models, the area under Receiver Operating Characteristic (ROC), Area Under Curve (AUC), and various statistical indices were used. The results indicated that performance of all the models is good for generating flash flood susceptibility maps (AUC = 0.983–0.988). However, performance of LMT model is the best among the four methods (LMT: AUC = 0.988; KLR: AUC = 0.985; RBFC: AUC = 0.984; and NBM: AUC = 0.983). The present study would be useful for the construction of accurate flash flood susceptibility maps with the objectives of identifying flood-susceptible areas/zones for proper flash flood risk management.

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GIS Based Hybrid Computational Approaches for Flash Flood Susceptibility Assessment

Water ◽

10.3390/w12030683 ◽

2020 ◽

Vol 12 (3) ◽

pp. 683 ◽

Cited By ~ 18

Author(s):

Binh Thai Pham ◽

Mohammadtaghi Avand ◽

Saeid Janizadeh ◽

Tran Van Phong ◽

Nadhir Al-Ansari ◽

...

Keyword(s):

Land Use ◽

Feature Selection ◽

Decision Tree ◽

Flash Flood ◽

Feature Selection Method ◽

Flash Floods ◽

Computational Approaches ◽

Flood Susceptibility ◽

Statistical Measures ◽

Susceptibility Maps

Flash floods are one of the most devastating natural hazards; they occur within a catchment (region) where the response time of the drainage basin is short. Identification of probable flash flood locations and development of accurate flash flood susceptibility maps are important for proper flash flood management of a region. With this objective, we proposed and compared several novel hybrid computational approaches of machine learning methods for flash flood susceptibility mapping, namely AdaBoostM1 based Credal Decision Tree (ABM-CDT); Bagging based Credal Decision Tree (Bag-CDT); Dagging based Credal Decision Tree (Dag-CDT); MultiBoostAB based Credal Decision Tree (MBAB-CDT), and single Credal Decision Tree (CDT). These models were applied at a catchment of Markazi state in Iran. About 320 past flash flood events and nine flash flood influencing factors, namely distance from rivers, aspect, elevation, slope, rainfall, distance from faults, soil, land use, and lithology were considered and analyzed for the development of flash flood susceptibility maps. Correlation based feature selection method was used to validate and select the important factors for modeling of flash floods. Based on this feature selection analysis, only eight factors (distance from rivers, aspect, elevation, slope, rainfall, soil, land use, and lithology) were selected for the modeling, where distance to rivers is the most important factor for modeling of flash flood in this area. Performance of the models was validated and compared by using several robust metrics such as statistical measures and Area Under the Receiver Operating Characteristic (AUC) curve. The results of this study suggested that ABM-CDT (AUC = 0.957) has the best predictive capability in terms of accuracy, followed by Dag-CDT (AUC = 0.947), MBAB-CDT (AUC = 0.933), Bag-CDT (AUC = 0.932), and CDT (0.900), respectively. The proposed methods presented in this study would help in the development of accurate flash flood susceptible maps of watershed areas not only in Iran but also other parts of the world.

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Combination of machine learning model (LR-FR) for flash flood susceptibility assessment in Dawuan Sub watershed Mojokerto Regency, East Java

IOP Conference Series Earth and Environmental Science ◽

10.1088/1755-1315/739/1/012017 ◽

2021 ◽

Vol 739 (1) ◽

pp. 012017

Author(s):

L Y Irawan ◽

Sumarmi ◽

D Panoto ◽

Nabila ◽

I H Pradana ◽

...

Keyword(s):

Machine Learning ◽

Flash Flood ◽

Learning Model ◽

Susceptibility Assessment ◽

Flood Susceptibility ◽

Machine Learning Model

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Flood Spatial Modeling in Northern Iran Using Remote Sensing and GIS: A Comparison between Evidential Belief Functions and Its Ensemble with a Multivariate Logistic Regression Model

Remote Sensing ◽

10.3390/rs11131589 ◽

2019 ◽

Vol 11 (13) ◽

pp. 1589 ◽

Cited By ~ 43

Author(s):

Duie Tien Bui ◽

Khabat Khosravi ◽

Himan Shahabi ◽

Prasad Daggupati ◽

Jan F. Adamowski ◽

...

Keyword(s):

Logistic Regression ◽

Vegetation Index ◽

Slope Angle ◽

Training Data ◽

Multivariate Logistic Regression Model ◽

Success Rates ◽

Topographic Wetness Index ◽

Validation Data ◽

Flood Susceptibility ◽

Susceptibility Maps

Floods are some of the most dangerous and most frequent natural disasters occurring in the northern region of Iran. Flooding in this area frequently leads to major urban, financial, anthropogenic, and environmental impacts. Therefore, the development of flood susceptibility maps used to identify flood zones in the catchment is necessary for improved flood management and decision making. The main objective of this study was to evaluate the performance of an Evidential Belief Function (EBF) model, both as an individual model and in combination with Logistic Regression (LR) methods, in preparing flood susceptibility maps for the Haraz Catchment in the Mazandaran Province, Iran. The spatial database created consisted of a flood inventory, altitude, slope angle, plan curvature, Topographic Wetness Index (TWI), Stream Power Index (SPI), distance from river, rainfall, geology, land use, and Normalized Difference Vegetation Index (NDVI) for the region. After obtaining the required information from various sources, 151 of 211 recorded flooding points were used for model training and preparation of the flood susceptibility maps. For validation, the results of the models were compared to the 60 remaining flooding points. The Receiver Operating Characteristic (ROC) curve was drawn, and the Area Under the Curve (AUC) was calculated to obtain the accuracy of the flood susceptibility maps prepared through success rates (using training data) and prediction rates (using validation data). The AUC results indicated that the EBF, EBF from LR, EBF-LR (enter), and EBF-LR (stepwise) success rates were 94.61%, 67.94%, 86.45%, and 56.31%, respectively, and the prediction rates were 94.55%, 66.41%, 83.19%, and 52.98%, respectively. The results showed that the EBF model had the highest accuracy in predicting flood susceptibility within the catchment, in which 15% of the total areas were located in high and very high susceptibility classes, and 62% were located in low and very low susceptibility classes. These results can be used for the planning and management of areas vulnerable to floods in order to prevent flood-induced damage; the results may also be useful for natural disaster assessment.

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Spatiotemporal Characteristics and Driving Force Analysis of Flash Floods in Fujian Province

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi9020133 ◽

2020 ◽

Vol 9 (2) ◽

pp. 133 ◽

Cited By ~ 3

Author(s):

Junnan Xiong ◽

Quan Pang ◽

Chunkun Fan ◽

Weiming Cheng ◽

Chongchong Ye ◽

...

Keyword(s):

Driving Forces ◽

Flash Flood ◽

Principal Component ◽

Flash Floods ◽

Fujian Province ◽

Spatiotemporal Characteristics ◽

Middle Mountains ◽

Driving Force Analysis ◽

Economic Development Model ◽

The Impact

Flash floods are one of the most destructive natural disasters. The comprehensive identification of the spatiotemporal characteristics and driving factors of a flash flood is the basis for the scientific understanding of the formation mechanism and the distribution characteristics of flash floods. In this study, we explored the spatiotemporal patterns of flash floods in Fujian Province from 1951 to 2015. Then, we analyzed the driving forces of flash floods in geomorphic regions with three different grades based on three methods, namely, geographical detector, principal component analysis, and multiple linear regression. Finally, the sensitivity of flash floods to the gross domestic product, village point density, annual maximum one-day precipitation (Rx1day), and annual total precipitation from days > 95th percentile (R95p) was analyzed. The analytical results indicated that (1) The counts of flash floods rose sharply from 1988, and the spatial distribution of flash floods mainly extended from the coastal low mountains, hills, and plain regions of Fujian (IIA2) to the low-middle mountains, hills, and valley regions in the Wuyi mountains (IIA4) from 1951 to 2015. (2) From IIA2 to IIA4, the impact of human activities on flash floods was gradually weakened, while the contribution of precipitation indicators gradually strengthened. (3) The sensitivity analysis results revealed that the hazard factors of flash floods in different periods and regions had significant differences in Fujian Province. Based on the above results, it is necessary to accurately forecast extreme precipitation and improve the economic development model of the IIA2 region.

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Exploring the Impact of Multitemporal DEM Data on the Susceptibility Mapping of Landslides

Applied Sciences ◽

10.3390/app10072518 ◽

2020 ◽

Vol 10 (7) ◽

pp. 2518 ◽

Cited By ~ 2

Author(s):

Jiaying Li ◽

Weidong Wang ◽

Zheng Han ◽

Yange Li ◽

Guangqi Chen

Keyword(s):

Logistic Regression ◽

Temporal Variation ◽

Landscape Change ◽

Logistic Regression Model ◽

Susceptibility Mapping ◽

Susceptibility Assessment ◽

Susceptibility Maps ◽

Digital Elevation ◽

The Impact ◽

Topographic Variation

Digital elevation models (DEMs) are fundamental data models used for susceptibility assessment of landslides. Due to landscape change and reshaping processes, a DEM can show obvious temporal variation and has a significant influence on assessment results. To explore the impact of DEM temporal variation on hazard susceptibility, the southern area of Sichuan province in China is selected as a study area. Multitemporal DEM data spanning over 17 years are collected and the topographic variation of the landscape in this area is investigated. Multitemporal susceptibility maps of landslides are subsequently generated using the widely accepted logistic regression model (LRM). A positive correlation between the topographic variation and landslide susceptibility that was supported by previous studies is quantitatively verified. The ratio of the number of landslides to the susceptibility level areas (RNA) in which the hazards occur is introduced. The RNA demonstrates a general decrease in the susceptibility level from 2000 to 2009, while the ratio of the decreased level is more than fifteen times greater than that of the ratio of the increased level. The impact of the multitemporal DEM on susceptibility mapping is demonstrated to be significant. As such, susceptibility assessments should use DEM data at the time of study.

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Flash flood susceptibility assessment using the parameters of drainage basin morphometry in SE Bangladesh

Quaternary International ◽

10.1016/j.quaint.2020.04.047 ◽

2020 ◽

Cited By ~ 1

Author(s):

Akhtar Alam ◽

Bayes Ahmed ◽

Peter Sammonds

Keyword(s):

Drainage Basin ◽

Flash Flood ◽

Susceptibility Assessment ◽

Flood Susceptibility ◽

Basin Morphometry

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Flash Flood Susceptibility Evaluation in Human-Affected Areas Using Geomorphological Methods—The Case of 9 August 2020, Euboea, Greece. A GIS-Based Approach

GeoHazards ◽

10.3390/geohazards2040020 ◽

2021 ◽

Vol 2 (4) ◽

pp. 366-382

Author(s):

Anna Karkani ◽

Niki Evelpidou ◽

Maria Tzouxanioti ◽

Alexandros Petropoulos ◽

Nicoletta Santangelo ◽

...

Keyword(s):

Debris Flows ◽

Flash Flood ◽

Flash Floods ◽

Alluvial Fans ◽

Field Observations ◽

The Road ◽

Flood Susceptibility ◽

High Hazard ◽

Flooding Events ◽

Very High

Flash floods occur almost exclusively in small basins, and they are common in small Mediterranean catchments. They pose one of the most common natural disasters, as well as one of the most devastating. Such was the case of the recent flood in Euboea island, in Greece, in August 2020. A field survey was accomplished after the 2020 flash floods in order to record the main impacts of the event and identify the geomorphological and man-made causes. The flash flood susceptibility in the urbanized alluvial fans was further assessed using a Geographic Information System (GIS)-based approach. Our findings suggest that a large portion of the alluvial fans of Politika, Poros and Mantania streams are mainly characterized by high and very high hazard. In fact, ~27% of the alluvial fans of Politika and Poros streams are characterized with very high susceptibility, and ~54% of Psachna area. GIS results have been confirmed by field observations after the 2020 flash flood, with significant damages noted, such as debris flows and infrastructure damages, in buildings, bridges and the road networks. In addition, even though the adopted approach may be more time-consuming in comparison to purely computational methods, it has the potential of being more accurate as it combines field observations and the effect of past flooding events.

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Factoring Multi-Hazard Risk Perception in Risk Assessment and Reduction Measures in Landslide and Flash Flood Prone Areas – A Case Study of Sichon District, Nakhon Si Thammarat Province, Thailand

Journal of Disaster Research ◽

10.20965/jdr.2021.p0571 ◽

2021 ◽

Vol 16 (4) ◽

pp. 571-578

Author(s):

Indrajit Pal ◽

Jessada Karnjana ◽

◽

Keyword(s):

Risk Perception ◽

Risk Perceptions ◽

Flash Flood ◽

Pearson Correlation ◽

Flash Floods ◽

Qualitative Assessment ◽

Government Officials ◽

Household Level ◽

Degree Of Risk ◽

Relationship Of

This study’s purpose is to analyze the degree of risk and vulnerability involved in landslide and flash flood prone community areas in Thepparat sub-district, Sichon district, Nakhon Si Thammarat province, Thailand. It also aims to analyze and understand the socio-economic impacts on the community at the household level, and assess the community’s risk and vulnerability by examining its risk perception. The risk perception was done using focus group discussions and a questionnaire survey with key stakeholders. It mainly focused on how the risk of landslides and flash floods influences the community’s risk perceptions, which was tested in two parts: at the organizational and community levels by focusing on government officials and households, respectively. A correlation matrix was used to understand the relationship of the indicators selected. The Pearson correlation result has shown that the degree of risk awareness positively correlates with the income level, education level, and controllability, signifying that the risk of landslides and flash floods influences household risk perceptions. The qualitative assessment recommends community-level preparedness as being paramount to reduce the risk for a resilient community.

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Frequency Ratio Density, Logistic Regression and Weights of Evidence Modelling for Landslide Susceptibility Assessment and Mapping in Yanase and Naka Catchments of Southeast Shikoku, Japan

10.21203/rs.3.rs-37349/v2 ◽

2020 ◽

Author(s):

Matebie Meten ◽

Netra Prakash Bhandary

Keyword(s):

Logistic Regression ◽

Landslide Susceptibility ◽

Frequency Ratio ◽

Weights Of Evidence ◽

Annual Rainfall ◽

Slope Aspect ◽

Susceptibility Assessment ◽

Landslide Susceptibility Assessment ◽

Susceptibility Maps ◽

Landslide Susceptibility Maps

Abstract Landslide susceptibility assessment is an important tool for disaster management and development activities. Shikoku Island in the southwest Japan is one of the most landslide prone areas due to heavy typhoon rainfall, complex geology and the presence of mountainous areas and low topographic features (valleys).Yanase and Naka Catchments of Shikoku Island in Japan were chosen as a study area. The objective of this study is to apply Frequency Ratio Densisty (FRD), Logistic Regression (LR) and Weights of Evidence (WoE) models in a GIS environment to prepare the landslide susceptibility maps of this area and select the best one for future infrastructure and landuse planning. Data layers including slope, aspect, profile curvature, plan curvature, lithology, land use, distance from river, distance from fault and annual rainfall were used in this study. In FR method, two models were attempted but the FRD model was found slightly better in its performance. In case of LR method, two models, one with equal proportion and the other with unequal proportion of landslide and non-landslide points were applied and the one with equal proportions was chosen based on its highest performance. A total of five landslide susceptibility maps(LSMs) were produced using FR, LR and WoE models resulting two, two and one LSMs respectively. However, one best model was chosen from the FR and LR methods based on the highest area under the curve (AUC) of the receiver operating characteristic (ROC) curves. This reduced the total number of landslide susceptibility maps to three with the success rates of 86.7%, 86.8% and 80.7% from FRD, LR and WoE models respectively. For validation purpose, all landslides were overlaid over the three landslide susceptibility maps and the percentage of landslides in each susceptibility class was calculated. The percentages of landslides that fall in the high and very high susceptibility classes of FRD, LR and WoE models showed 82%, 84% and 78% respectively. This showed that the LR model with equal proportions of landslides and non-landslide points was slightly better than FRD and WoE models in predicting the probability of future landslide occurrence.

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