Comparison of analytic network process and artificial neural network models for flash flood susceptibility assessment

Dahri, Noura; Yousfi, Rabiaa; Bouamrane, Ali; Abida, Habib; Pham, Quoc Bao; Derdous, Oussama

doi:10.1016/j.jafrearsci.2022.104576

Cited by 28 publications

(10 citation statements)

References 46 publications

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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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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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“…According to existing studies, machine learning models are widely used in susceptibility prediction modeling due to their powerful ability to handle non-linear data with different scales and from different types of sources (Zhang et al, 2022). Recently, the research field of machine learning models has been rapidly expanding, with models such as multilayer perceptron (MLP) (Haribabu et al, 2021), support vector machine (SVM) (Xiong et al, 2019), logistic regression (LR) (Nguyen and Bouvier, 2019;Huang et al, 2020a), random forest (RF) (Abedi et al, 2022), decision trees (Ngo et al, 2021), and artificial neural networks (Dahri et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

Modeling rules of regional flash flood susceptibility prediction using different machine learning models

Chen

Zhang²,

Yang³

et al. 2023

Front. Earth Sci.

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The prediction performance of several machine learning models for regional flash flood susceptibility is characterized by variability and regionality. Four typical machine learning models, including multilayer perceptron (MLP), logistic regression (LR), support vector machine (SVM), and random forest (RF), are proposed to carry out flash flood susceptibility modeling in order to investigate the modeling rules of different machine learning models in predicting flash flood susceptibility. The original data of 14 environmental factors, such as elevation, slope, aspect, gully density, and highway density, are chosen as input variables for the MLP, LR, SVM, and RF models in order to estimate and map the distribution of the flash flood susceptibility index in Longnan County, Jiangxi Province, China. Finally, the prediction performance of various models and modeling rules is evaluated using the ROC curve and the susceptibility index distribution features. The findings show that: 1) Machine learning models can accurately assess the region’s vulnerability to flash floods. The MLP, LR, SVM, and RF models all predict susceptibility very well. 2) The MLP (AUC=0.973, MV=0.1017, SD=0.2627) model has the best prediction performance for flash flood susceptibility, followed by the SVM (AUC=0.964, MV=0.1090, SD=0.2561) and RF (AUC=0.975, MV=0.2041, SD=0.1943) models, and the LR (AUC=0.882, MV=0.2613, SD=0.2913) model. 3) To a large extent, environmental factors such as elevation, gully density, and population density influence flash flood susceptibility.

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“…In recent years, machine learning based models are well-known as the most advanced quantitative methods for flood susceptibility mapping. Common machine learning based models used for flood susceptibility modeling are Artificial Neural Networks (ANN) [1,2], Support Vector Machines (SVM) [3,4], Random Forest [5,6], K-Nearest Neighbors (KNN) [7,8], Decision Trees based models [9,10], Logistic Regression [11,12], Extreme Gradient Boosting (XGBoost) [5,13]. In general, these mentioned machine learning models performed well for flood susceptibility modeling and mapping at different areas of the world.…”

Section: Introductionmentioning

confidence: 99%

Modeling and Mapping of Flood Susceptibility at Que Son District, Quang Nam Province, Vietnam using CatBoost

Phong,

Nguyen,

Pham

2023

IOP Conf. Ser.: Mater. Sci. Eng.

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In this research, the main objective is to model and map flood susceptibility in Que Son district, Quang Nam province, Vietnam using one of the effective machine learning model namely CatBoost. With this purpose, a total of 96 flood and non-flood locations and a set of 10 conditioning factors were collected to construct the geospatial database. Thereafter, Shap feature importance method was used to validate and select the most important conditioning factors used for modeling of flood susceptibility, and the results showed that only 8 conditioning factors including aspect, slope, curvature, elevation, land cover, rainfall, distance to rivers, and Topographic Wetness Index (TWI) were selected for final modelling of flood susceptibility at the study area. Validation of the model was also done using various statistical indexes including area under the ROC curve (AUC). Validation results showed that the performance of CatBoost model (AUC = 0.96 for training and AUC = 0.94 for testing) is good for prediction of flood susceptibility of the study area. Thus, it can be concluded that CatBoost is valuable tool for flood susceptibility modeling which can be used to assess flood susceptibility in other flood prone areas of the world. In addition, flood susceptibility map generated from CatBoost model in this study might be helpful in development of better flood mitigation strategies at the study area.

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Comparison of analytic network process and artificial neural network models for flash flood susceptibility assessment

Cited by 28 publications

References 46 publications

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

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

Modeling rules of regional flash flood susceptibility prediction using different machine learning models

Modeling and Mapping of Flood Susceptibility at Que Son District, Quang Nam Province, Vietnam using CatBoost

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