Characterizing recurrent flood hazards in the Himalayan foothill region through data-driven modelling

Hasanuzzaman, Md.; Shit, Pravat Kumar; Bera, Biswajit; Islam, Aznarul

doi:10.1016/j.asr.2023.02.028

Cited by 13 publications

(2 citation statements)

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“…Event data (cases) have five (5) incident data criteria, namely rainfall, water level, drainage conditions, and embankment conditions. The incident data criteria in multi-criteria decision making are called multi-criteria decision making (MCDM), as shown in Table 1.…”

Section: Scope Of Studymentioning

confidence: 99%

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Modelling of Flood Hazard Early Warning Group Decision Support System

Soebroto,

Limantara,

Suhartanto

et al. 2024

Civ Eng J

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Early warning of flood hazards needs to be carried out comprehensively to avoid a higher risk of disaster. Every decision on early warning of a flood hazard is carried out in part by one party, namely the government or water resource managers. This research aims to provide a collaborative decision-making model for early warning of flood hazards through a Group Decision Support System Model (GDSS), especially in Indonesia. The novelty of this research is that the GDSS model involves more than one decision-maker and multi-criteria decision-making for early warning of flood hazards in the downstream Kali Sadar River, Mojokerto Regency, East Java Province, Indonesia. The GDSS model was developed using a hybrid method, namely the Analytical Network Process (ANP) and VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR). There was more than one decision result; voting was carried out using the BORDA method to produce the decision. The test results of GDSS were obtained using a Spearman rank correlation coefficient of 0.8425 and matrix confusion, an accuracy value of 86.7%, a precision value of 86.7%, a recall value of 86.7%, and an f-measure of 86.7%. Based on the test results, good results were obtained from the GDSS model. Doi: 10.28991/CEJ-2024-010-02-018 Full Text: PDF

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Section: Scope Of Studymentioning

confidence: 99%

“…This is due to differences in preferences in making decisions from the government and water resource managers. Data and information on flood warnings need to pay attention to the following criteria: rainfall, river discharge, water level, embankment conditions, and drainage condition data [5].…”

Section: Introductionmentioning

confidence: 99%