Application of Google earth to investigate the change of flood inundation area due to flood detention dam

Madadi, Mohamad Reza; Azamathulla, Hazi Mohammad; Yakhkeshi, M E

doi:10.1007/s12145-014-0197-8

Cited by 32 publications

(13 citation statements)

References 33 publications

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“…A comparison of the results of a zero variant and the proposed variants with several detention reservoirs was performed. The comparison was done at a specific profile below the detention reservoirs and below the confluence of the Gidra and Kamenný streams [24].…”

Section: Resultsmentioning

confidence: 99%

Flash flood mitigation modeling — case study small Carpathians

Janík¹,

Šoltész²

2017

Pollack Periodica

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Flash floods are becoming a phenomenon that troubles people more and more often in many Slovak under-mountain regions and society needs to deal with these extreme natural events. This article is showing an example of mathematical modeling of flood protection measures in Small Carpathians region. To secure flood protection, detention reservoirs have been proposed, because of their automatic function that is important due to rapid progress of flash floods. As a base for this project a previous work that attempted to reconstruct the flood has been used. HEC-RAS 4.1.0 software package was used for numerical modeling, which includes flood mitigation calculation. Six variants of proposed detention reservoirs were created and checked by a design flood wave that was created according to rainfall with recurrence period of 100 years. All of the proposed detention reservoirs have exact proportions needed to secure flood protection including height of the dam, length of the dam in crest, type and dimension of outlet and dimensions of emergency spillways. The modeling included flood wave passing during several situations-normal function of detention reservoirs, blocked outlet and dam break. This study should be considered as a base for future flood protection project. An extensive terrain survey has been performed in frame of this project.

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Section: Resultsmentioning

confidence: 99%

Flash flood mitigation modeling — case study small Carpathians

Janík¹,

Šoltész²

2017

Pollack Periodica

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“…Machine learning (ML) and deep learning (DL) algorithms play a critical role in geospatial and remote sensing applications. Both ML and DL are widely used in various applications which involves classifying images such as land cover classification [3], Geological disaster recognition [4] and ART neural networks in remote sensing applications [5], assessing climatic changes [6], examining flood inundation [7], and many more. Many ML algorithms are widely in use for classifying the images such as artificial neural networks (ANNs) [8], support vector machines (SVMs) [9], decision trees (DT) [10], and ensemble methods such as random forests [11].…”

Section: Related Workmentioning

confidence: 99%

A Comparison of Machine Learning Approaches for Classifying Flood-Hit Areas in Aerial Images

Jothi

Priyadarsini

2020

Advances in Intelligent Systems and Computing

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Floods caused due to climatic changes have become one among the most devastating natural hazards. Immediate relief operations play an important role in saving numerous lives during flood-hit time. Various technologies are used for quick response, one being the use of drones. As drones take the aerial images of the floodhit areas, we have proposed a method of classifying aerial images to identify flood-hit areas using various classifiers such as SVM, fine tree, KNN, and neural networks. Their performances are compared, and it is observed that SVM classifier outperforms the remaining classifiers with almost 93.1% due to its simplicity though neural networks minimize the amount of training to a larger extent. This classification of images is then used to identify the flood-affected areas to spot the extent of floods. Keywords SVM • Fine tree • KNN • Neural networks • Classification • Disaster response

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“…After the compilation of the final river geometry file, in different return periods and the HEC-RAS generated water level for different return periods [22]. The water surface level for each return period has been exported in HEC-GeoRAS for final inundation area mapping along the river [23]. Flood hazard assessments are slope, elevation, average rainfall, drainage density, land use, and soil type figure 5.…”

Section: Flood Hazard Assessment and Inundation Area Mappingmentioning

confidence: 99%

Flood Plain Mapping and Hazard Assessment of Muga River by Using ArcGIS and HEC-RAS Model Upper Blue Nile Ethiopia

Aynalem¹

2020

LARP

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Flood happens repeatedly and is a common phenomenon in Ethiopia causing a lot of losses to human lives as well as damage to property. The majority of flood disasters' victims are people living in and nearby stretch of floodplains. It results direct or indirect loss of agricultural productivity, infrastructure, and disruption of transportation access and services. The objective of this study was to analyze flood inundation area mapping and Hazard Assessment of Muga River. Flood generating factors, i.e. slope, elevation, rainfall, drainage density, land use, and soil type were rated and combined to delineate flood hazard zones using a multi-criteria evaluation technique in an ArcGIS environment. The flooded areas along the Muga River have been mapped based on highest flows for different return periods using the HEC-RAS model, ArcGIS for spatial data processing and HEC-GeoRAS for interfacing between HEC-RAS and ArcGIS. The areas along the Muga River simulated to be inundated for 5, 10, 25, 50 and 100 years return periods. The flooded areas along the Muga River are 18 km2, 21 km 2 , 26 km 2 , 34 km 2 and 43 km 2 for 5, 10, 25, 50 and 100 year return periods, respectively. Proper land use management and afforestation, is significant to reduce the adverse effects of flooding particularly in the low-lying flood prone areas. The result of this study will helps the concerned bodies to formulate develop strategies according to the available flood plain mapping and hazard to the area.

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Application of Google earth to investigate the change of flood inundation area due to flood detention dam

Cited by 32 publications

References 33 publications

Flash flood mitigation modeling — case study small Carpathians

Flash flood mitigation modeling — case study small Carpathians

A Comparison of Machine Learning Approaches for Classifying Flood-Hit Areas in Aerial Images

Flood Plain Mapping and Hazard Assessment of Muga River by Using ArcGIS and HEC-RAS Model Upper Blue Nile Ethiopia

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