Flood zones detection using a runoff model built on Hexagonal shape based cellular automata

Douass, Souhaib; Kbir, M. Aït

doi:10.14445/22315381/ijett-v68i6p211s

Cited by 3 publications

(2 citation statements)

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“…On the other hand, the incorporation of DGGS and bottom-up predictive models such as cellular automata and agent-based modeling requires further development because the neighborhood relationship over the grid cells is an essential component of the modeling process. Although not using a hierarchical grid system, Douass and Kbir [14] gave an example of calculating water flow dynamics and delineating flood zones with the cellular automata-based algorithm in hexagonal grid meshes. Conducting these bottom-up predictive models in a hexagonal DGGS environment can be promising if the neighborhood navigation is supported by a cell indexing mechanism, which is exemplified by the Quadrilateral 2-Dimensional Integer (Q2DI) indexing implemented in the DGGRID library [63].…”

Section: Flood Modeling In Hexagonal Dggsmentioning

confidence: 99%

“…The hierarchical nature of DGGS can also support multi-scale analysis in a systematic manner, where the area ratio between two consecutive resolutions is consistent. Algorithms on hexagonal grids were explored by a few studies, although not using multi-scale structures, such as flow direction and accumulation, watershed boundary extraction, valley line modeling, and flood zone delineation [11][12][13][14]. In particular, topographical and hydrological parameters commonly used in flood modeling have been developed in the context of ISEA3H DGGS and implemented at multiple resolutions, which set the foundation of this study [15,16].…”

Section: Introductionmentioning

confidence: 99%

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Multi-Scale Flood Mapping under Climate Change Scenarios in Hexagonal Discrete Global Grids

McGrath

Stefanakis

2022

IJGI

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Among the most prevalent natural hazards, flooding has been threatening human lives and properties. Robust flood simulation is required for effective response and prevention. Machine learning is widely used in flood modeling due to its high performance and scalability. Nonetheless, data pre-processing of heterogeneous sources can be cumbersome, and traditional data processing and modeling have been limited to a single resolution. This study employed an Icosahedral Snyder Equal Area Aperture 3 Hexagonal Discrete Global Grid System (ISEA3H DGGS) as a scalable, standard spatial framework for computation, integration, and analysis of multi-source geospatial data. We managed to incorporate external machine learning algorithms with a DGGS-based data framework, and project future flood risks under multiple climate change scenarios for southern New Brunswick, Canada. A total of 32 explanatory factors including topographical, hydrological, geomorphic, meteorological, and anthropogenic were investigated. Results showed that low elevation and proximity to permanent waterbodies were primary factors of flooding events, and rising spring temperatures can increase flood risk. Flooding extent was predicted to occupy 135–203% of the 2019 flood area, one of the most recent major flooding events, by the year 2100. Our results assisted in understanding the potential impact of climate change on flood risk, and indicated the feasibility of DGGS as the standard data fabric for heterogeneous data integration and incorporated in multi-scale data mining.

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Section: Flood Modeling In Hexagonal Dggsmentioning

confidence: 99%