Flood hazard mapping and assessment using fuzzy analytic hierarchy process and GIS techniques in Takelsa, Northeast Tunisia

Souissi, Dhekra; Souie, Ali; Sebei, Abdelaziz; Mahfoudhi, Rabeb; Zghibi, Adel; Zouhri, Lahcen; Amiri, Walid; Ghanmi, Mohamed

doi:10.1007/s12517-022-10541-4

Cited by 11 publications

(12 citation statements)

References 86 publications

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“…During the time of precipitation, water goes through the high terrain to the low terrain and exceeds the capacity of the waterbodies, causing a flood. This is one of the common reasons for flooding in other studies as well [ 5 , 7 ]. According to our findings, the northwestern districts of Sunamganj and Hobiganj are especially prone to flooding.…”

Section: Discussionmentioning

confidence: 81%

“…Different methods have been put forth to assess the likelihood of a flood disaster depending on the system's vulnerability and hydrology [ 5 ]. Some studies have used AHP (Analytical Hierarchy Process) to detect flash floods [ 2 , 6 ]; some research has employed the Fuzzy Analytical Hierarchy Process (FAHP) to predict the development of food insecurity [ 7 ]; hydraulic models are carried out in HEC-RAS [ 4 ], Mike Urban [ 8 ] stimulating floods as well as impact assessment using HEC-RAS and comparison with MODIS images [ 9 ]. Research using machine learning techniques has gained popularity in flood prediction and monitoring.…”

Section: Introductionmentioning

confidence: 99%

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Artificial neural network for flood susceptibility mapping in Bangladesh

Rudra

Sarkar

2023

Heliyon

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Section: Discussionmentioning

confidence: 81%

Section: Introductionmentioning

confidence: 99%

Artificial neural network for flood susceptibility mapping in Bangladesh

Rudra

Sarkar

2023

Heliyon

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“…Among software models, HEC‐RAS (Ghimire et al, 2022; Namara et al, 2022; Sejati et al, 2022), HEC‐HMS (Altaf & Romshoo, 2022; Nadeem et al, 2022; Shah & Lone, 2022), MIKEs (Mike11, Mike21, Mike Flood, Mike Urban) (Dikici et al, 2022; Goumas et al, 2022; Li et al, 2022; Zhang et al, 2022; Zhou, Teng, et al, 2022; Zhou, Zheng, et al, 2022), Bentley Open Flow (de Andrade et al, 2022; Ellwood et al, 2022; Khakhar et al, 2022), Flo‐2D (Gerundo et al, 2022; Komolafe, 2022; Liu et al, 2022; Wang, Luo, et al, 2022), and CCHE (Lee et al, 2020; Kakati et al, 2022; Poorzaman et al, 2022) models can be mentioned. Also, the most applied numerical models include artificial neural network (Ahmad et al, 2022; Dahri et al, 2022; Wang, Yang, et al, 2022), random forest (Abedi et al, 2022; El‐Magd & Ahmed, 2022; Ha & Kang, 2022; Zhu & Zhang, 2022), and analytic hierarchy process (Bouamrane et al, 2022; Roy et al, 2023; Souissi et al, 2022; Vilasan & Kapse, 2022).…”

Section: Introductionmentioning

confidence: 99%

Using geomorphologic indicators in preparation for flood zoning and flood risk maps in the Kashafroud basin, Iran

Panahi,

Khodashenas,

Faridhosseini

2024

J Flood Risk Management

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The risk of flooding has become more significant in many parts of the world due to climate change and increased urbanization. Flood has devastating effects on infrastructure, and communities, causing damage to property and loss of life. Simulation of flood extent in a particular area is done by using various mathematical models, hydrologic‐hydraulic models, and datasets. Flood modeling using hydraulic‐hydrological models has many errors due to the lack of hydraulic‐hydrologic data and insufficient statistical period length. This study demonstrates the fact that the geomorphological index (GI) method, which is based on the digital elevation model and requires little hydraulic‐hydrologic data, is an effective method for flood modeling. Flood zoning based on GI was performed within the Kashafroud basin with 25, 100, and 200‐year return periods by using geomorphic flood area (GFA) plugin in QGIS software. The true positive rates were 0.985, 0.989, and 0.992, respectively, which showed the high accuracy of flood zoning based on the GI method. Here proposed method showed that using the GFA plugin offers a good way for the flood risk assessment in a basin with the lack of measured data as an alternative to the hydraulic‐hydrological methods.

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“…LULC affects soil erosion, ooding, sustainable development, and biodiversity (Gajbhiye et al 2015). In northern Tunisia, the hydrological behavior of watersheds is undergoing major changes affecting climate, vegetation cover, surface conditions, and LULC (Chebbi et al 2019; Souissi et al 2022).…”

Section: Introductionmentioning

confidence: 99%

Evaluation of SVM and RF Machine Learning Algorithms in Land Use/Land Cover Change Assessment: Tessa Watershed Case Study (Northwest of Tunisia)

BACCARI,

HAMZA,

SLAMA

et al. 2024

Preprint

Self Cite

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This study aims to highlight the changes in LULC (land use and Land cover) in the Tessa watershed (Northwest of Tunisia) between 1993 and 2023. Remote sensing coupled with geographic information systems (GIS) and statistical analysis, are used. Accuracy metrics make it possible to evaluate the performance of the image classification method, using the calculation of the producer’s accuracy, the user’s accuracy, overall accuracy, and the Kappa coefficient. Two Machine Learning (ML) algorithms related to the supervised classification are used for two Landsat images related to 1993 and 2023: the Support Vector Machine (SVM) and the Random Forest (RF). These algorithms are integrated into the SCP plugin of the QGIS software used in this study. The overall accuracy achieved by applying the SVM algorithm to the Landsat 5 TM image from 1993 is 88.24% with a Kappa value of 0.8, whereas the overall accuracy obtained for the Landsat 8 OLI image from 2023 is 99.4% with a Kappa value of 0.99. By applying the RF algorithm, the overall accuracy obtained for the 1993 Landsat 5 TM image is 86% with a Kappa value of 0.8, while for the 2023 Landsat 8 OLI image, the overall accuracy obtained is 81% with a Kappa value of 0.77. Using the transition matrix, it was possible to detect LULC changes spatiotemporally. A comparison of the classification results obtained from SVM and RF algorithms with ground truth showed that the SVM classifier was more accurate in the study area.

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Flood hazard mapping and assessment using fuzzy analytic hierarchy process and GIS techniques in Takelsa, Northeast Tunisia

Cited by 11 publications

References 86 publications

Artificial neural network for flood susceptibility mapping in Bangladesh

Artificial neural network for flood susceptibility mapping in Bangladesh

Using geomorphologic indicators in preparation for flood zoning and flood risk maps in the Kashafroud basin, Iran

Evaluation of SVM and RF Machine Learning Algorithms in Land Use/Land Cover Change Assessment: Tessa Watershed Case Study (Northwest of Tunisia)

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