Flood susceptibility modeling in Teesta River basin, Bangladesh using novel ensembles of bagging algorithms

Talukdar, Swapan; Ghose, Bonosri; Shahfahad, .; Salam, Roquia; Mahato, Susanta; Pham, Quoc Bao; Linh, Nguyễn Thị Thùy; Costache, Romulus; Avand, Mohammadtaghi

doi:10.1007/s00477-020-01862-5

Cited by 156 publications

(39 citation statements)

References 131 publications

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“…Mathematical theories were the first approaches to flood patterns, mainly using hydrological data. Based on existing data, new models have been created for areas with similar characteristics [107][108][109]. Statistics were used as soon as the databases allowed their processing with good results [110][111][112][113][114].…”

Section: Research Methods and Advances In Flood Researchmentioning

confidence: 99%

An Overview of Flood Risk Analysis Methods

Diaconu

Costache

Popa

2021

Water

Self Cite

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Scientific papers present a wide range of methods of flood analysis and forecasting. Floods are a phenomenon with significant socio-economic implications, for which many researchers try to identify the most appropriate methodologies to analyze their temporal and spatial development. This research aims to create an overview of flood analysis and forecasting methods. The study is based on the need to select and group papers into well-defined methodological categories. The article provides an overview of recent developments in the analysis of flood methodologies and shows current research directions based on this overview. The study was performed taking into account the information included in the Web of Science Core Collection, which brought together 1326 articles. The research concludes with a discussion on the relevance, ease of application, and usefulness of the methodologies.

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Section: Research Methods and Advances In Flood Researchmentioning

confidence: 99%

An Overview of Flood Risk Analysis Methods

Diaconu

Costache

Popa

2021

Water

Self Cite

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“…Curve number and slope degree made a moderate contribution to urban flood inundation. Based on the type of the study area, which results in different hydro-climatological conditions, some researchers have concluded that land use is the most important factor, since it is significantly correlated with flood potential degree in the flood susceptibility map (Ali et al 2020;Talukdar et al 2020;Yariyan et al 2020). Impervious areas (e.g.…”

Section: Influences Of the Flood Conditioning Factorsmentioning

confidence: 99%

Development of a novel hybrid multi-boosting neural network model for spatial prediction of urban flood

Darabi

Rahmati

Naghibi

et al. 2021

Geocarto International

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In this study, a new hybridized machine learning algorithm for urban flood susceptibility mapping, named MultiB-MLPNN, was developed using a multi-boosting technique and MLPNN. The model was tested in Amol City, Iran, a data-scarce city in an ungauged area which is prone to severe flood inundation events and currently lacks flood prevention infrastructure. Performance of the hybridized model was compared with that of a standalone MLPNN model, random forest and boosted regression trees. Area under the curve, efficiency, true skill statistic, Matthews correlation coefficient, misclassification rate, sensitivity and specificity were used to evaluate model performance. In validation, the MultiB-MLPNN model showed the best predictive performance. The hybridized MultiB-MLPNN model is thus useful for generating realistic flood susceptibility maps for data-scarce urban areas. The maps can be used to develop risk-reduction measures to protect urban areas from devastating floods, particularly where available data are insufficient to support physically based hydrological or hydraulic models.

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“…7). These were divided using the natural break algorithm in Arc Map software (Talukdar et al 2020, Yousefi et al 2020.…”

Section: Spatial Prediction Of Water Erosion Susceptibilitymentioning

confidence: 99%

A New Approach For Smart Soil Erosion Modelling: Integration of Empirical And Machine Learning Models

Avand

Mohammadi

Mirchooli

et al. 2021

Preprint

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Despite advances in artificial intelligence modelling, the lack of soil erosion data and other watershed information is still one of the important factors limiting soil-erosion modelling. Additionally, the limited number of parameters and the lack of evaluation criteria are major disadvantages of empirical soil-erosion models. To overcome these limitations, we introduce a new approach that integrates empirical and artificial intelligence models. Erosion-prone locations (erosion ≥16 tons/ha/year) are identified using RUSLE model and a soil-erosion map is prepared using random forest (RF), artificial neural network (ANN), classification tree analysis (CTA), and generalized linear model (GLM). This study uses 13 factors affecting soil erosion in the Talar watershed, Iran, to increase prediction accuracy. The results reveal that the RF model has the highest prediction performance (AUC=0.95, Kappa=0.87, Accuracy=0.93, and Bias=0.88), outperforming the three machine-learning models. The results show that slope angle, land use/land cover, elevation, and rainfall erosivity are the factors that contribute the most to soil erosion propensity in the watershed. Curvature and topography position index (TPI) were removed from the analysis due to multicollinearity with other factors. The results can be used to improve the identification of hot spots of soil erosion, especially in watersheds for which soil-erosion data are limited.

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Flood susceptibility modeling in Teesta River basin, Bangladesh using novel ensembles of bagging algorithms

Cited by 156 publications

References 131 publications

An Overview of Flood Risk Analysis Methods

An Overview of Flood Risk Analysis Methods

Development of a novel hybrid multi-boosting neural network model for spatial prediction of urban flood

A New Approach For Smart Soil Erosion Modelling: Integration of Empirical And Machine Learning Models

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