A mixed approach for urban flood prediction using Machine Learning and GIS

Motta, Marcel; Neto, Miguel Sabino; Sarmento, Pedro

doi:10.1016/j.ijdrr.2021.102154

Cited by 83 publications

(37 citation statements)

References 32 publications

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“…The attempts of combining logic of programming languages and machine learning approaches to cartography resulted in several outputs such as e.g. combination of machine learning classifiers along with GIS techniques (Motta et al 2021), AWK language for processing tables from geodata through conversion and formatting (Lemenkova 2019e), modeling links between environmental, biodiversity and climate change impacts (Walther and Huettmann 2021), integration of GRASS GIS, Python, TeX language, or artificial neural networks for geological engineering modeling (Bragagnolo et al 2020, Lemenkov andLemenkova 2021b), to mention a few of them.…”

Section: Discussionmentioning

confidence: 99%

Dataset compilation by GRASS GIS for thematic mapping of Antarctica: Topographic surface, ice thickness, subglacial bed elevation and sediment thickness

Lemenkova¹

2021

Czech Polar Rep.

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This paper presents the GRASS GIS-based thematic mapping of Antarctica using scripting approach and associated datasets on topography and geophysics. The state-of-the art in cartographic development points at two important aspects. The first one comprises shell scripting promoted repeatability of the GIS technique, increased automatization in cartographic workflow, and compatibility of GRASS with Python, PROJ and GDAL libraries which enables advanced geospatial data processing: converting formats, re-projecting and spatial analysis. The second aspect is that data visualization greatly influences geologic research through improving the interpretation between the Antarctic glaciation and surface. This includes the machine learning algorithms of image classification enabling to distinguish between glacier and non-glacier surfaces through automatically partitioning data and analysis of various types of surfaces. Presented detailed maps of Antarctic include visualized datasets from the ETOPO1, GlobSed, EGM96 and Bedmap2 projects. The grids include bed and surface elevation, ETOPO1-based bathymetry and topography, bed, ice and sediment thickness, grounded bed uncertainty, subglacial bed elevation, geoid undulations, ice mask grounded and shelves. Data show the distribution of the present-day glacier, geophysical fields and topographic landforms for analysis of processes and correlations between the geophysical and geological phenomena. Advances in scripting cartography are significant contributions to the geological and glaciological research. Processing high-resolution datasets of Southern Ocean retrieved by remote sensing methods present new steps in automatization of the digital mapping, as presented in this research, and promotes comprehensive monitoring of geological, permafrost and glacial processes in Antarctica. All maps have been plotted using GRASS GIS version 7.8. with technical details of scripts described and interpreted.

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Section: Discussionmentioning

confidence: 99%

Dataset compilation by GRASS GIS for thematic mapping of Antarctica: Topographic surface, ice thickness, subglacial bed elevation and sediment thickness

Lemenkova¹

2021

Czech Polar Rep.

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“…Sanchez-Medina and C-Sanchez (2020) used machine learning to successfully forecast hotel booking cancellations. Other areas of machine learning applications can be seen in transportation (Huang and Zhu 2021), flood prediction (Motta et al, 2021), telecommunications (de Andres, 2020), smart city (Zekić-Sušac, Mitrović, and Has 2021), amongst others.…”

Section: Application Of Machine Learning In Various Industrial Segmentsmentioning

confidence: 99%

Application of machine learning algorithm in the sheet metal industry: an exploratory case study

Shamsuzzoha

Kankaanpaa

Nguyen

et al. 2021

International Journal of Computer Integrated Manufacturing

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This study solved a practical problem in a case in the sheet metal industry using machine learning and deep learning algorithms. The problem in the case company was related to detecting the minimum gaps between components, which were produced after the punching operation of a metal sheet. Due to the narrow gaps between the components, an automated sheer machine could not grip the rest of the sheet skeleton properly after the punching operation. This resulted in some of the scraped sheet on the worktable being left behind, which needed a human operator to intervene. This caused an extra trigger to the production line that resulted in a break in production. To solve this critical problem, the relevant images of the components and the gaps between them were analyzed using machine learning and deep learning techniques. The outcome of this study contributed to eliminating the production bottleneck by optimizing the gaps between the punched components. This optimization process facilitated the easy and safe movement of the gripper machine and contributed to minimizing the sheet waste.

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“…In recent years, machine learning algorithms have attracted increasing attention in the field of risk assessment management [ 18 , 19 , 20 , 21 ]. Riedel et al [ 22 ] carried out seismic vulnerability assessment of urban environments in moderate-to-low seismic hazard regions using association rule learning and support vector machine methods.…”

Section: Introductionmentioning

confidence: 99%

Study on Machine Learning Models for Building Resilience Evaluation in Mountainous Area: A Case Study of Banan District, Chongqing, China

Zhang

Wen

Liao

et al. 2022

Sensors

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‘Resilience’ is a new concept in the research and application of urban construction. From the perspective of building adaptability in a mountainous environment and maintaining safety performance over time, this paper innovatively proposes machine learning methods for evaluating the resilience of buildings in a mountainous area. Firstly, after considering the comprehensive effects of geographical and geological conditions, meteorological and hydrological factors, environmental factors and building factors, the database of building resilience evaluation models in a mountainous area is constructed. Then, machine learning methods such as random forest and support vector machine are used to complete model training and optimization. Finally, the test data are substituted into models, and the models’ effects are verified by the confusion matrix. The results show the following: (1) Twelve dominant impact factors are screened. (2) Through the screening of dominant factors, the models are comprehensively optimized. (3) The accuracy of the optimization models based on random forest and support vector machine are both 97.4%, and the F1 scores are greater than 94.4%. Resilience has important implications for risk prevention and the control of buildings in a mountainous environment.

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A mixed approach for urban flood prediction using Machine Learning and GIS

Cited by 83 publications

References 32 publications

Dataset compilation by GRASS GIS for thematic mapping of Antarctica: Topographic surface, ice thickness, subglacial bed elevation and sediment thickness

Dataset compilation by GRASS GIS for thematic mapping of Antarctica: Topographic surface, ice thickness, subglacial bed elevation and sediment thickness

Application of machine learning algorithm in the sheet metal industry: an exploratory case study

Study on Machine Learning Models for Building Resilience Evaluation in Mountainous Area: A Case Study of Banan District, Chongqing, China

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