Flood Prediction and Uncertainty Estimation Using Deep Learning

Gude, Vinayaka; Corns, Steven; Long, Suzanna

doi:10.3390/w12030884

Cited by 59 publications

(23 citation statements)

References 31 publications

(27 reference statements)

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“…For all five categories of rainfall intensity, the proposed model using weighted broadcasting exhibits superior performance. In particular, the proposed model using weighted broadcasting shows more than 30% improved performance in CSI metric than the existing model when the rainfall intensity is 30 mm/h or higher, which is the intensity value when the influence of rainfall on human life becomes substantial, implying that the proposed model can be useful for applications such as flood prediction [34][35][36]. Figure 4 shows the CSI and HSS scores of the two compared models while varying the future time steps.…”

Section: Resultsmentioning

confidence: 98%

Enhancing the Encoding-Forecasting Model for Precipitation Nowcasting by Putting High Emphasis on the Latest Data of the Time Step

et al. 2021

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Nowcasting is an important technique for weather forecasting because sudden weather changes significantly affect human life. The encoding-forecasting model, which is a state-of-the-art architecture in the field of data-driven radar extrapolation, does not particularly focus on the latest data when forecasting natural phenomena. This paper proposes a weighted broadcasting method that emphasizes the latest data of the time step to improve the nowcasting performance. This weighted broadcasting method allows the most recent rainfall patterns to have a greater impact on the forecasting network by extending the architecture of the existing encoding-forecasting model. Experimental results show that the proposed model is 1.74% and 2.20% better than the existing encoding-forecasting model in terms of mean absolute error and critical success index, respectively. In the case of heavy rainfall with an intensity of 30 mm/h or higher, the proposed model was more than 30% superior to the existing encoding-forecasting model. Therefore, applying the weighted broadcasting method, which explicitly places a high emphasis on the latest information, to the encoding-forecasting model is considered as an improvement that is applicable to the state-of-the-art implementation of data-driven radar-based precipitation nowcasting.

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Section: Resultsmentioning

confidence: 98%

Enhancing the Encoding-Forecasting Model for Precipitation Nowcasting by Putting High Emphasis on the Latest Data of the Time Step

et al. 2021

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“…An alternative approach that addresses these issues is computational intelligence. A key feature of computational intelligence approaches is the capacity to manage complexity and non-linearity without needing to understand underlying processes [15]. In summary, statistical methods rely on precise underlying relationships and exhibit decreased performance as the number of variables increases whereas computational intelligence approaches identify patterns using large amounts of training data to establish a model capable of accurate predictions [16].…”

Section: A Geospatial Deep Learning Approachmentioning

confidence: 99%

“…LSTM networks are the deep learning variant of RNNs. All figures and mathematical formulation are borrowed from [15]. The primary benefit of LSTM networks is the capacity to retain longer term information.…”

Section: Lstm Prediction Of Stream Stagementioning

confidence: 99%

Using Trend Extraction and Spatial Trends to Improve Flood Modeling and Control

Hale¹,

Long²,

Gude³

et al. 2022

Data Science, Data Visualization, and Digital Twins

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Effective management of flood events depends on a thorough understanding of regional geospatial characteristics, yet data visualization is rarely effectively integrated into the planning tools used by decision makers. This chapter considers publicly available data sets and data visualization techniques that can be adapted for use by all community planners and decision makers. A long short-term memory (LSTM) network is created to develop a univariate time series value for river stage prediction that improves the temporal resolution and accuracy of forecasts. This prediction is then tied to a corresponding spatial flood inundation profile in a geographic information system (GIS) setting. The intersection of flood profile and affected road segments can be easily visualized and extracted. Traffic decision makers can use these findings to proactively deploy re-routing measures and warnings to motorists to decrease travel-miles and risks such as loss of property or life.

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“…A method commonly applied to measure the water level of a river is the implantation of gauges at different locations of its course [3][4][5]. Although well-established and suitable for most situations, this solution is unable to detect uncommon events, such as extreme flooding.…”

Section: Introductionmentioning

confidence: 99%

Memory-Based Pruning of Deep Neural Networks for IoT Devices Applied to Flood Detection

Fernandes¹,

Nonato

Ranieri

et al. 2021

Sensors

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Automatic flood detection may be an important component for triggering damage control systems and minimizing the risk of social or economic impacts caused by flooding. Riverside images from regular cameras are a widely available resource that can be used for tackling this problem. Nevertheless, state-of-the-art neural networks, the most suitable approach for this type of computer vision task, are usually resource-consuming, which poses a challenge for deploying these models within low-capability Internet of Things (IoT) devices with unstable internet connections. In this work, we propose a deep neural network (DNN) architecture pruning algorithm capable of finding a pruned version of a given DNN within a user-specified memory footprint. Our results demonstrate that our proposed algorithm can find a pruned DNN model with the specified memory footprint with little to no degradation of its segmentation performance. Finally, we show that our algorithm can be used in a memory-constraint wireless sensor network (WSN) employed to detect flooding events of urban rivers, and the resulting pruned models have competitive results compared with the original models.

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Flood Prediction and Uncertainty Estimation Using Deep Learning

Cited by 59 publications

References 31 publications

Enhancing the Encoding-Forecasting Model for Precipitation Nowcasting by Putting High Emphasis on the Latest Data of the Time Step

Enhancing the Encoding-Forecasting Model for Precipitation Nowcasting by Putting High Emphasis on the Latest Data of the Time Step

Using Trend Extraction and Spatial Trends to Improve Flood Modeling and Control

Memory-Based Pruning of Deep Neural Networks for IoT Devices Applied to Flood Detection

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