Deep Learning-Based Forecast and Warning of Floods in Klang River, Malaysia

Faruq, Amrul; Arsa, Hudan Pandu; Hussein, Shamsul Faisal Mohd; Razali, Che Munira Che; Marto, Aminaton; Abdullah, Shahrum Shah

doi:10.18280/isi.250311

Cited by 11 publications

(5 citation statements)

References 17 publications

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“…[ 29,37,38,48,[51][52][53][54][55] River or flood water, level observed [36,38,56] River or flood water, level forecasted [32,[57][58][59][60][61] Flood inundation extent Establish a spatial representation of floods to understand the impacted area.…”

Section: Intelligence On Flood Hazardsmentioning

confidence: 99%

Advanced Technologies for Offering Situational Intelligence in Flood Warning and Response Systems: A Literature Review

Samansiri

Fernando

Ingirige

2022

Water

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Deaths and property damage from floods have increased drastically in the past two decades due to various reasons such as increased populations, unplanned developments, and climate change. Such losses from floods can be reduced by issuing timely early warnings and through effective response mechanisms based on situational intelligence during emerging flood situations. This paper presents the outcome of a literature review that was conducted to identify the types and sources of the intelligence required for flood warning and response processes as well as the technology solutions that can be used for offering such intelligence. Twenty-seven different types of intelligence are presented together with the technologies that can be used to extract such intelligence. Furthermore, a conceptual architecture that illustrates how relevant technology solutions can be used to extract intelligence at various stages of a flood cycle for decision-making in issuing early warnings and planning responses is presented.

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Section: Intelligence On Flood Hazardsmentioning

confidence: 99%

Advanced Technologies for Offering Situational Intelligence in Flood Warning and Response Systems: A Literature Review

Samansiri

Fernando

Ingirige

2022

Water

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“…An effective real-time flood forecasting model may be helpful for disaster prevention, offering an advanced alert and mitigating the damage from the flood occurrence [35]. Flood forecasting has been improved by utilising deep learning models such as LSTM, RNN, and many others [18]. Many studies have applied a deep learning model in their study to predict flood occurrence and are proven to be an informative and accurate model as shown in Table I.…”

Section: B Recurrent Neural Networkmentioning

confidence: 99%

“…Those methods can only cover a small river flow area [17]. For example, Faruq et al [18] used an LSTM model to predict the flood by using a Klang River lever dataset. Another study [19] used the ANN model to predict floods by using the Kelantan river lever and rainfall dataset in separate models.…”

Section: Introductionmentioning

confidence: 99%

Flood Prediction using Deep Learning Models

Ali¹,

Asmai²,

Abidin³

et al. 2022

IJACSA

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Deep learning has recently appeared as one of the best reliable approaches for forecasting time series. Even though there are numerous data-driven models for flood prediction, most studies focus on prediction using a single flood variable. The creation of various data-driven models may require unfeasible computing resources when estimating multiple flood variables. Furthermore, the trends of several flood variables can only be revealed by analysing long-term historical observations, which conventional data-driven models do not adequately support. This study proposed a time series model with layer normalization and Leaky ReLU activation function in multivariable long-term short memory (LSTM), bidirectional long-term short memory (BI-LSTM) and deep recurrent neural network (DRNN). The proposed models were trained and evaluated by using the sensory historical data of river water level and rainfall in the east coast state of Malaysia. It were then, compared to the other six deep learning models. In terms of prediction accuracy, the experimental results also demonstrated that the deep recurrent neural network model with layer normalization and Leaky ReLU activation function performed better than other models.

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“…Measured from sensors or manual methods Assess whether the river is about to be flooded or has flooded and issue warnings accordingly. [16,24,25,34,[37][38][39][40][41] Observed by the community [23,25,42] forecasted by simulations [19,[43][44][45][46][47] Flood inundation inundation extent Establish a spatial representation of floods to understand the impacted area.…”

Section: River and Flood Water Levelmentioning

confidence: 99%

Review of Intelligence Required for Issuing Accurate Flood Early Warnings and Effective Disaster Response

Samansiri¹,

Fernando²,

Ingirige³

2022

Preprint

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Deaths and property damage from the flood have increased drastically in the past two decades due to various reasons such as increased population, unplanned development and climate change. Losses from floods can be reduced by having accurate intelligence of an emerging flood situation in order to make timely decisions for issuing early warnings and responding efficiently. This paper presents a thorough analysis of the types and sources of intelligence required for flood warning and response processes and technology solutions that can be used for capturing such intelligence. A structured review, covering a more comprehensive range of published literature on Flood Early Warning and Response Systems (FEWRS), was conducted to identify the necessary intelligence and the technology that can be used to capture intelligence required for various phases of a flood hazard as it develops. Twenty-seven different types of key intelligence required in the flood cycle were identified. A conceptual architecture was identified that illustrates how relevant technology solutions can be used to extract intelligence at various stages of a flood event for decision making for early warnings and response.

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Deep Learning-Based Forecast and Warning of Floods in Klang River, Malaysia

Cited by 11 publications

References 17 publications

Advanced Technologies for Offering Situational Intelligence in Flood Warning and Response Systems: A Literature Review

Advanced Technologies for Offering Situational Intelligence in Flood Warning and Response Systems: A Literature Review

Flood Prediction using Deep Learning Models

Review of Intelligence Required for Issuing Accurate Flood Early Warnings and Effective Disaster Response

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