Urban flood modeling using deep-learning approaches in Seoul, South Korea

Lei, Xinxiang; Chen, Wei; Panahi, Mahdi; Falah, Fatemeh; Rahmati, Omid; Uuemaa, Evelyn; Kalantari, Zahra; Ferreira, Carla; Rezaie, Fatemeh; Tiefenbacher, John P.; Lee, Saro; Bian, Huiyuan

doi:10.1016/j.jhydrol.2021.126684

Cited by 112 publications

(57 citation statements)

References 85 publications

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“…The application of GeoAI in hydrological spatial prediction is diverse; it can be used, for example, in the risk mapping of hydrological extremes such as flood and drought [88][89][90]. In particular, GeoAI is widely applied in flood mapping, using satellite imagery, UAVs, high resolution LiDAR topographic data, and automatic water level sensors [91][92][93].…”

Section: Spatial Prediction Of Hydrological Variablesmentioning

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: Spatial Prediction Of Hydrological Variablesmentioning

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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show abstract

“…The model also serves as a decision-making tool for providers of drinking water and land use planners. Urban flood modelling utilizing deep-learning techniques was developed by Lei et al [42]. Flood hazard mapping was produced using a deep convolution neural network model and a GIS.…”

Section: Hydrological Modelsmentioning

confidence: 99%

A Brief Review of Recent Developments in the Integration of Deep Learning with GIS

Mohan

M.V.S.S

2022

GaEE

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The interaction of Deep Learning (DL) methods with Geographical Information System (GIS) provides the opportunity to obtain new insights into environmental processes through the spatial, temporal and spectral resolutions as well as data integration. The two technologies may be connected to form a dynamic system that is incredibly well adapted to the evaluation of environmental conditions through the interrelationships of texture, size, pattern, and process. This perspective has acquired popularity in multiple disciplines. GIS is significantly dependant on processors, particularly for 3D calculations, map rendering, and route calculation whereas DL can process huge amounts of data. DL has received a lot of attention recently as a technology with a plethora of promising results. Furthermore, the growing use of DL methods in a variety of disciplines, including GIS, is evident. This study tries to provide a brief overview of the use of DL methods in GIS. This paper introduces the essential DL concepts relevant to GIS, the majority of which have been published in recent years. This research explores remote sensing applications and technologies in areas such as mapping, hydrological modelling, disaster management, and transportation route planning. Finally, conclusions on contemporary framework methodologies and suggestions for further studies are provided.

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“…Urban flooding triggered by excessive rainfall has been one of the most common natural disasters in recent years (Global Natural Disaster Assessment Report 2020). Developing an accurate urban flood model becomes imperative as flooding affects livelihoods and economies (Teng et al 2017;Anni et al 2020;Lei et al 2021) of the population in affected areas. Simulating urban flooding is challenging because of the complex interaction between hydro-meteorological, surface, and sub-surface factors, which are the major determining factors of flood flow.…”

Section: Introductionmentioning

confidence: 99%

Urban Flood Simulations with Modified Digital Elevation Model

Dasallas

Lee

2022

Preprint

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The acquisition of high-resolution digital elevation model (DEM) data is an on-going challenge in urban flood modeling, especially for larger domains. The accuracy of flood simulations is significantly dependent on the precision of DEM data. To address this issue, we propose a method of modifying mid-resolution DEM data to provide a realistic ground elevation for urban flood modeling using geographic information system (GIS) tools. The modified DEM contains the properties of the existing digital terrain and digital surface model (DTM and DSM) data. Flood calculations were performed using 2D shallow water and kinematic equations in the integrated multiscale simulation scheme developed in recent literature and tested for an extreme flood event in an urbanized area in the Philippines. The results show that the underestimated and overestimated flood tendencies for DTM and DSM, respectively, were adequately resolved in MDEM simulations, which were able to accurately capture the flood propagation along the natural and artificial barriers in the urban area, as compared to untreated DTM and DSM data, with root mean square error (RMSE) improvements from RMSEDTM = 30.64 and RMSEDSM = 17.28 to RMSEMDEM = 0.17. The proposed method significantly improved the accuracy of the simulations. It would allow high-resolution urban flood modeling with limited detailed elevation data, which is crucial to propose adequate flood action plans specifically for developing countries experiencing frequent rainfall events.

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Urban flood modeling using deep-learning approaches in Seoul, South Korea

Cited by 112 publications

References 85 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

A Brief Review of Recent Developments in the Integration of Deep Learning with GIS

Urban Flood Simulations with Modified Digital Elevation Model

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