Hybrid-based approaches for the flood susceptibility prediction of Kermanshah province, Iran

Paryani, Sina; Bordbar, Mojgan; Jun, Changhyun; Panahi, Mahdi; Bateni, Sayed M.; Neale, Christopher M. U.; Moeini, Hamidreza; Lee, Saro

doi:10.1007/s11069-022-05701-4

Cited by 5 publications

(3 citation statements)

References 119 publications

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“…Popular optimization algorithms such as particle swarm optimization (PSO) 35 , genetic algorithm (GA) 36 , and differential evolution (DE) 37 have been extensively used for optimizing the computational parameters of machine learning models which are responsible for tuning the LCFs-landslide relationships. Newer generations of optimization algorithms consist of Harris hawks optimization (HHO) 38 , salp swarm algorithm (SSA) 39 , cuckoo optimization algorithm (COA) 40 , Satin bowerbird optimizer (SBO) 41 , teaching–learning-based optimization (TLBO) 42 , biogeography-based optimization (BBO) 43 , etc. which have served for landslide susceptibility mapping worldwide.…”

Section: Introductionmentioning

confidence: 99%

Fast hybrid methods for modeling landslide susceptibility in Ardal County

2024

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Recently, machine learning models have received huge attention for environmental risk modeling. One of these applications is landslide susceptibility mapping which is a necessary primary step for dealing with the landslide risk in prone areas. In this study, a conventional machine learning model called multi-layer perceptron (MLP) neural network is built upon advanced optimization algorithms to achieve a firm prediction of landslide susceptibility in Ardal County, West of Iran. The used geospatial dataset consists of fourteen conditioning factors and 170 landslide events. The used optimizers are electromagnetic field optimization (EFO), symbiotic organisms search (SOS), shuffled complex evolution (SCE), and electrostatic discharge algorithm (ESDA) that contribute to tuning MLP’s internal parameters. The competency of the models is evaluated using several statistical methods to provide a comparison among them. It was discovered that the EFO-MLP and SCE-MLP enjoy much quicker training than SOS-MLP and ESDA-MLP. Further, relying on both accuracy and time criteria, the EFO-MLP was found to be the most efficient model (time = 1161 s, AUC = 0.879, MSE = 0.153, and R = 0.657). Hence, the landslide susceptibility map of this model is recommended to be used by authorities to provide real-world protective measures within Ardal County. For helping this, a random forest-based model showed that Elevation, Lithology, and Land Use are the most important factors within the studied area. Lastly, the solution discovered in this study is converted into an equation for convenient landslide susceptibility prediction.

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Section: Introductionmentioning

confidence: 99%

Fast hybrid methods for modeling landslide susceptibility in Ardal County

2024

Sci Rep

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“…Paryani et al. (2022) optimized the Support Vector Regression model using four metaheuristic methods (Harris HAWK Optimization, Particle Swarm Optimization, Gray Wolf Optimizer [GWO], and Bat Algorithm [BA]) to generate flood susceptibility maps for Kermanshah Province, Iran. Ahmadlou et al.…”

Section: Introductionmentioning

confidence: 99%

“…For example, Hong, Panahi, et al (2018) found that the standalone Adaptive Neuro-Fuzzy Inference System (ANFIS) model was outperformed by ANFIS integrated with Genetic Algorithm and Differential Evolution metaheuristic algorithms for spatial flood modeling in China. Paryani et al (2022) optimized the Support Vector Regression model using four metaheuristic methods (Harris HAWK Optimization, Particle Swarm Optimization, Gray Wolf Optimizer [GWO], and Bat Algorithm [BA]) to generate flood susceptibility maps for Kermanshah Province, Iran. Ahmadlou et al (2019) used an optimized ANFIS model with Biogeography-Based Optimization (BBO) and BA algorithms for spatial flood modeling in Iran, finding the BBO model to be superior to other algorithms.…”

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confidence: 99%

A Country Wide Evaluation of Sweden's Spatial Flood Modeling With Optimized Convolutional Neural Network Algorithms

Panahi,

Khosravi,

Rezaie

et al. 2023

Earth's Future

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Flooding is one of the most serious and frequent natural hazards affecting human life, property, and the environment. This study develops and tests a deep learning approach for large‐scale spatial flood modeling, using Convolutional Neural Network (CNN) and optimized versions combined with the Gray Wolf Optimizer (GWO) or the Imperialist Competitive Algorithm (ICA). With Sweden as an application case for nation‐wide flood susceptibility mapping, this modeling approach considers ten geo‐environmental input factors (slope, elevation, aspect, plan curvature, length of slope, topographic wetness index, distance from river, distance from wetland, rainfall, and land use). The GWO and ICA optimization improves model prediction by 12% and 8%, respectively, compared with the standalone CNN model performance. The results show 40% of the land area, 45% of the railroad, and 43% of the road network of Sweden to have high or very high flood susceptibility. They also show the aspect to have the highest input factor impact on flood susceptibility prediction while, for example, rainfall ranks only seven of the total 10 considered geo‐environmental input factors. In general, accurate nation‐wide flood susceptibility prediction is essential for guiding flood management and mitigation efforts. This study's approach to such prediction has emerged as well‐performing and cost‐effective for the case of Sweden, calling for further application and testing in other world regions.

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