Flood routing model using integration of Delft3D and GIS (case study: Tanggul watershed, Jember)

Haq, Taqiuddin; Halik, Gusfan; Hidayah, Entin

doi:10.1063/5.0014607

Cited by 5 publications

(3 citation statements)

References 6 publications

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“…Climatically, based on observations of rainfall for ten years (2009)(2010)(2011)(2012)(2013)(2014)(2015)(2016)(2017)(2018)(2019) Village will overflow and cause flooding (Haq et al, 2020). Land use in the research area is dominated by agricultural land in rice fields (48.46%) and plantations/gardens by 33.67%, while settlements are 8.82%.…”

Section: Study Areamentioning

confidence: 99%

“…Physical modeling requires experiments to validate the model's predictive performance (Mudashiru et al, 2021). It can then be simulated in 1, 2, and 3-dimensional forms using a numeric model to determine the process of flooding (Carmo, 2020;Balica et al, 2013), such as numerical models using Delft3D in the Tanggul watershed (Haq et al, 2020), the use of HEC-RAS in the Singojuruh sub-district and the upstream Citarum watershed (Bachri et al, 2021;Siregar & Indrawan, 2017). The physicalbased approach is exact for flood prediction but requires many input data such as hydrological, topographic, morphological, and remote sensing data processed in GIS (Ji et al, 2012).…”

Section: Introductionmentioning

confidence: 99%

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Assessment of Flood Hazard Mapping Based on Analytical Hierarchy Process (AHP) and GIS: Application in Kencong District, Jember Regency, Indonesia

Mujib

Apriyanto²,

Kurnianto³

et al. 2021

Geos. Ind.

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Flood is one of the most frequent hydrometeorological disasters which leads in economic losses. The first step in flood disaster mitigation efforts is mapping vulnerable areas. Kencong District frequently affected by the annual flooding event. This study aims to assess flood hazard mapping by integrating the AHP method and Geographic Information System. This study used a descriptive quantitative approach through the correlation matrix of the AHP model for each physical environmental factor. These factors include slope, altitude, distance from the river, soil type, Topographic Wetness Index (TWI), and Curvature. Furthermore, with the Geographic Information System (GIS), the weighted overlay stage was carried out to obtain the results of flood-prone areas. Based on the AHP analysis, the most significant factors in determining flood-prone areas were the distance from rivers, slopes, and TWI. The results of flood-prone areas mapping were divided into five classes: from deficient 0.02%, low 4.26%, medium 37.11%, high 51.89%, and very high 6.72%. Validation of GIS mapping results with data in the field has an AUC value of 84%, which indicates that the prediction of the AHP-GIS model is perfect in flood-prone areas mapping in the Kencong District. The integration of AHP method and Geographic Information System in flood hazard assessment were able to produce a model to evaluate the spatial distribution of flood-prone areas. Keywords : Flood Hazard Mapping; Multi-criteria decision analysis; AHP Model; GIS; Jember Copyright (c) 2021 Geosfera Indonesia and Department of Geography Education, University of Jember This work is licensed under a Creative Commons Attribution-Share A like 4.0 International License

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Section: Study Areamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Assessment of Flood Hazard Mapping Based on Analytical Hierarchy Process (AHP) and GIS: Application in Kencong District, Jember Regency, Indonesia

Mujib

Apriyanto²,

Kurnianto³

et al. 2021

Geos. Ind.

View full text Add to dashboard Cite

show abstract

“…Three primary approaches are commonly used for mapping flood-prone areas (Teng et al, 2017). Physical modeling involves experiments to validate predictive models, while numeric models like Delft3D simulate flooding processes in various dimensions (Haq et al, 2020). Though providing precise predictions, physical-based approaches require extensive data input, including hydrological, topographic, morphological, and remote sensing data, processed in a Geographic Information System (GIS) (Ji et al, 2012).…”

Section: Introductionmentioning

confidence: 99%

Integrated flood risk assessment in Hunza-Nagar, Pakistan: unifying big climate data analytics and multi-criteria decision-making with GIS

Mukhtar,

Shangguan,

Ding

et al. 2024

Front. Environ. Sci.

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Floods are a widespread natural disaster with substantial economic implications and far-reaching consequences. In Northern Pakistan, the Hunza-Nagar valley faces vulnerability to floods, posing significant challenges to its sustainable development. This study aimed to evaluate flood risk in the region by employing a GIS-based Multi-Criteria Decision Analysis (MCDA) approach and big climate data records. By using a comprehensive flood risk assessment model, a flood hazard map was developed by considering nine influential factors: rainfall, regional temperature variation, distance to the river, elevation, slope, Normalized difference vegetation index (NDVI), Topographic wetness index (TWI), land use/land cover (LULC), curvature, and soil type. The analytical hierarchy process (AHP) analysis assigned weights to each factor and integrated with geospatial data using a GIS to generate flood risk maps, classifying hazard levels into five categories. The study assigned higher importance to rainfall, distance to the river, elevation, and slope compared to NDVI, TWI, LULC, curvature, and soil type. The weighted overlay flood risk map obtained from the reclassified maps of nine influencing factors identified 6% of the total area as very high, 36% as high, 41% as moderate, 16% as low, and 1% as very low flood risk. The accuracy of the flood risk model was demonstrated through the Receiver Operating Characteristics-Area Under the Curve (ROC-AUC) analysis, yielding a commendable prediction accuracy of 0.773. This MCDA approach offers an efficient and direct means of flood risk modeling, utilizing fundamental GIS data. The model serves as a valuable tool for decision-makers, enhancing flood risk awareness and providing vital insights for disaster management authorities in the Hunza-Nagar Valley. As future developments unfold, this study remains an indispensable resource for disaster preparedness and management in the Hunza-Nagar Valley region.

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Automated floodwater depth estimation using large multimodal model for rapid flood mapping

Akinboyewa,

Ning,

Lessani

et al. 2024

Comput.Urban Sci.

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Information on the depth of floodwater is crucial for rapid mapping of areas affected by floods. However, previous approaches for estimating floodwater depth, including field surveys, remote sensing, and machine learning techniques, can be time-consuming and resource-intensive. This paper presents an automated and rapid approach for estimating floodwater depth from on-site flood photos. A pre-trained large multimodal model, Generative pre-trained transformers (GPT-4) Vision, was used specifically for estimating floodwater. The input data were flood photos that contained referenced objects, such as street signs, cars, people, and buildings. Using the heights of the common objects as references, the model returned the floodwater depth as the output. Results show that the proposed approach can rapidly provide a consistent and reliable estimation of floodwater depth from flood photos. Such rapid estimation is transformative in flood inundation mapping and assessing the severity of the flood in near-real time, which is essential for effective flood response strategies.

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Flood routing model using integration of Delft3D and GIS (case study: Tanggul watershed, Jember)

Cited by 5 publications

References 6 publications

Assessment of Flood Hazard Mapping Based on Analytical Hierarchy Process (AHP) and GIS: Application in Kencong District, Jember Regency, Indonesia

Assessment of Flood Hazard Mapping Based on Analytical Hierarchy Process (AHP) and GIS: Application in Kencong District, Jember Regency, Indonesia

Integrated flood risk assessment in Hunza-Nagar, Pakistan: unifying big climate data analytics and multi-criteria decision-making with GIS

Automated floodwater depth estimation using large multimodal model for rapid flood mapping

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