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Environmental remote sensing in floo...

Cao, Chunxiang.

Environmental remote sensing in flooding areasa case study of Ayutthaya, Thailand /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Environmental remote sensing in flooding areasby Chunxiang Cao ... [et al.].
其他題名:	a case study of Ayutthaya, Thailand /
其他作者:	Cao, Chunxiang.
出版者:	Singapore :Springer Singapore :2021.
面頁冊數:	xi, 148 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
標題:	FloodsRemote sensing.
電子資源:	https://doi.org/10.1007/978-981-15-8202-8
ISBN:	9789811582028$q(electronic bk.)

Environmental remote sensing in flooding areasa case study of Ayutthaya, Thailand /
Environmental remote sensing in flooding areasa case study of Ayutthaya, Thailand /[electronic resource] :by Chunxiang Cao ... [et al.]. - Singapore :Springer Singapore :2021. - xi, 148 p. :ill., digital ;24 cm.

Geographical characteristics of Study area -- Datasets and data preparation -- Flooding identification by vegetation index -- Flood identification by Support Vector Machine (SVM) -- Improved support vector machine classifier by Particle filter algorithm -- Flood related parameters affecting waterborne diseases -- Measure of Disease Risk -- Modeling Outbreak Risk based on Back Propagation Neural Network (BPNN) algorithm -- Application of surveillance communicable diseases risk using Expert system -- Conclusions.

This book introduces flood inundation area and flood risks assessment based on a comprehensive monitoring system using remote sensing and geographic information system technologies. Taking the 2011 flood disaster of Ayutthaya in Thailand as an example, it presents a flood intrusion zone identification method based on remote sensing technology, spatial information technology and geographic information system for flood disaster monitoring and early warning system. It introduces the study area and data, vegetation index, improved support vector machine and flood intrusion zone identification method. It also analyzes the flood remote sensing parameters and waterborne diseases, method of risk assessment of waterborne disease outbreak, waterborne disease outbreak risk monitoring based on backpropagation neural network and its expert system. It not only promotes a new interdisciplinary approach both in public health and space information technology, but also greatly supports decision makers in disaster reduction.

ISBN: 9789811582028$q(electronic bk.)

Standard No.: 10.1007/978-981-15-8202-8doiSubjects--Topical Terms:

802093
Floods
--Remote sensing.

LC Class. No.: GB1399.5.T5

Dewey Class. No.: 551.4890112

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505 0 $a Geographical characteristics of Study area -- Datasets and data preparation -- Flooding identification by vegetation index -- Flood identification by Support Vector Machine (SVM) -- Improved support vector machine classifier by Particle filter algorithm -- Flood related parameters affecting waterborne diseases -- Measure of Disease Risk -- Modeling Outbreak Risk based on Back Propagation Neural Network (BPNN) algorithm -- Application of surveillance communicable diseases risk using Expert system -- Conclusions.
520 $a This book introduces flood inundation area and flood risks assessment based on a comprehensive monitoring system using remote sensing and geographic information system technologies. Taking the 2011 flood disaster of Ayutthaya in Thailand as an example, it presents a flood intrusion zone identification method based on remote sensing technology, spatial information technology and geographic information system for flood disaster monitoring and early warning system. It introduces the study area and data, vegetation index, improved support vector machine and flood intrusion zone identification method. It also analyzes the flood remote sensing parameters and waterborne diseases, method of risk assessment of waterborne disease outbreak, waterborne disease outbreak risk monitoring based on backpropagation neural network and its expert system. It not only promotes a new interdisciplinary approach both in public health and space information technology, but also greatly supports decision makers in disaster reduction.
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