An Integration of Least Squares Support Vector Machines and Firefly Optimization Algorithm for Flood Susceptible Modeling Using GIS

Nguyen, Viet-Nghia; Bui, Dieu Tien; Ngo, Phuong-Thao Thi; Nguyen, Quoc-Phi; Nguyen, Van Cam; Long, Nguyễn Quốc; Revhaug, Inge

doi:10.1007/978-3-319-68240-2_4

Cited by 5 publications

(5 citation statements)

References 26 publications

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“…• Evolution-based, including the genetic algorithm (GA) (Dodangeh et al, 2020;Hong et al, 2018), evolution strategies (Dodangeh et al, 2020), and differential evolution (DE) (Hong et al, 2018) • Physics-based, including simulated annealing (Harada et al, 2016) and wind-driven optimization (Liu et al, 2019) • Swarm-based, including ant colony optimization (Lai et al, 2016), the firefly algorithm (Nguyen et al, 2017;Nhu et al, 2020), grasshopper optimization algorithm (Ruidas et al, 2022), and particle swarm optimization (PSO) (Sachdeva et al, 2017) • Human-based, including culture algorithm (Tien Bui et al, 2018) and teaching learning-based optimization (Zamli, 2016) Previous studies have provided different solutions to solving real-life problems, which have fallen into three main categories: modifying current algorithms, hybridizing different algorithms, and proposing new algorithms. All three categories have been proven effective (Abualigah et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

“…Several algorithms have been successfully applied to construct the flood susceptibility map. These have been divided into four main categories: Evolution‐based , including the genetic algorithm (GA) (Dodangeh et al, 2020; Hong et al, 2018), evolution strategies (Dodangeh et al, 2020), and differential evolution (DE) (Hong et al, 2018) Physics‐based , including simulated annealing (Harada et al, 2016) and wind‐driven optimization (Liu et al, 2019) Swarm‐based , including ant colony optimization (Lai et al, 2016), the firefly algorithm (Nguyen et al, 2017; Nhu et al, 2020), grasshopper optimization algorithm (Ruidas et al, 2022), and particle swarm optimization (PSO) (Sachdeva et al, 2017) Human‐based , including culture algorithm (Tien Bui et al, 2018) and teaching learning‐based optimization (Zamli, 2016) …”

Section: Introductionmentioning

confidence: 99%

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Flood susceptibility assessment using hybrid machine learning and remote sensing in Quang Tri province, Vietnam

Nguyen

2022

Transactions in GIS

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Flooding is the most dangerous of all environmental hazards and leads to significant impacts both on the environment and on human life. Environmental protection and water management can be achieved by modeling flood susceptibility, to inform strategy that will reduce flood damage. The aim of this study is to develop novel hybrid models based on a radial basis functions neural network (RBFNN), arithmetic optimization algorithm (AOA), artificial bee colony (ABC), and ant lion optimizer (ALO), to build flood susceptibility maps in the Quang Tri province of Vietnam. The obtained models were trained and validated using 1511 flood points and 14 conditioning factors. Various statistical indices were used to assess the performance of the models, namely root mean square error, receiver operation characteristics (ROC), area under the receiver operating characteristic curve (AUC), and the coefficient of determination (R2). The comparison analysis highlights that the RBFNN‐ABC model was better than other models with an AUC of 0.98, followed by RBFNN‐AOA, support vector machine, and random forest, all with an AUC value of 0.96, and finally RBFNN‐ALO, with an AUC of 0.95. From the flood susceptibility map, it can be seen that the eastern areas of the study area have high and very high flood susceptibility, that requires local government attention. The approach and results of this study can support national and local authorities, decision‐makers, and other planners in the construction of appropriate strategies to reduce potential damage in the future.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Flood susceptibility assessment using hybrid machine learning and remote sensing in Quang Tri province, Vietnam

Nguyen

2022

Transactions in GIS

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“…While the flood locations will be the dependent variable in estimating the flood susceptibility, a number of 12 flood predictors will be used as explanatory variables, and their spatial distribution will be based on the flood exposure values. It is worth stating that the following predictors were selected following a meticulous analysis of the literature (Nguyen et al 2017;Tang et al 2020;Costache et al 2020c): slope, altitude, aspect, TPI, TWI, convergence index, plan curvature, hydrological soil groups, land use, distance from rivers, lithology and rainfall. The first 7 mentioned flood predictors, which are also morphometric indices, were derived from the Digital Elevation Model (DEM) extract from the Shuttle Radar Topographic Mission (SRTM) 30 m. It is worth noting that, at the present moment, for perimeter covered the study area, another DEM with a high resolution of 30 m is not available.…”

Section: Flood Conditioning Factorsmentioning

confidence: 99%

“…Along with its high support for flood and flash-flood early warning systems, the high-accurate estimation of flood exposed areas can also help to draw up the river basins flood defence plans (Albano et al 2017;Johann and Leismann 2017;Brillinger et al 2020). The following models are among the most popular ML algorithms used to estimate the flood susceptibility: Artificial Neural Network (ANN) (Costache et al 2020b), support vector machine (SVM) (Nguyen et al 2017;Tehrany et al 2019;Sahana et al 2020), decision trees-based models (Chen et al 2018;Khosravi et al 2018;Costache 2019c), naïve Bayes (Ali et al 2020;Tang et al 2020), deep learning neural network (Costache et al 2020a), extreme learning machine (Tang et al 2020). All the models applied in the above works achieved an accuracy higher than 80%.…”

Section: Introductionmentioning

confidence: 99%

Detection of areas prone to flood risk using state-of-the-art machine learning models

Costache

Arabameri

Elkhrachy

et al. 2021

Geomatics, Natural Hazards and Risk

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The present study aims to evaluate the susceptibility to floods in the river basin of Buzau in Romania through the following 6 machine learning models: Support Vector Machine (SVM), J48 decision tree, Adaptive Neuro-Fuzzy Inference System (ANFIS), Random Forest (RF), Artificial Neural Network (ANN) and Alternating Decision Tree (ADT). In the first stage of the study, an inventory of the areas affected by floods was made in the study area, and a number of 205 flood points were identified. Further, 12 flood predictors were selected to be used for final susceptibility mapping. The six models' training was performed by using 70% of the total flood points that have been associated with the values of flood predictors. The highest accuracy (0.973) was obtained by the RF model, while J48 had the lowest performance (0.825). Besides, by classifying flood predictors' values in flood and non-flood pixels, the six flood susceptibility maps were made. High and very high flood susceptibility values cover between 17.71% (MLP) and 27.93% (ANFIS) of the study area. The validation of the results, performed using the ROC Curve, shows that the most accurate flood susceptibility values are also assigned to the RF model (AUC ¼ 0.996).

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“…Therefore, using an optimization algorithm to boost the ability of ML models is a beneficial approach for drought forecasting. Firefly optimization algorithm (FA) as one of the nature-inspired algorithms has been successfully contributed to drought forecasting [41], flood studies [42,43], reference evapotranspiration estimation [44,45], rainfall pattern prediction [46], and groundwater studies [47,48]. Previous research has proven the performance of coupled ML techniques for drought modeling, however, the usage of hybrid models based on ANN coupled with the firefly algorithm (ANN-FA) has not yet been explored.…”

Section: Introductionmentioning

confidence: 99%

Modeling Various Drought Time Scales via a Merged Artificial Neural Network with a Firefly Algorithm

Mohammadi

2023

Hydrology

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Drought monitoring and prediction have important roles in various aspects of hydrological studies. In the current research, the standardized precipitation index (SPI) was monitored and predicted in Peru between 1990 and 2015. The current study proposed a hybrid model, called ANN-FA, for SPI prediction in various time scales (SPI3, SPI6, SPI18, and SPI24). A state-of-the-art firefly algorithm (FA) has been documented as a powerful tool to support hydrological modeling issues. The ANN-FA uses an artificial neural network (ANN) which is coupled with FA for Lima SPI prediction via other stations. Through the intelligent utilization of SPI series from neighbors’ stations as model inputs, the suggested approach might be used to forecast SPI at various time scales in a meteorological station with insufficient data. To conduct this, the SPI3, SPI6, SPI18, and SPI24 were modeled in Lima meteorological station using other meteorological stations’ datasets in Peru. Various error criteria were employed to investigate the performance of the ANN-FA model. Results showed that the ANN-FA is an effective and promising approach for drought prediction and also a multi-station strategy is an effective strategy for SPI prediction in the meteorological station with a lack of data. The results of the current study showed that the ANN-FA approach can help to predict drought with the mean absolute error = 0.22, root mean square error = 0.29, the Pearson correlation coefficient = 0.94, and index of agreement = 0.97 at the testing phase of best estimation (SPI3).

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An Integration of Least Squares Support Vector Machines and Firefly Optimization Algorithm for Flood Susceptible Modeling Using GIS

Cited by 5 publications

References 26 publications

Flood susceptibility assessment using hybrid machine learning and remote sensing in Quang Tri province, Vietnam

Flood susceptibility assessment using hybrid machine learning and remote sensing in Quang Tri province, Vietnam

Detection of areas prone to flood risk using state-of-the-art machine learning models

Modeling Various Drought Time Scales via a Merged Artificial Neural Network with a Firefly Algorithm

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