Development of flood hazard map and emergency relief operation system using hydrodynamic modeling and machine learning algorithm

Rahman, Mohd Tadza Abdul; Chen, Ningsheng; Islam, Monirul; Mahmud, Golam Iftekhar; Pourghasemi, Hamid Reza; Alam, Mehtab; Rahim, Abdur; Baig, Muhammad Aslam; Arnob, Bhattacharjee,; Dewan, Ashraf

doi:10.1016/j.jclepro.2021.127594

Cited by 41 publications

(16 citation statements)

References 57 publications

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“…In this study, water level forecasting was carried out in two major cities of Bangladesh: Dhaka and Sylhet [7][8][9]14,16]. These two case study regions were selected as they are highly vulnerable to riverine flooding [55,56]. Besides, forecasting model structure and time series water level data are available for all the stations relative to other regions of the country.…”

Section: Study Areamentioning

confidence: 99%

“…Hence, we performed data imputations to address the data gap. Since water level data at different stations are missing randomly, we used the multiple imputation by chained equations (MICE) technique to impute the data gap [56]. MICE is robust and one of the widely used approaches to overcome a large amount of missing data [56].…”

Section: Datasetmentioning

confidence: 99%

“…Since water level data at different stations are missing randomly, we used the multiple imputation by chained equations (MICE) technique to impute the data gap [56]. MICE is robust and one of the widely used approaches to overcome a large amount of missing data [56]. It imputes missing data by The number of missing values is larger in stations around Sylhet than those located adjacent to Dhaka.…”

Section: Datasetmentioning

confidence: 99%

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Water Level Forecasting Using Spatiotemporal Attention-Based Long Short-Term Memory Network

Noor

Haq

Rakib

et al. 2022

Water

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Bangladesh is in the floodplains of the Ganges, Brahmaputra, and Meghna River delta, crisscrossed by an intricate web of rivers. Although the country is highly prone to flooding, the use of state-of-the-art deep learning models in predicting river water levels to aid flood forecasting is underexplored. Deep learning and attention-based models have shown high potential for accurately forecasting floods over space and time. The present study aims to develop a long short-term memory (LSTM) network and its attention-based architectures to predict flood water levels in the rivers of Bangladesh. The models developed in this study incorporated gauge-based water level data over 7 days for flood prediction at Dhaka and Sylhet stations. This study developed five models: artificial neural network (ANN), LSTM, spatial attention LSTM (SALSTM), temporal attention LSTM (TALSTM), and spatiotemporal attention LSTM (STALSTM). The multiple imputation by chained equations (MICE) method was applied to address missing data in the time series analysis. The results showed that the use of both spatial and temporal attention together increases the predictive performance of the LSTM model, which outperforms other attention-based LSTM models. The STALSTM-based flood forecasting system, developed in this study, could inform flood management plans to accurately predict floods in Bangladesh and elsewhere.

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Section: Study Areamentioning

confidence: 99%

Section: Datasetmentioning

confidence: 99%

Section: Datasetmentioning

confidence: 99%

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Water Level Forecasting Using Spatiotemporal Attention-Based Long Short-Term Memory Network

Noor

Haq

Rakib

et al. 2022

Water

Self Cite

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“…Several ML algorithms have been successfully used to predict flood risks. These include artificial neural network (ANN), which is one of the most widely used ML algorithms for flood risk prediction [21][22][23][24][25][26][27]. Other ML algorithms have been used to predict flood risk such as support vector machine [28][29][30], random forest (RF) [17,31,32], logistic regression [7], adaptive neuro-fuzzy inference system [33] and Long Short-term Memory [34,35].…”

Section: Introductionmentioning

confidence: 99%

Spatial prediction of flood-susceptible zones in the Ourika watershed of Morocco using machine learning algorithms

Meliho

Khattabi

Zejli

et al. 2022

ACI

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PurposeThe purpose of the paper is to predict mapping of areas vulnerable to flooding in the Ourika watershed in the High Atlas of Morocco with the aim of providing a useful tool capable of helping in the mitigation and management of floods in the associated region, as well as Morocco as a whole.Design/methodology/approachFour machine learning (ML) algorithms including k-nearest neighbors (KNN), artificial neural network, random forest (RF) and x-gradient boost (XGB) are adopted for modeling. Additionally, 16 predictors divided into categorical and numerical variables are used as inputs for modeling.FindingsThe results showed that RF and XGB were the best performing algorithms, with AUC scores of 99.1 and 99.2%, respectively. Conversely, KNN had the lowest predictive power, scoring 94.4%. Overall, the algorithms predicted that over 60% of the watershed was in the very low flood risk class, while the high flood risk class accounted for less than 15% of the area.Originality/valueThere are limited, if not non-existent studies on modeling using AI tools including ML in the region in predictive modeling of flooding, making this study intriguing.

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“…Whilst, keeping up with technologies and up-to-date tools such as providing adequate and precise flood hazard mapping and modeling (i.e., Flood Hazard Modeling-FHM) and information tools (i.e., Humanitarian Aid Information System-HAIS) to improve preparedness and awareness for actors that includes according to stakeholders and communities. These practices are effectively reducing flood impacts with identified potential flood hazardous areas and ensuring adequate time for the response measures from the decision-makers and communities on pluvial (urban) and coastal flood conditions (Dewan et al, 2006;Rahman et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Editorial: Urban Flood Resilience and Sustainable Flood Management Strategies in Megacities

et al. 2022

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Development of flood hazard map and emergency relief operation system using hydrodynamic modeling and machine learning algorithm

Cited by 41 publications

References 57 publications

Water Level Forecasting Using Spatiotemporal Attention-Based Long Short-Term Memory Network

Water Level Forecasting Using Spatiotemporal Attention-Based Long Short-Term Memory Network

Spatial prediction of flood-susceptible zones in the Ourika watershed of Morocco using machine learning algorithms

Editorial: Urban Flood Resilience and Sustainable Flood Management Strategies in Megacities

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