Influence of headwater reservoirs on climate change impacts and flood frequency in the Kabul River Basin

Taraky, Yar M.; Liu, Yongbo; Gharabaghi, Bahram; McBean, Edward A.; Daggupati, Prasad; Shrestha, Narayan Kumar

doi:10.1139/cjce-2020-0840

Cited by 3 publications

(2 citation statements)

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“…One of global warming's adverse effects is the increase in the frequency and severity of floods [1,2]. Resulting from both anthropologic and weather factors [3][4][5][6][7], floods are the deadliest natural event [8,9], where they have already affected billions of people worldwide [10][11][12], causing huge economic and human losses [13][14][15][16][17].…”

Section: Introductionmentioning

confidence: 99%

A New Graph-Based Deep Learning Model to Predict Flooding with Validation on a Case Study on the Humber River

Oliveira Santos,

Costa Rocha,

Scott

et al. 2023

Water

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Floods are one of the most lethal natural disasters. It is crucial to forecast the timing and evolution of these events and create an advanced warning system to allow for the proper implementation of preventive measures. This work introduced a new graph-based forecasting model, namely, graph neural network sample and aggregate (GNN-SAGE), to estimate river flooding. It then validated the proposed model in the Humber River watershed in Ontario, Canada. Using past precipitation and stage data from reference and neighboring stations, the proposed GNN-SAGE model could estimate the river stage for flooding events up to 24 h ahead, improving its forecasting performance by an average of 18% compared with the persistence model and 9% compared with the graph-based model residual gated graph convolutional network (GNN-ResGated), which were used as baselines. Furthermore, GNN-SAGE generated smaller errors than those reported in the current literature. The Shapley additive explanations (SHAP) revealed that prior data from the reference station was the most significant factor for all prediction intervals, with seasonality and precipitation being more influential for longer-range forecasts. The findings positioned the proposed GNN-SAGE model as a cutting-edge solution for flood forecasting and a valuable resource for devising early flood-warning systems.

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Section: Introductionmentioning

confidence: 99%

A New Graph-Based Deep Learning Model to Predict Flooding with Validation on a Case Study on the Humber River

Oliveira Santos,

Costa Rocha,

Scott

et al. 2023

Water

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“…An accurate flood prediction hinges on a nuanced understanding of the dynamics between these two factors. As soon as the soil's moisture levels or the river's capacity reach critical thresholds, flood risks amplify [30][31][32][33]. Through continuous monitoring and data analysis of both precipitation and river water levels, these sophisticated forecasting models can identify and highlight patterns indicative of potential flood events.…”

Section: Introductionmentioning

confidence: 99%

Flood Forecasting Using Hybrid LSTM and GRU Models with Lag Time Preprocessing

Zhang,

Zhou,

Van Griensven Thé

et al. 2023

Water

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Climate change and urbanization have increased the frequency of floods worldwide, resulting in substantial casualties and property loss. Accurate flood forecasting can offer governments early warnings about impending flood disasters, giving them a chance to evacuate and save lives. Deep learning is used in flood forecasting to improve the timeliness and accuracy of flood water level predictions. While various deep learning models similar to Long Short-Term Memory (LSTM) have achieved notable results, they have complex structures with low computational efficiency, and often lack generalizability and stability. This study applies a spatiotemporal Attention Gated Recurrent Unit (STA-GRU) model for flood prediction to increase the models’ computing efficiency. Another salient feature of our methodology is the incorporation of lag time during data preprocessing before the training of the model. Notably, for 12-h forecasting, the STA-GRU model’s R-squared (R2) value increased from 0.8125 to 0.9215. Concurrently, the model manifested reduced root mean squared error (RMSE) and mean absolute error (MAE) metrics. For a more extended 24-h forecasting, the R2 value of the STA-GRU model improved from 0.6181 to 0.7283, accompanied by diminishing RMSE and MAE values. Seven typical deep learning models—the LSTM, the Convolutional Neural Networks LSTM (CNNLSTM), the Convolutional LSTM (ConvLSTM), the spatiotemporal Attention Long Short-Term Memory (STA-LSTM), the GRU, the Convolutional Neural Networks GRU (CNNGRU), and the STA-GRU—are compared for water level prediction. Comparative analysis delineated that the use of the STA-GRU model and the application of the lag time pre-processing method significantly improved the reliability and accuracy of flood forecasting.

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Application of graph neural networks to forecast urban flood events: the case study of the 2013 flood of the Bow River, Calgary, Canada

Costa Rocha,

Oliveira Santos,

Scott

et al. 2024

International Journal of River Basin Management

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Influence of headwater reservoirs on climate change impacts and flood frequency in the Kabul River Basin

Cited by 3 publications

References 44 publications

A New Graph-Based Deep Learning Model to Predict Flooding with Validation on a Case Study on the Humber River

A New Graph-Based Deep Learning Model to Predict Flooding with Validation on a Case Study on the Humber River

Flood Forecasting Using Hybrid LSTM and GRU Models with Lag Time Preprocessing

Application of graph neural networks to forecast urban flood events: the case study of the 2013 flood of the Bow River, Calgary, Canada

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