Integration of Geographic Information System (GIS) and hydraulic modelling to simulate floodplain inundation level for Bandar Segamat

Sulaiman, Nur Aishah; Aziz, Nur Fadillah Ab; Tarmizi, Nazirah Md.; Samad, Abd Manan; Jaafar, Wan Zurina Wan

doi:10.1109/icsgrc.2014.6908706

Cited by 4 publications

(2 citation statements)

References 3 publications

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“…There is a high increase in the need for effective modelling to understand the problem to mitigate flash floods' disastrous effects. The integration of GIS techniques with the Hydrological Engineering Centre -River Analysis System (HEC-RAS) and the Hydrological Engineering Centre -Geographical River Analysis System (HEC-GeoRAS) in modelling floodplain areas has become one of the popular methods used among researchers nowadays (Sulaiman et al, 2014). In recent years, the use of GIS has grown rapidly in hydrology management and research.…”

Section: Introductionmentioning

confidence: 99%

Hydrological impacts of land use - land cover change on urban flood hazard: A case study of the Jukskei River in Alexandra Township, Johannesburg, South Africa.

Mawasha¹,

Britz²

2022

SA J of Geomatics

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Flooding in urban areas is a major natural disaster causing damage to infrastructure, properties and loss of life. In urban areas the major causes behind the changing hydrological processes (i.e., floods) include topography, increase in precipitation due to climate change and change in land-use/land-cover (LULC) over time. The objective of this study is to evaluate the spatial and temporal LULC change impacts on flooding along the Jukskei River in Alexandra Township, Johannesburg, South Africa. The LULC images of 1987 MSS and 2015 OLI derived from Landsat satellite were pre-processed and classified using a supervised classification method. The analysis of LULC revealed that, there is an increase in built-up area from 934,2 ha to 1277,2 ha and reduction in intact and sparse vegetation from 190,5 ha to 62,4 ha and 380,8 ha to 142,1 ha, respectively, between the years 1987 and 2015. The flood depth map, velocity map and flood depth-velocity for different return periods and LULC scenarios have been developed by using an integrated approach of the Hydrological Engineering Centre-River Analysis System (HEC-RAS) and the HEC-GeoRAS with the geographic information system (GIS) and remote sensing data. From the analysis, it is observed that there is an increase in flood depth and flood velocity from 2,3 m to 3,0 m and 1,4 m/s to 3,4 m/s, whereas the depth-velocity for the last 28-years increased by 3,4 m2/s from 2,9 m2/s to 6,3 m2/s for the 1987 LULC and the 2015 LULC conditions, respectively. The flood hazard maps generated in this study can be used by local authorities and municipalities for flood disaster management.

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Section: Introductionmentioning

confidence: 99%

Hydrological impacts of land use - land cover change on urban flood hazard: A case study of the Jukskei River in Alexandra Township, Johannesburg, South Africa.

Mawasha¹,

Britz²

2022

SA J of Geomatics

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“…In terms of mathematical models, it was found that current frameworks made their flood forecast based on three models, i.e., meteorological, hydrological, and ML ones. The first two models were used with many algorithms, e.g., HEC-HMS [57], HEC-RAS [59], Mike11 [60], Mike21 [60], Mike-Flood [60], and ECMWF [61], etc., while those using ML model were implemented with various methods (as discussed in the section I. E). Thus far, these systems were only experimental and, to our knowledge, not yet publicly distributed.…”

Section: F Flood Forecasting Frameworkmentioning

confidence: 99%

Flood Forecasting System Based on Integrated Big and Crowdsource Data by Using Machine Learning Techniques

Puttinaovarat

Horkaew

2020

IEEE Access

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Flood is one of the most disruptive natural hazards, responsible for loss of lives and damage to properties. A number of cities are subject to monsoons influences and hence face the disaster almost every year. Early notification of flood incident could benefit the authorities and public to devise both short and long terms preventive measures, to prepare evacuation and rescue mission, and to relieve the flood victims. Geographical locations of affected areas and respective severities, for instances, are among the key determinants in most flood administration. Thus far, an effective means of anticipating flood in advance remains lacking. Existing tools were typically based on manually input and prepared data. The processes were tedious and thus prohibitive for real-time and early forecasts. Furthermore, these tools did not fully exploit more comprehensive information available in current big data platforms. Therefore, this paper proposes a novel flood forecasting system based on fusing meteorological, hydrological, geospatial, and crowdsource big data in an adaptive machine learning framework. Data intelligence was driven by stateof-the-art learning strategies. Subjective and objective evaluations indicated that the developed system was able to forecast flood incidents, happening in specific areas and time frames. It was also later revealed by benchmarking experiments that the system configured with an MLP ANN gave the most effective prediction, with correct percentage, Kappa, MAE and RMSE of 97.93, 0.89, 0.01 and 0.10, respectively. INDEX TERMS Flood forecasting system, big data, machine learning, crowdsource, deep learning. PARAMATE HORKAEW received the B.Eng. degree (Hons.

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Recent literature in cartography and geographic information science

Finn¹,

Thunen²

2015

Cartography and Geographic Information Science

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Integration of Geographic Information System (GIS) and hydraulic modelling to simulate floodplain inundation level for Bandar Segamat

Cited by 4 publications

References 3 publications

Hydrological impacts of land use - land cover change on urban flood hazard: A case study of the Jukskei River in Alexandra Township, Johannesburg, South Africa.

Hydrological impacts of land use - land cover change on urban flood hazard: A case study of the Jukskei River in Alexandra Township, Johannesburg, South Africa.

Flood Forecasting System Based on Integrated Big and Crowdsource Data by Using Machine Learning Techniques

Recent literature in cartography and geographic information science

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