Assessment of data mining, multi-criteria decision making and fuzzy-computing techniques for spatial flood susceptibility mapping: a comparative study

Balogun, Abdul‐Lateef; Sheng, Tan Yong; Sallehuddin, Muhammad Helmy; Aina, Yusuf A.; Dano, Umar Lawal; Pradhan, Biswajeet; Yekeen, Shamsudeen Temitope; Tella, Abdulwaheed

doi:10.1080/10106049.2022.2076910

Cited by 14 publications

(6 citation statements)

References 103 publications

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“…The ANP method is simple, realistic, flexible, time-consuming, and cost-effective in use and it could create transparency and responsibility in decision procedure [91]. Balougn et al [92] claimed that the ANP cannot well model comparison judgments because of uncertainty in the human preference model. Moreover, its applicability by combining with Fuzzy logic, FLANN, has been reported by Alilou et al [93]in the evaluation of watershed health.…”

Section: Discussionmentioning

confidence: 99%

Spatial Prediction of Landslide using Hybrid Multi-Criteria Decision-Making Methods: A Case Study, Saqqez-Marivan Mountain Road

Shahabi¹,

Tavakolifar²,

Alizadeh³

et al. 2023

Preprint

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Landslides around the main roads in the mountains not only cause fatal events but also cause ecosystem damage, including land degradation. This study aims to map the susceptibility of the landslides around the Saqqez-Marivan main rod of Kurdistan province, Iran, using ensemble Fuzzy logic with Analytic Network Process (Fuzzy Logic-ANP; FLANP), and with TOPSIS (Fuzzy Logic-TOPSIS; FLTOPSIS). A total of 100 landslides were first recognized by field surveys and then they were randomly divided into a 70% dataset (70 locations) and a 30% dataset (30 locations), respectively, for training and validating the methods. Eleven landslide conditioning factors, including slope, aspect, elevation, lithology, land use, distance to fault, distance to a river, distance to road, soil type, curvature, and precipitation were used. The performance of the methods was checked by the areas under the receiver operating curve (AUCROC). Results concluded that the prediction accuracy based on validating datasets were, respectively, 0.882 and 0.918 for FLANP and FLTOPSIS methods. Our findings demonstrated that although both models were known as promising techniques, the FLTOPSIS method had a better capacity for predicting the susceptibility of landslides in the studied area. Therefore, the susceptibility map developed by the FLTOPSIS method can be used for the proper management of areas with high landslide potential and also for managers and planners during the implementation of land allocation and development projects, especially in mountainous areas.

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Section: Discussionmentioning

confidence: 99%

Spatial Prediction of Landslide using Hybrid Multi-Criteria Decision-Making Methods: A Case Study, Saqqez-Marivan Mountain Road

Shahabi¹,

Tavakolifar²,

Alizadeh³

et al. 2023

Preprint

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“…The most crucial phase in creating the final flood susceptibility maps is the choice of the flood influencing factors, also called conditioning factors, which have a critical effect on the output maps (Shafapour Tehrany et al 2019). Therefore, the most pertinent and frequently used flood conditioning factors by other researchers (Tehrany et al 2014;Rahmati et al 2016;Balogun et al 2022;Tella and Balogun 2020) were used in this study, even though there is still no universal approach or consensus on how to choose these factors. The complex process of flood susceptibility modelling necessitates the identification of various flood conditioning factors.…”

Section: Flood Conditioning Factorsmentioning

confidence: 99%

“…There exist different techniques to map flood-prone areas. The four main categories of the most popular techniques are hydrologically based (Farooq et al 2019;Loi et al 2019), quantitative (Arora et al 2021;Cao et al 2016), qualitative (Balogun et al 2022;Tella and Balogun 2020), and machine learning techniques (Khosravi et al 2019;Rahman et al 2019). The most widely used methods for mapping flooding include artificial neural networks (ANNs), frequency ratio (FR), logistic regression (LR), decision trees (DT), and support vector machines (SVMs) (Mojaddadi et al 2017;Tehrany et al 2015).…”

Section: Introductionmentioning

confidence: 99%

Spatial Prediction of Flood in Kuala Lumpur City of Malaysia Using Logistic Regression

Tella

Mustafa²,

Balogun³

et al. 2023

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Flooding is one of the most prevalent natural disasters affecting people worldwide. Flooding is a devastating natural disaster in Malaysia regarding the number of people affected, socioeconomic damage, severity, and scale of the impact. Urban flooding is currently a major concern due to the possible consequences and frequency with which it occurs in urban areas as urbanization and population increase. Due to the paved surfaces, paved roads, high population, and buildings that prevent water infiltration and movement to the nearby river, urban floods pose a significant threat to the sustainability of lives and properties in the city. The recent floods in Kuala Lumpur in December 2021 and January 2022 affected many buildings, infrastructure, and lives. As a result, this city needs to model the susceptibility of flood-prone areas for an early warning system against future flood hazards in Kuala Lumpur. This is because flooding can never be eradicated but can be minimized and managed. Therefore, this study integrates geospatial technology and a statistical model (logistic regression) to assess flood hazards in Kuala Lumpur. Ten flood conditioning factors such as altitude, slope, TWI, drainage density, distance to river, LULC, NDVI, NDWI, rainfall and MNDWI were used to predict the areas susceptible to flood. The prediction shows an overall accuracy of 0.84, precision of 0.91, recall of 0.72, and F1-score of 0.80. Distance to river, MNDWI, TWI, and LULC are the critical variables that showed high significance in the model prediction. Thus, stakeholders should prioritize urban planning and increase the drainage system to avoid flood effects.

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“…To assess flood hazards over the past few decades, conventional optimization approaches like linear, nonlinear, and dynamic programming have been used (Rather et al 2022;Meraj et al 2022a). Moreover, frequency ratio (FR) (Rahman et al 2019;Sarkar and Mondal 2020;Ghosh and Dey 2021), Multi-Criteria Decision Making (MCDM) (Das 2020;Das and Gupta 2021;Mitra et al 2022;Gupta and Dixit 2022) and fuzzy logic (FL) (Ghosh and Dey 2021;Akay 2021;Balogun et al 2022) models are also considered for the flood hazard assessment analyses. These studies demonstrate that flood risks are linked to multidimensional attributes and incorporate spatial elements.…”

Section: Introductionmentioning

confidence: 99%

Evaluating Flood Susceptibility in the Brahmaputra River Basin: An Insight into Asia's Eastern Himalayan Floodplains Using Machine Learning and Multi-Criteria Decision-Making

Debnath,

Sahariah,

Mazumdar

et al. 2023

Earth Syst Environ

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Floods represent a significant threat to human life, property, and agriculture, especially in low-lying floodplains. This study assesses flood susceptibility in the Brahmaputra River basin, which spans China, India, Bhutan, and Bangladesh—an area notorious for frequent flooding due to the saturation of river water intake capacity. We developed and evaluated several innovative models for predicting flood susceptibility by employing Multi-Criteria Decision Making (MCDM) and Machine Learning (ML) techniques. The models showed robust performance, evidenced by Area Under the Receiver Operating Characteristic Curve (AUC-ROC) scores exceeding 70% and Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE) scores below 30%. Our findings indicate that approximately one-third of the studied region is categorized as moderately to highly flood-prone, while over 40% is classified as low to very low flood-risk areas. Specific regions with high to very high flood susceptibility include Dhemaji, Dibrugarh, Lakhimpur, Majuli, Darrang, Nalbari, Barpeta, Bongaigaon, and Dhubri districts in Assam; Coochbihar and Jalpaiguri districts in West Bengal; and Kurigram, Gaibandha, Bogra, Sirajganj, Pabna, Jamalpur, and Manikganj districts in Bangladesh. Owing to their strong performance and the suitability of the training datasets, we recommend the application of the developed MCDM techniques and ML algorithms in geographically similar areas. This study holds significant implications for policymakers, regional administrators, environmentalists, and engineers by informing flood management and prevention strategies, serving as a climate change adaptive response within the Brahmaputra River basin.

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Assessment of data mining, multi-criteria decision making and fuzzy-computing techniques for spatial flood susceptibility mapping: a comparative study

Cited by 14 publications

References 103 publications

Spatial Prediction of Landslide using Hybrid Multi-Criteria Decision-Making Methods: A Case Study, Saqqez-Marivan Mountain Road

Spatial Prediction of Landslide using Hybrid Multi-Criteria Decision-Making Methods: A Case Study, Saqqez-Marivan Mountain Road

Spatial Prediction of Flood in Kuala Lumpur City of Malaysia Using Logistic Regression

Evaluating Flood Susceptibility in the Brahmaputra River Basin: An Insight into Asia's Eastern Himalayan Floodplains Using Machine Learning and Multi-Criteria Decision-Making

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