Urban Pluvial Flood Forecasting using Open Data with Machine Learning Techniques in Pattani Basin

Noymanee, Jeerana; Nikitin, Nikolay O.; Kalyuzhnaya, Anna V.

doi:10.1016/j.procs.2017.11.187

Cited by 55 publications

(28 citation statements)

References 19 publications

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“…Data-driven models ignore the physical background of a problem and rather explore the relation between the input and output data [6]. For short or long-term flood forecasts, different data-driven models have been implemented, such as neuro-fuzzy [7], support vector machine [8], support vector regression [9,10], Bayesian linear regression methods [11] and artificial neural network (ANN) [12]. Among them, artificial neural networks (ANN) can be an effective tool for flood modeling, if it is properly applied, overcoming pitfalls as over-fitting/under-fitting with sufficient and representative data for model training [13].…”

Section: Introductionmentioning

confidence: 99%

Multistep Flood Inundation Forecasts with Resilient Backpropagation Neural Networks: Kulmbach Case Study

Lin

Leandro

Gerber

et al. 2020

Water

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Flooding, a significant natural disaster, attracts worldwide attention because of its high impact on communities and individuals and increasing trend due to climate change. A flood forecast system can minimize the impacts by predicting the flood hazard before it occurs. Artificial neural networks (ANN) could efficiently process large amounts of data and find relations that enable faster flood predictions. The aim of this study is to perform multistep forecasts for 1–5 h after the flooding event has been triggered by a forecast threshold value. In this work, an ANN developed for the real-time forecast of flood inundation with a high spatial resolution (4 m × 4 m) is extended to allow for multiple forecasts. After trained with 120 synthetic flood events, the ANN was first tested with 60 synthetic events for verifying the forecast performance for 3 h, 6 h, 9 h and 12 h lead time. The model produces good results, as shown by more than 81% of all grids having an RMSE below 0.3 m. The ANN is then applied to the three historical flood events to test the multistep inundation forecast. For the historical flood events, the results show that the ANN outputs have a good forecast accuracy of the water depths for (at least) the 3 h forecast with over 70% accuracy (RMSE within 0.3 m), and a moderate accuracy for the subsequent forecasts with (at least) 60% accuracy.

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Section: Introductionmentioning

confidence: 99%

Multistep Flood Inundation Forecasts with Resilient Backpropagation Neural Networks: Kulmbach Case Study

Lin

Leandro

Gerber

et al. 2020

Water

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“…These approximations make use of artificial neural networks (ANNs) to obtain control actions and cope with the nonlinear behaviour, but are too dated to include the latest breakthroughs that have appeared in the field in regards to function approximation tasks [8] in order to improve their performance, thus not being competitive with respect to other control techniques. Even more, efforts have been put towards using machine learning techniques to forecast pluvial flooding [9] or waste-water indicators [10], but, to the best of our knowledge, have not been tightly integrated in the automatic control strategies so that they can improve with the additional information.…”

Section: Introductionmentioning

confidence: 99%

Control of Urban Drainage Systems: Optimal Flow Control and Deep Learning in Action

Ochoa

Riaño-Briceño

Quijano

et al. 2019

2019 American Control Conference (ACC)

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A hierarchical control strategy is proposed to solve the optimal drainage problem in sewer systems by combining an optimization technique known as minimum scaled consensus control (MSCC) with the deep deterministic policy gradient (DDPG) algorithm. The MSCC strategy operates at the global control level, and is used to determine the flows of the hydraulic structures of the drainage system, such that the water is optimally distributed, i.e., wastewater flows are controlled to minimize saturation of water levels and/or flooding events, filling each of the drainage system components (e.g., pipes, tanks, wastewater treatment plants) proportionally to their capacity. On the other hand, the DDPG uses a model-free approach at the local control level, setting the drainage flows by operating valves and gates, without any knowledge of the inherent dynamics, so that it can be used to handle the nonlinearities of the system. Finally, a case study is presented to show the effectiveness of the proposed strategy.

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“…Less expensive, and easy to use because the rating indices and arithmetical outcomes can be utilized as a mention point but there is difficulty in standardizing the dataset for flood risk evaluation. Noymanee, Nikitin, & Kalyuzhnaya, (2017) designed a water level model. The use of machine learning techniques for open data using machine learning based model (Bayesian linear model) for predicting flood peak in the city.…”

Section: 0mentioning

confidence: 99%

Risk Assessment Model for Pluvial Flood Prediction Using Fuzzy-Based Classification Technique

2021

CEIS

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Both developed and developing countries are promoting risk management and refining the ability to alleviate the effects of disaster both man-made and natural, which have become a threat to human life and the world's economy. The variability in climate change, rapid urbanization and fast-growing socioeconomic development has naturally increased the risk associated with flooding. A recent report showed that flood have affected more individuals than any other category of disaster in the 21st century with the highest percentage of 43% of all disaster events in 2019 and Africa been the second vulnerable continent after Asia. So, it is highly important to devise a scientific method for flood risk reduction since it cannot be eradicated. Machine learning can improve the risk management. The paper proposes a pluvial flood detection and prediction system based on machine learning techniques. The proposed model will employ a fuzzy rule-based classification approach for pluvial flood risk assessment.

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Urban Pluvial Flood Forecasting using Open Data with Machine Learning Techniques in Pattani Basin

Cited by 55 publications

References 19 publications

Multistep Flood Inundation Forecasts with Resilient Backpropagation Neural Networks: Kulmbach Case Study

Multistep Flood Inundation Forecasts with Resilient Backpropagation Neural Networks: Kulmbach Case Study

Control of Urban Drainage Systems: Optimal Flow Control and Deep Learning in Action

Risk Assessment Model for Pluvial Flood Prediction Using Fuzzy-Based Classification Technique

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