洪水遥感诊断:以泰国大城为例:a case study of ayutthaya, thailand

图书信息

内容简介

洪灾是世界上分布区域*广、破坏性**的自然灾害之一，基于遥感技术监测洪灾面积、评估灾害损失的研究始于20世纪80年代后期。随着近30年的技术发展，洪灾遥感监测及灾后传染病预测领域的研究取得了一定的进展。本书系统介绍了洪水淹没区识别及洪灾引起的水源性传染病爆发风险监测方法。全书以2011年泰国大城地区洪灾为例，介绍了基于遥感技术的洪水淹没区识别方法和基于地理信息系统等空间信息技术的洪灾区水源性疾病监测预警系统，包括研究区和数据；基于植被指数、支持向量机和改进后的支持向量机的洪水淹没区识别方法；洪水遥感参数和水源性疾病的相关关系；水源性疾病爆发风险评估方法、基于后向传播神经网络的水源性疾病爆发风险监测及其专家系统的应用案例等。本书具有较强的实用性，可为该领域科研工作者提供参考，也可为灾害治理部门的科学决策提供支撑。

本书可供地学、遥感科学、传染病学和空间分析领域的科研人员和管理人员参考。

目录

1. 洪水遥感诊断:以泰国大城为例:a case study of ayutthaya, thailand 目录
2. Part Ⅰ Flooding Identification Method
3. 1 Geographical Characteristics of the Study Area
4. 1.1 Characteristics of the Ayutthaya Province
5. 1.2 Thailand Major Flood Event of 2011
6. 1.3 Environmental Remote Sensing of Flooding Area
7. 1.4 References
8. 2 Datasets and Data Preparation
9. 2.1 Remote Sensing Datasets and Preprocessing
10. 2.1.1 Image Enhancement or Radiometric Correction
11. 2.1.2 Georeferencing or Geometric Correction
12. 2.2 Flood Water Quality Data
13. 2.3 Morbidity Data
14. 2.4 Summary
15. References
16. 3 Flooding Identification by Vegetation Index
17. 3.1 Flooding Identification from Multispectral Images
18. 3.2 Flooding Identification from SAR Images
19. 3.3 Multi-temporal Remote Sensing Data for Flooding Identification
20. 3.4 Summary
21. References
22. 4 Flooding Identification by Support Vector Machine
23. 4.1 Support Vector Machine
24. 4.2 Affecting Parameters of the SVM Classifier
25. 4.3 Summary
26. References
27. 5 Improved Support Vector Machine Classifier Through a Particle Filter Algorithm
28. 5.1 Linear Dynamics Systems
29. 5.1.1 Linear Continuous Systems
30. 5.1.2 Discrete Linear Systems
31. 5.2 Random Process and Stochastic Systems to Model State Estimation
32. 5.2.1 Probability,Random Variables,and Their Statistical Properties
33. 5.2.2 Statistical Properties of Random Processes and Random Sequence
34. 5.2.3 Linear System Models of Random Processes and Random Sequences
35. 5.3 Particle Filter Algorithm
36. 5.4 A Support Vector Machine-Based Particle Filter (SVM-PF)
37. 5.5 A SVM-PF Applied in the Study Area
38. 5.6 Measured Results of the SVM-PF in Water Identification of Flooding Area
39. 5.6.1 Reference Dataset
40. 5.6.2 Accuracy Assessment
41. 5.7 An In-House Classifier for All Land Cover--CANFET
42. 5.7.1 Important Theories and Concepts
43. 5.7.2 Original CANFET
44. 5.7.3 Simplified CANFET
45. 5.7.4 Direct-Matching CANFET
46. 5.8 Summary
47. References
48. Part Ⅱ Waterborne Diseases Caused by Flooding Disasters
49. 6 Flood-Related Parameters Affecting Waterborne Diseases
50. 6.1 Flood Parameters Derived from Multi-temporal Remote Sensing Data
51. 6.2 Population Variables
52. 6.3 Flood Water Quality
53. 6.3.1 Inverse Distance Weighting for Spatial Distribution
54. 6.3.2 Spatial Distribution of Dissolved Oxygen
55. 6.4 Summary
56. References
57. 7 Measure of Disease Risk
58. 7.1 Waterborne Diseases Caused by Flooding
59. 7.2 Disease Risk Assessment
60. 7.3 Outbreak Detection Methods
61. 7.4 Estimation of Outbreak Risk Using Risk Ratio Function
62. 7.5 Summary
63. References
64. 8 Modeling Outbreak Risk Based on the Back Propagation Neural Network (BPNN) Algorithm
65. 8.1 Back Propagation Neural Network (BPNN)
66. 8.2 A Multilayer Feed-Forward Neural Network
67. 8.3 Back Propagation
68. 8.4 Modeling Outbreak Risk Based on BPNN
69. 8.4.1 Initial Analysis of Input and Reference Data
70. 8.4.2 Results of Training and Testing the BPNN
71. 8.5 Summary
72. References
73. 9 Application of Surveillance of Communicable Disease Risk Using Expert Systems
74. 9.1 Introduction to the Expert System
75. 9.2 Integrated Expert System for Surveillance of Communicable Disease Risk
76. 9.2.1 Expert System for Diagnosis Design
77. 9.2.2 Expert System for Diagnosis Evaluation
78. 9.3 Summary
79. References
80. 10 Conclusions and Discussions
81. 10.1 Summary of Major Results
82. 10.1.1 Flood Identification
83. 10.1.2 Waterborne Diseases Caused by Flooding Disasters
84. 10.2 Further Work
85. References
86. 展开全部