Flood-Prone Area Assessment Using GIS-Based Multi-Criteria Analysis: A Case Study in Davao Oriental, Philippines

Cabrera, Jonathan Salar; Lee, Han Soo

doi:10.3390/w11112203

Cited by 75 publications

(45 citation statements)

References 46 publications

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“…the algorithms (Cabrera and Lee 2019). In this article, the ROC curve will explain the ability of the algorithms to exactly forecast the incidence of the landslide history.…”

Section: Validation Indicatorsmentioning

confidence: 99%

Exploring novel hybrid soft computing models for landslide susceptibility mapping in Son La hydropower reservoir basin

Dũng

Hiếu

Phong

et al. 2021

Geomatics, Natural Hazards and Risk

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In this study, two novel hybrid models namely Bagging-based Rough Set (BRS) and AdaBoost-based Rough Set (ABRS) were used to generate landslide susceptibility maps of Son La hydropower reservoir basin, Vietnam. In total, 186 past landslide events and twelve landslides affecting factors (slope degree, slope aspect, elevation, curvature, focal flow, river density, rainfall, aquifer, weathering crust, lithology, fault density and road density) were considered in the modeling study. The landslide data was split into training (70%) and testing (30%) for the model's development and validation. One R feature selection method was used to select and prioritize the landslide affecting factors based on their importance in model prediction. Performance of the hybrid developed models was evaluated and also compared with single rough set (RS) and support vector machine (SVM) models using various standard statistical measures including area under the curve (AUC)-receiver operating characteristics (ROC) curve. The results show that the developed hybrid model BRS (AUC ¼ 0.845) is the most accurate model in comparison to other models (ABRS, SVM and RS) in predicting landslide susceptibility. Therefore, the BRS model can be used as an effective tool in the development of an accurate landslide susceptibility map of the hilly area.

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“…the algorithms (Cabrera and Lee 2019). In this article, the ROC curve will explain the ability of the algorithms to exactly forecast the incidence of the landslide history.…”

Section: Validation Indicatorsmentioning

confidence: 99%

Exploring novel hybrid soft computing models for landslide susceptibility mapping in Son La hydropower reservoir basin

Dũng

Hiếu

Phong

et al. 2021

Geomatics, Natural Hazards and Risk

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show abstract

“…To evaluate the flood risk under climate change scenarios in the short term (2020-2030), medium term (2050-2060), and long term (2090-2100), the population is also projected according to each period. Cabrera and Lee [12] stated that population settlements and the drastic increase in populations are among the factors that intensify the risk of floods. In this study, the arithmetic increase method (AIM) was used to calculate the future population projections.…”

Section: Population and Socioeconomic Datamentioning

confidence: 99%

“…The CR is the crucial element generating the hazard index (HI) for the MCDA using the AHP method. However, the AHP method shows some limitations due to the subjectivity in choosing the value of the indicator weighting from the arbitrary judgements of experts [12,14]. According to Saaty [30], this subjectivity problem was reduced by the CR test.…”

Section: Multicriteria Decision Analysis (Mcda)mentioning

confidence: 99%

“…The HI is coherent if the CR is 9.9% or the CR is 0%, but the weights of the criteria vary in every CR generation. Cabrera and Lee [12] presented the sensitivity of HI to varying CR values based on experiments in which the change in CR did not significantly affect the result of the flood hazard map. The overall average change according to the classification is 0.19%, and the changes are acceptable.…”

Section: Multicriteria Decision Analysis (Mcda)mentioning

confidence: 99%

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Impacts of Climate Change on Flood-Prone Areas in Davao Oriental, Philippines

Cabrera

Lee

2018

Water

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This study aims to quantitatively assess the impacts of climate change on the flood-prone risk areas in Davao Oriental, Philippines for the years 2030, 2050, and 2100 in comparison with the present situation by identifying flood risk zones based on multisource data, including rainfall, slope, elevation, drainage density, soil type, distance to the main channel, and population density. The future temperatures and rainfall projections from the Coupled Model Intercomparison Project Phase 5 (CMIP5) predictions of the Intergovernmental Panel on Climate Change (IPCC) were used. The future temperatures from the CMIP5 predictions showed that Davao Oriental should experience approximately 1 • C and 3 • C increases under the Representative Concentration Pathway (RCP)4.5 and RCP8.5 scenarios, respectively, while the rainfall should slightly increase in the coming years. Among the 39 general circulation models (GCMs) available from CMIP5, the GFDL-ESM2M model showed good agreement with the observed rainfall dataset at the local stations. The intensity of rainfall should increase approximately 69% in the future, resulting in an increase in the magnitude of the floods. The resulting flood risk map shows that 95.91% of Davao Oriental is presently under the low and moderate flood risk categories, and those categories should slightly decrease to 95.75% in the future. The high and very high flood risk areas cover approximately 3% of the province at present and show no dramatic change in the future. Presently, 28 out of the 183 barangays (towns) are at high and very high risks of floods, whereas in the coming years, only one barangay will be added to the very high risk of floods. These barangays under the high and very high categories of flood risk are primarily situated on riversides and coastal areas. Thus, immediate actions from decision-makers are needed to develop a community-based disaster risk plan under the future conditions.

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“…Floods are a frequent problem and occur annually in watershed areas. Factors affecting flooding often include heavy rains over a long time period, since the monsoon troughs run across this watershed area (Papaioannou et al, 2015;Şarpe & Haidu, 2017;Cabrera & Lee, 2020). Terrain with inefficient drainage is also one of the top factors affecting flooding, such as a river with a large amount of sediment, a curved river with few branches, etc.…”

Section: Introductionmentioning

confidence: 99%

Flood Prone Risk Area Analysis During 2005 – 2019 in Lam Se Bok Watershed, Ubon Ratchathani Province, Thailand

Waiyasusri¹,

Kulpanich²,

Worachairungreung³

et al. 2021

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This research investigates the application of logistic regression analysis for flood prone risk mapping in the Lam Se Bok watershed area. The study found that floods have occurred as many as 15 times since 2005. In 2019, flooding covered 200.01 km 2 of the watershed (5.51% of the total watershed). Among the areas that flood every year, 15 floods occurred in the lower part of the LSBW basin in Na Udom village, Khok Sawang and Fa Huan village, Rai Khi sub-district, which are in the south of Lue Amnat District, Amnat Charoen Province, as well as in parts of Dum Yai sub-district, Muang Sam Sip district, Ubon Ratchathani. Logistic regression analysis was used to determine the influence of certain variables on this flooding. The variables showing positive β values were mean annual precipitation and distance to a road. The variables showing negative β values included elevation, terrain, slope, soil drainage, distance to stream, land-use, and distance to village, respectively. All of these variables can be analyzed for their Flood Prone Risk area in GIS. The study found that floodprone areas at the very high-level flood prone risk areas, with a total area of 638.59 km 2 (17.59%), high level flood prone risk areas cover an area of 1,848.10 km2 (50.92%). Medium flood prone risk areas cover 794.95 km2 (21.90%). Low flood prone risk areas cover 310.86 km2 (8.56%), the least vulnerable to flooding encompassed 46.35 km2 (1.27%)., and occurred in areas with low elevation and areas with high annual average rainfall when the variable was located in the middle and downstream parts of the LSBW river basin.

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Flood-Prone Area Assessment Using GIS-Based Multi-Criteria Analysis: A Case Study in Davao Oriental, Philippines

Cited by 75 publications

References 46 publications

Exploring novel hybrid soft computing models for landslide susceptibility mapping in Son La hydropower reservoir basin

Exploring novel hybrid soft computing models for landslide susceptibility mapping in Son La hydropower reservoir basin

Impacts of Climate Change on Flood-Prone Areas in Davao Oriental, Philippines

Flood Prone Risk Area Analysis During 2005 – 2019 in Lam Se Bok Watershed, Ubon Ratchathani Province, Thailand

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