A deep learning model for predicting river flood depth and extent

Hosseiny, Hossein

doi:10.1016/j.envsoft.2021.105186

Cited by 47 publications

(15 citation statements)

References 35 publications

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“…In addition, the numerical solving approach of the hydraulic model results in high numerical instability due to sensitivity to the initial and boundary conditions, model structure, and spatial and temporal discretization [120]. Thus, the GeoAI method has emerged as a promising tool for hydraulic modeling in large-scale and natural systems [19,119,121,122]. Emerging deep learning applications in computer fluid dynamics have also shown potential for the modeling of turbulent and complex flow structures [123][124][125].…”

Section: Hydraulic Modelingmentioning

confidence: 99%

Geospatial Artificial Intelligence (GeoAI) in the Integrated Hydrological and Fluvial Systems Modeling: Review of Current Applications and Trends

Gonzales‐Inca

Calle

Croghan

et al. 2022

Water

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This paper reviews the current GeoAI and machine learning applications in hydrological and hydraulic modeling, hydrological optimization problems, water quality modeling, and fluvial geomorphic and morphodynamic mapping. GeoAI effectively harnesses the vast amount of spatial and non-spatial data collected with the new automatic technologies. The fast development of GeoAI provides multiple methods and techniques, although it also makes comparisons between different methods challenging. Overall, selecting a particular GeoAI method depends on the application’s objective, data availability, and user expertise. GeoAI has shown advantages in non-linear modeling, computational efficiency, integration of multiple data sources, high accurate prediction capability, and the unraveling of new hydrological patterns and processes. A major drawback in most GeoAI models is the adequate model setting and low physical interpretability, explainability, and model generalization. The most recent research on hydrological GeoAI has focused on integrating the physical-based models’ principles with the GeoAI methods and on the progress towards autonomous prediction and forecasting systems.

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Section: Hydraulic Modelingmentioning

confidence: 99%

Geospatial Artificial Intelligence (GeoAI) in the Integrated Hydrological and Fluvial Systems Modeling: Review of Current Applications and Trends

Gonzales‐Inca

Calle

Croghan

et al. 2022

Water

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“…For instance, Abid et al [18] identified water bodies using an unsupervised curriculum-learning method based on the convolutional neural network, which overcomes the challenges faced by remote-sensing images. Hosseiny [19] proposed a novel deep-learning framework for automatic identification of river geometry and flooding extent and prediction of riverwater depth. Aziz et al [20] used the unsupervised machine-learning algorithms, such as the k-means, clustering large application, and hierarchical agglomerative clustering for predicting river-sediment adaptation.…”

Section: Introductionmentioning

confidence: 99%

Monitoring and Predicting Channel Morphology of the Tongtian River, Headwater of the Yangtze River Using Landsat Images and Lightweight Neural Network

et al. 2022

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The Tongtian River is the source of the Yangtze River and is a national key ecological reserve in China. Monitoring and predicting the changes and mechanisms of the Tongtian River channel morphology are beneficial to protecting the “Asian Water Tower”. This study aims to quantitatively monitor and predict the accretion and erosion area of the Tongtian River channel morphology during the past 30 years (1990–2020). Firstly, the water bodies of the Tongtian River were extracted and the accretion and erosion areas were quantified using 1108 Landsat images based on the combined method of three water-body indices and a threshold, and the surface-water dataset provided by the European Commission Joint Research Centre. Secondly, an intelligent lightweight neural-network model was constructed to predict and analyze the accretion and erosion area of the Tongtian River. Results indicate that the Tongtian River experienced apparent accretion and erosion with a total area of 98.3 and 94.9 km2, respectively, during 1990–2020. The braided (meandering) reaches at the upper (lower) Tongtian River exhibit an overall trend of accretion (erosion). The Tongtian River channel morphology was determined by the synergistic effect of sediment-transport velocity and streamflow. The lightweight neural network well-reproduced the complex nonlinear processes in the river-channel morphology with a final prediction error of 0.0048 km2 for the training session and 4.6 km2 for the test session. Results in this study provide more effective, reasonable, and scientific decision-making aids for monitoring, protecting, understanding, and mining the evolution characteristics of rivers, especially the complex change processes of braided river channels in alpine regions and developing countries.

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“…In recent years, machine learning has been combined and applied in many flood-related dataset studies. [16], [18]- [20]. Machine learning uses computational algorithms to evaluate information and develop predictions with big data.…”

Section: Introductionmentioning

confidence: 99%

“…Machine learning uses computational algorithms to evaluate information and develop predictions with big data. [20]. This approach to using machine learning is deep learning which is very useful in a rare area of data.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Predictive Modeling Classification of Post-Flood and Abrasion Effects With Deep Learning Approach

Marleny¹,

Mambang

2022

TIERS

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Floods and abrasion are the most common disasters in Indonesia. A lot of data is collected from post-flood and abrasion disasters. From the data released by BNPB, disaster data is directly based on the occurrence of disasters. From these data, we will test predictive modeling classification with a deep learning approach. Big data can be made through classification and predictive modeling. Our proposed model is a classification of predictive modeling of post-flood and abrasion data using the H2O deep learning approach. Deep learning H2O models can also be evaluated with specific model metrics, termination metrics, and performance charts. This approach is used to optimize the performance and accuracy of predictions during the modeling process using our dataset pool training. The big data to be processed will generate new knowledge for policies in decision making. Big data performance modeled with Deep Learning H2O is used to predict the Survival attributes of post-flood and abrasion sample datasets. The best metric performance is obtained from the maxout activation function with a 200-200 unit layer that gets an accuracy of 93.49% with AUC: 0.808 +/- 0.022 (micro average: 0.808).

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A deep learning model for predicting river flood depth and extent

Cited by 47 publications

References 35 publications

Geospatial Artificial Intelligence (GeoAI) in the Integrated Hydrological and Fluvial Systems Modeling: Review of Current Applications and Trends

Geospatial Artificial Intelligence (GeoAI) in the Integrated Hydrological and Fluvial Systems Modeling: Review of Current Applications and Trends

Monitoring and Predicting Channel Morphology of the Tongtian River, Headwater of the Yangtze River Using Landsat Images and Lightweight Neural Network

Predictive Modeling Classification of Post-Flood and Abrasion Effects With Deep Learning Approach

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