Flood Prediction using Artificial Neural Networks: Empirical Evidence from Mauritius as a Case Study

Dhunny, A.Z.; Seebocus, Reena Hansa; Allam, Zaheer; Chuttur, Mohammad Yasser; Eltahan, Muhammed; Mehta, Harsh

doi:10.17977/um018v3i12020p1-10

Cited by 8 publications

(5 citation statements)

References 27 publications

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“…The research work [180] studies flood prediction using Machine neural networks in Mauritius. The average temperature of the earth is increasing at an alarming rate and it has been envisaged to increase by a factor of about 1.4 to 5.8 degree Celsius by the year 2100.…”

Section: ) Researchmentioning

confidence: 99%

“…Flash floods are thus a global phenomenon affecting major parts of the world as indicated for the year 2018, which marked the occurrence of several deadly flash floods in Kerala, France and Vietnam. In [180] the focus is on Mauritius, which is a small island located in the Indian Ocean, off the east coast of Africa and Madagascar. The morphological landscape of Mauritius consists of highlands and coastal regions in a relatively small geographical area of 1865 km such that it is typical for the island to experience several microclimates on the same day in different regions.…”

Section: ) Researchmentioning

confidence: 99%

See 1 more Smart Citation

Machine Intelligence in Africa: a survey

Tembine,

Tapo,

Danioko

et al. 2024

Preprint

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Section: ) Researchmentioning

confidence: 99%

Section: ) Researchmentioning

confidence: 99%

Machine Intelligence in Africa: a survey

Tembine,

Tapo,

Danioko

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…Recently, various studies using neural network models for flood prediction have been conducted [4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21]. An artificial neural network (ANN) model is a data-driven model that can make predictions rapidly, owing to fewer computational requirements than existing physical models.…”

Section: Introductionmentioning

confidence: 99%

“…An artificial neural network (ANN) model is a data-driven model that can make predictions rapidly, owing to fewer computational requirements than existing physical models. ANN models can improve the accuracy of predicting hydrological variables, such as water level, flow rate, and precipitation, as they effectively predict nonlinear data [4][5][6][7][8][9][10][11]. Several studies have compared the accuracy of neural network models for outflow prediction [12][13][14].…”

Section: Introductionmentioning

confidence: 99%

Ungauged Basin Flood Prediction Using Long Short-Term Memory and Unstructured Social Media Data

Lee,

Hwang

2023

Water

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Floods are highly perilous and recurring natural disasters that cause extensive property damage and threaten human life. However, the paucity of hydrological observational data hampers the precision of physical flood models, particularly in ungauged basins. Recent advances in disaster monitoring have explored the potential of social media as a valuable source of information. This study investigates the spatiotemporal consistency of social media data during flooding events and evaluates its viability as a substitute for hydrological data in ungauged catchments. To assess the utility of social media as an input factor for flood prediction models, the study conducted time-series and spatial correlation analyses by employing spatial scan statistics and confusion matrices. Subsequently, a long short-term memory model was used to forecast the outflow volume in the Ui Stream basin in South Korea. A comparative analysis of various input factor combinations revealed that datasets incorporating rainfall, outflow models, and social media data exhibited the highest accuracy, with a Nash–Sutcliffe efficiency of 94%, correlation coefficient of 97%, and a minimal normalized root mean square error of 0.92%. This study demonstrated the potential of social media data as a viable alternative for data-scarce basins, highlighting its effectiveness in enhancing flood prediction accuracy.

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“…Despite advances in developing models based on RNN, these models remain challenging to scale to long data sequences. Dhunny et al [25] have proven that an ANN model can predict the flood water level well within 24 hours ahead of time by using the data from rainfall and present river level data. In this study, the flood was predicted for one day ahead.…”

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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Flood Prediction using Artificial Neural Networks: Empirical Evidence from Mauritius as a Case Study

Cited by 8 publications

References 27 publications

Machine Intelligence in Africa: a survey

Machine Intelligence in Africa: a survey

Ungauged Basin Flood Prediction Using Long Short-Term Memory and Unstructured Social Media Data

Flood Prediction using Deep Learning Models

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