Flood modelling using Artificial Neural Network

Ruslan, Fazlina Ahmat; Zakaria, Nur Khalidah; Adnan, Ramli

doi:10.1109/icsgrc.2013.6653287

Cited by 19 publications

(10 citation statements)

References 10 publications

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“…One method is to gather rainfall data and daily water levels to test and train a ML model such as ANN [93]. This model has been built to predict the water levels after a time interval of 24, 48 and 72 h. Ruslan [94] used data based on water levels at three upstream river locations to train an ANN model. A Neural Network (NN) inverse model was integrated with the output to improve the results [94].…”

Section: Flood Risk Management and Evacuation Strategies For The Aged Care Facilitiesmentioning

confidence: 99%

“…This model has been built to predict the water levels after a time interval of 24, 48 and 72 h. Ruslan [94] used data based on water levels at three upstream river locations to train an ANN model. A Neural Network (NN) inverse model was integrated with the output to improve the results [94]. Shi et al [95] used rainfall and river flow data to train an SVM classifier to make predictions regarding river flow change and peak flow within 48 h. ANN model is programmed to mimic the human's brain way of learning.…”

Section: Flood Risk Management and Evacuation Strategies For The Aged Care Facilitiesmentioning

confidence: 99%

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An AI/ML-Based Strategy for Disaster Response and Evacuation of Victims in Aged Care Facilities in the Hawkesbury-Nepean Valley: A Perspective

et al. 2022

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The Hawkesbury-Nepean Valley, Australia’s longest coastal catchment, is spanned by a river system of more than 470 km, that runs from Goulburn to Broken Bay, covering a total area of over 2.2 million hectares. This region has remained prone to flood events, with considerable mortalities, economic impacts and infrastructural losses occurring quite regularly. The topography, naturally variable climatic conditions and the ‘bathtub’ effect in the region are responsible for the frequent flood events. In response, the Government at the national/federal, state and local level has focused on the design of efficient flood risk management strategies with appropriate evacuation plans for vulnerable communities from hospitals, schools, childcare and aged care facilities during a flood event. Despite these overarching plans, specialized response and evacuation plans for aged care facilities are critical to reducing the loss incurred by flood events in the region. This is the focus of this present paper, which reviews the history of flood events and responses to them, before examining the utilization of artificial intelligence (AI) techniques during flood events to overcome the flood risks. An early flood warning system, based on AI/Machine Learning (ML) strategy is being suggested for a timely decision, enhanced disaster prediction, assessment and response necessary to overcome the flood risks associated with aged care facilities within the Hawkesbury-Nepean region. A framework entailing AI/ML methods for identifying the safest route to the destination using UAV and path planning has been proposed for timely disaster response and evacuation of the residents of aged care facilities.

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Section: Flood Risk Management and Evacuation Strategies For The Aged Care Facilitiesmentioning

confidence: 99%

Section: Flood Risk Management and Evacuation Strategies For The Aged Care Facilitiesmentioning

confidence: 99%

An AI/ML-Based Strategy for Disaster Response and Evacuation of Victims in Aged Care Facilities in the Hawkesbury-Nepean Valley: A Perspective

et al. 2022

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“…Most of the previously used models were mainly focused on the hydrodynamic model, hydrological model, multi-criteria decision analysis (MCDA), statistical models (SM), and machine learning (ML) techniques, which are incorporated in the geographic information system (Singh and Kumar 2013;Pradhan 2014;Elkhrachy 2015;Vojtek and Vojteková 2016;Rosser et al 2017;Samanta et al 2018;Tiryaki and Karaca 2018;Liuzzo et al 2019;Santos et al 2019;Tehrany et al 2019a;Shahabi et al 2020). The most commonly used models and techniques concerning flood susceptibility mapping include frequency ratio (FR) (Rahmati et al 2015;Shafapour Tehrany et al 2017;Samanta et al 2018;Tehrany et al 2019a;Sarkar and Mondal 2020), analytical hierarchy process (AHP) (Elkhrachy 2015;Dahri and Abida 2017;Das 2018;Rahman et al 2019;Sepehri et al 2020a), shannon's entropy (Khosravi and Pourghasemi 2016;, weights of evidence (WoE) (Tehrany et al 2014b;Rahmati et al 2015;Shafapour Tehrany et al 2017;Costache 2019), artificial neural networks (ANN) (Kia et al 2012;Ruslan et al 2013;Elsafi 2014;Rahman et al 2019;Kordrostami et al 2020), fuzzy logic (Nandalal and Ratnayake 2011;Sahana and Patel 2019;Sepehri et al 2020b), support vector machines (Tehrany et al 2014b…”

Section: Introductionmentioning

confidence: 99%

A Comparative Evaluation of GIS based Flood Susceptibility Models: A Case of Kopai River Basin, Eastern India

Ghosh

Sutradhar

Das³

et al. 2021

Preprint

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Flood is a typical natural disaster which every year causes enormous damage to the natural environment, buildings and victims worldwide. The efficiency of flooding depends upon numerous criteria: flood strength, amplitude, frequency, flow time, changes in plan and river geometry, etc. In this research, three statistical models such as the frequency ratio, Shannon's entropy, and weight of evidence have been used to identify flood susceptibility regions in the Kopai River Basin. In order to run these statistical models, flooded and non-flooded pixels were employed. The outcome of these three models demonstrates that the upper reach of the basin is characterised by non-flooded zones and the bottom reach of the basin is high flooded zones. The lower part of the basin is therefore more vulnerable than that of the upper portion. 11 thematic layers are employed to get the final output of flood susceptibility zones. e.g., soil type, normalised vegetation difference index, normalised water differential index, river lift, slope, drainage density, land usage and land cover, rainfall, soil moisture indices, surface roughness. 370 sample points have been taken as test data and finally receiver operating curves have been plotted for testing the degree of accuracy. The result of the validation reveals that the accuracy of the frequency ratio is 96.5%. Shannon entropy is 91.2% accurate and the weight of evidence is 97.1% accurate. Weight of evidence is therefore the best model for flood susceptibility zones identification for this Kopai basin.

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“…Previous attempts at modelling flood hazards have adopted several approaches. Such approaches include multi-criteria evaluation (Gazi et al, 2019;Gebre, 2015;Meyer et al, 2009;Rincón et al, 2018), probabilistic modelling approach (Apel et al, 2006;Budiyono et al, 2016), and neural networks (Kia et al, 2012;Paul and Das, 2014;Ruslan et al, 2013). Spatial logistic regression was applied in the Kelantan river basin, Malaysia, to map and delineate the flood-susceptible risk area (Pradhan and Lee, 2009)).…”

Section: Introductionmentioning

confidence: 99%

Modelling flood hazard in dry climates of southern Africa: a case of Beitbridge, Limpopo Basin, Zimbabwe

Chikwiramakomo

Gumindoga

Shekede

et al. 2021

WSA

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Floods are among the natural hazards that have adverse effects on human lives, livelihoods, economies and infrastructure. Dry climates of southern Africa have, over the years, experienced an increase in the frequency of tropical cyclone induced floods. However, understanding the key factors that influence susceptibility to floods has remained largely unexplored in these dry climates. Therefore, this study sought to model flood hazards and determine key factors that significantly explain the probability of flood occurrence in the southern parts of Beitbridge District, Zimbabwe. To achieve these objectives, logistic regression was used to predict spatial variations in flood hazards following cyclone Dineo in 2017. Before spatial prediction of flood hazard, environmental variables were tested for multicollinearity using the Pearson correlation coefficient. Only two environmental variables, i.e., elevation and rainfall, were not significantly correlated and were thus used in the subsequent flood hazard modelling. Results demonstrate that two variables significantly (p < 0.05) predicted spatial variations in flood hazard in the southern parts of the Beitbridge District with relatively high accuracy defined by the area under the curve (AUC = 0.98). In addition, results indicate that ~56 % of the study area is regarded as highly susceptible to floods. Given the projected increase in extreme events such as intense rainfall as a result of climate change, floods will be expected to correspondingly increase in these semi-arid regions. Results presented in this study underscore the importance of geospatial techniques in flood-hazard modelling, which is the key input in sustainable land-use planning. It can thus be concluded that spatial analytical techniques play a key role in flood early warning systems aimed at supporting and building resilient communities in the face of climate change–induced floods.

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Flood modelling using Artificial Neural Network

Cited by 19 publications

References 10 publications

An AI/ML-Based Strategy for Disaster Response and Evacuation of Victims in Aged Care Facilities in the Hawkesbury-Nepean Valley: A Perspective

An AI/ML-Based Strategy for Disaster Response and Evacuation of Victims in Aged Care Facilities in the Hawkesbury-Nepean Valley: A Perspective

A Comparative Evaluation of GIS based Flood Susceptibility Models: A Case of Kopai River Basin, Eastern India

Modelling flood hazard in dry climates of southern Africa: a case of Beitbridge, Limpopo Basin, Zimbabwe

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