Title

影像感測於城市水患之監視

Translated Titles

Visual Sensing for Urban Flood Monitoring

Authors

羅世瑋

Key Words

視覺感測 ; 都市水患監測 ; 水位變動 ; visual sensing ; urban flood monitoring ; water level fluctuation

PublicationName

清華大學生醫工程與環境科學系學位論文

Volume or Term/Year and Month of Publication

2015年

Academic Degree Category

博士

Advisor

許靖涵

Content Language

英文

Chinese Abstract

隨著越來越極端的氣候下,都市洪水事件發生的頻率與嚴重性 已在全球性的範圍下愈加嚴重,然而綜觀目前研究而言,對於都市 內的小區域所進行即時減災決策所需要的實境影像資訊較為不足。 本研究提出以河川與水資源監視影像為基礎的洪水影像自動化監視 與水位漲落分析,加值傳統的被動式水情監視攝影機使其具有智慧 化視覺感測與分析能力。 結合本研究所提出之視覺感測方法,傳統的水情監視鏡頭可具 有感測與分析現地洪水事件的能力,解決現有水情監測仍需仰賴人 力進行全天畫面監看的現況。再者,視覺感測網路與傳統感測網路 相比,傳統感測網路多數只能提供感測器所量測到之一維物理參 數,視覺感測網路則可以提供監測現場的動態影像資訊,補足水利 防災單位在進行減災行動的決策時缺乏的實地視覺資訊。 本文以視覺感測方法,提供自動化分析遠端水情監視之畫面, 並測定洪水事件之形成,並使用近期洪水事件進行實驗驗證,證實 能夠進行電腦自主式的畫面監看與測定洪水事件發生工作,藉由提 供洪水事件之預警偵測與水位變動數據,將能使水利防災管理單位 能快速正確地理解當地水情狀況,進而明確地發起相應的減災措 施,未來更可應用在智慧化都市之洪水監控。

English Abstract

With the increasing climatic extremes, the frequency and severity of urban flood events have intensified worldwide. In this study, image-based automated monitoring of flood formation and analyses of water level fluctuation were proposed as value- added intelligent sensing applications to turn a passive monitoring camera into a visual sensor. Combined with the proposed visual sensing method, traditional hydrological monitoring cameras have the ability to sense and analyze the local situation of flood events. This can solve the current problem that image-based flood monitoring heavily relies on continuous manned monitoring. Conventional sensing networks can only offer one-dimensional physical parameters measured by gauge sensors, whereas visual sensors can acquire dynamic image information of monitored sites and provide disaster prevention agencies with actual field information for decision-making to relieve flood hazards. The visual sensing method established in this study provides spatiotemporal information that can be used for automated remote analysis for monitoring urban floods. This paper focuses on the determination of flood formation based on image- processing techniques. The experimental results suggest that the visual sensing approach may be a reliable way for determining the water fluctuation and measuring its elevation and flood intrusion with respect to real-world coordinates. The performance of the proposed method has been confirmed; it has the capability to monitor and analyze the flood status, and therefore, it can serve as an active flood warning system.

Topic Category 醫藥衛生 > 醫藥總論
原子科學院 > 生醫工程與環境科學系
生物農學 > 生物科學
Reference
  1. 2. Guhathakurta, P.; Sreejith, O. P.; Menon, P. A., Impact of climate change on extreme rainfall events and flood risk in India. Journal of Earth System Science 2011, 120, (3), 359-373.
    連結:
  2. 3. Hallegatte, S.; Green, C.; Nicholls, R. J.; Corfee-Morlot, J., Future flood losses in major coastal cities. Nature Clim. Change 2013, 3, (9), 802-806.
    連結:
  3. 4. Wade, S. D.; Rance, J.; Reynard, N., The UK Climate Change Risk Assessment 2012: Assessing the Impacts on Water Resources to Inform Policy Makers. Water Resources Management 2013, 27, (4), 1085-1109.
    連結:
  4. 6. Gourley, J. J.; Maddox, R. A.; Howard, K. W.; Burgess, D. W., An Exploratory Multisensor Technique for Quantitative Estimation of Stratiform Rainfall. Journal of Hydrometeorology 2002, 3, (2), 166-180.
    連結:
  5. 9. Shih, D. S.; Chen, C. H.; Yeh, G. T., Improving our understanding of flood forecasting using earlier hydro-meteorological intelligence. Journal of Hydrology 2014, 512, 470-481.
    連結:
  6. 12. Tsubaki, R.; Fujita, I.; Tsutsumi, S., Measurement of the flood discharge of a small-sized river using an existing digital video recording system. Journal of Hydro-Environment Research 2011, 5, (4), 313-321.
    連結:
  7. 13. Kim, J.; Han, Y.; Hahn, H., Embedded implementation of image-based water-level measurement system. Iet Computer Vision 2011, 5, (2), 125-133.
    連結:
  8. 14. Liu, L.; Liu, Y.; Wang, X.; Yu, D.; Liu, K.; Huang, H.; Hu, G., Developing an effective 2-D urban flood inundation model for city emergency management based on cellular automata. Natural Hazards and Earth System Sciences 2015, 15, (3), 381-391.
    連結:
  9. 15. Creutin, J. D.; Muste, M.; Bradley, A. A.; Kim, S. C.; Kruger, A., River gauging using PIV techniques: a proof of concept experiment on the Iowa River. Journal of Hydrology 2003, 277, (3–4), 182-194.
    連結:
  10. 19. Grady, L.; Schiwietz, T.; Aharon, S.; Westermann, M., Random walks for interactive organ segmentation in two and three dimensions: Implementation and validation. Medical Image Computing and Computer-Assisted Intervention - Miccai 2005, Pt 2 2005, 3750, 773-780.
    連結:
  11. 21. Vantaram, S. R.; Saber, E., Survey of contemporary trends in color image segmentation. Journal of Electronic Imaging 2012, 21, (4).
    連結:
  12. 22. Borga, M.; Stoffel, M.; Marchi, L.; Marra, F.; Jakob, M., Hydrogeomorphic response to extreme rainfall in headwater systems: Flash floods and debris flows. Journal of Hydrology 2014, 518, 194-205.
    連結:
  13. 25. Li, M. W.; Li, G. L.; Jiang, Y. Z., The Application of the Electrode type Water Level Gauge in Reclaimed Water Treatment Control System. Measurement Technology and Engineering Researches in Industry, Pts 1-3 2013, 333-335, 2297-2300.
    連結:
  14. 26. Ji, Y. N.; Zhang, M. J.; Wang, Y. C.; Wang, P.; Wang, A. B.; Wu, Y.; Xu, H.; Zhang, Y. N., Microwave-Photonic Sensor for Remote Water-Level Monitoring Based on Chaotic Laser. International Journal of Bifurcation and Chaos 2014, 24, (3).
    連結:
  15. 32. Heiner, B.; Barfuss, S. L.; Johnson, M. C., Conditional Assessment of Flow Measurement Accuracy. Journal of Irrigation and Drainage Engineering-Asce 2011, 137, (6), 367-374.
    連結:
  16. 33. Hall, A. C.; Schumann, G. J. P.; Bamber, J. L.; Bates, P. D.; Trigg, M. A., Geodetic corrections to Amazon River water level gauges using ICESat altimetry. Water Resources Research 2012, 48.
    連結:
  17. 34. Hoque, R.; Nakayama, D.; Matsuyama, H.; Matsumoto, J., Flood monitoring, mapping and assessing capabilities using RADARSAT remote sensing, GIS and ground data for Bangladesh. Natural Hazards 2011, 57, (2), 525-548.
    連結:
  18. 35. Boon, J. D.; Heitsenrether, R. M.; Hensley, W. M., Multi-Sensor Evaluation of Microwave Water Level Measurement Error. 2012 OCEANS 2012.
    連結:
  19. 36. Chen, Y. C., Flood discharge measurement of a mountain river - Nanshih River in Taiwan. Hydrology and Earth System Sciences 2013, 17, (5), 1951-1962.
    連結:
  20. 39. Ouma, Y.; Tateishi, R., Urban Flood Vulnerability and Risk Mapping Using Integrated Multi-Parametric AHP and GIS: Methodological Overview and Case Study Assessment. Water 2014, 6, (6), 1515-1545.
    連結:
  21. 41. Schlaffer, S.; Matgen, P.; Hollaus, M.; Wagner, W., Flood detection from multi-temporal SAR data using harmonic analysis and change detection. International Journal of Applied Earth Observation and Geoinformation 2015, 38, 15-24.
    連結:
  22. 42. Schmitt, A.; Brisco, B., Wetland Monitoring Using the Curvelet-Based Change Detection Method on Polarimetric SAR Imagery. Water 2013, 5, (3), 1036-1051.
    連結:
  23. 43. Grings, F. M.; Ferrazzoli, P.; Karszenbaum, H.; Salvia, M.; Kandus, P.; Jacobo-Berlles, J. C.; Perna, P., Model investigation about the potential of C band SAR in herbaceous wetlands flood monitoring. International Journal of Remote Sensing 2008, 29, (17-18), 5361-5372.
    連結:
  24. 44. Simon, R. N.; Tormos, T.; Danis, P. A., Very high spatial resolution optical and radar imagery in tracking water level fluctuations of a small inland reservoir. International Journal of Applied Earth Observation and Geoinformation 2015, 38, (0), 36-39.
    連結:
  25. 47. Sulistioadi, Y. B.; Tseng, K. H.; Shum, C. K.; Hidayat, H.; Sumaryono, M.; Suhardiman, A.; Setiawan, F.; Sunarso, S., Satellite radar altimetry for monitoring small rivers and lakes in Indonesia. Hydrol. Earth Syst. Sci. 2015, 19, (1), 341-359.
    連結:
  26. 49. Pierdicca, N.; Pulvirenti, L.; Chini, M.; Boni, G.; Squicciarino, G.; Candela, L., Flood Mapping by Sar: Possible Approaches to Mitigate Errors Due to Ambiguous Radar Signatures. 2014 Ieee International Geoscience and Remote Sensing Symposium (Igarss) 2014, 3850-3853.
    連結:
  27. 50. Mason, D. C.; Giustarini, L.; Garcia-Pintado, J.; Cloke, H. L., Detection of flooded urban areas in high resolution Synthetic Aperture Radar images using double scattering. International Journal of Applied Earth Observation and Geoinformation 2014, 28, 150-159.
    連結:
  28. 52. Garcia-Pintado, J.; Neal, J. C.; Mason, D. C.; Dance, S. L.; Bates, P. D., Scheduling satellite-based SAR acquisition for sequential assimilation of water level observations into flood modelling. Journal of Hydrology 2013, 495, 252-266.
    連結:
  29. 55. Yucel, I.; Onen, A.; Yilmaz, K. K.; Gochis, D. J., Calibration and evaluation of a flood forecasting system: Utility of numerical weather prediction model, data assimilation and satellite-based rainfall. Journal of Hydrology 2015, 523, 49-66.
    連結:
  30. 57. Olsson, J.; Simonsson, L.; Ridal, M., Rainfall nowcasting: predictability of short-term extremes in Sweden. Urban Water Journal 2015, 12, (1), 3-13.
    連結:
  31. 58. Liu, J.; Wang, J. H.; Pan, S. B.; Tang, K. W.; Li, C. Z.; Han, D. W., A real-time flood forecasting system with dual updating of the NWP rainfall and the river flow. Natural Hazards 2015, 77, (2), 1161-1182.
    連結:
  32. 60. Garcia-Pintado, J.; Mason, D. C.; Dance, S. L.; Cloke, H. L.; Neal, J. C.; Freer, J.; Bates, P. D., Satellite-supported flood forecasting in river networks: A real case study. Journal of Hydrology 2015, 523, 706-724.
    連結:
  33. 62. Pandey, R. K.; Cretaux, J. F.; Berge-Nguyen, M.; Tiwari, V. M.; Drolon, V.; Papa, F.; Calmant, S., Water level estimation by remote sensing for the 2008 flooding of the Kosi River. International Journal of Remote Sensing 2014, 35, (2), 424-440.
    連結:
  34. 63. Chen, S.; Liu, H. J.; You, Y. L.; Mullens, E.; Hu, J. J.; Yuan, Y.; Huang, M. Y.; He, L.; Luo, Y. M.; Zeng, X. J.; Tang, G. Q.; Hong, Y., Evaluation of High-Resolution Precipitation Estimates from Satellites during July 2012 Beijing Flood Event Using Dense Rain Gauge Observations. Plos One 2014, 9, (4).
    連結:
  35. 69. Schumann, G. J. P.; Neal, J. C.; Voisin, N.; Andreadis, K. M.; Pappenberger, F.; Phanthuwongpakdee, N.; Hall, A. C.; Bates, P. D., A first large-scale flood inundation forecasting model. Water Resources Research 2013, 49, (10), 6248-6257.
    連結:
  36. 70. Schumann, G. J. P.; Bates, P. D.; Neal, J. C.; Andreadis, K. M., Technology: Fight floods on a global scale. Nature 2014, 507, (7491), 169-169.
    連結:
  37. 82. Singh, H. K.; Meitei, N. C.; Sarkar, S. T.; Tiwari, D.; Bezboruah, T., Truly Nonintrusive Liquid-Level-Sensing Method Based on Lateral Displacement Effect of Light Rays. Ieee Sensors Journal 2013, 13, (2), 801-806.
    連結:
  38. 83. Singh, H. K.; Chamuah, N.; Sarkar, D.; Bezboruah, T., Non-Intrusive Technique for Measuring Refractive Index of Clear and Transparent Liquids. Ieee Sensors Journal 2014, 14, (2), 313-314.
    連結:
  39. 84. Lin, C.-Y.; Chu, E.; Ku, L.-W.; Liu, J., Active Disaster Response System for a Smart Building. Sensors 2014, 14, (9), 17451-17470.
    連結:
  40. 85. Lynggaard, P.; Skouby, K. E., Deploying 5G-Technologies in Smart City and Smart Home Wireless Sensor Networks with Interferences. Wireless Personal Communications 2015, 81, (4), 1399-1413.
    連結:
  41. 87. He, K.; Sun, J.; Tang, X., Single image haze removal using dark channel prior. Pattern Analysis and Machine Intelligence, IEEE Transactions on 2011, 33, (12), 2341-2353.
    連結:
  42. 88. Fabijanska, A.; Goclawski, J., New accelerated graph-based method of image segmentation applying minimum spanning tree. Iet Image Processing 2014, 8, (4), 239-251.
    連結:
  43. 89. Felzenszwalb, P.; Felzenszwalb, D., Efficient Graph-Based Image Segmentation. International journal of computer vision 2004, 59, (2), 167-181.
    連結:
  44. 91. Ziegler, A. D.; Lim, H.; Tantasarin, C.; Jachowski, N. R.; Wasson, R., Floods, false hope, and the future. Hydrological Processes 2012, 26, (11), 1748-1750.
    連結:
  45. 92. Lee, C. S.; Huang, L. R.; Chen, D. Y. C., The modification of the typhoon rainfall climatology model in Taiwan. Nat. Hazards Earth Syst. Sci. 2013, 13, (1), 65-74.
    連結:
  46. 95. Yen, H. H.; Xiong, H. K.; Lee, I., Recent Advances in Wireless Visual Sensor Networks. International Journal of Distributed Sensor Networks 2014.
    連結:
  47. 1. Webster, P. J., Meteorology: Improve weather forecasts for the developing world. Nature 2013, 493, (7430), 17-19.
  48. 5. Ratnapradipa, D., 2012 NEHA/UL Sabbatical Report Vulnerability to Potential Impacts of Climate Change: Adaptation and Risk Communication Strategies for Environmental Health Practitioners in the United Kingdom. Journal of Environmental Health 2014, 76, (8), 28-33.
  49. 7. Lin, P. F.; Chang, P. L.; Jou, B. J. D.; Wilson, J. W.; Roberts, R. D., Objective Prediction of Warm Season Afternoon Thunderstorms in Northern Taiwan Using a Fuzzy Logic Approach. Weather and Forecasting 2012, 27, (5), 1178-1197.
  50. 8. Lee, C.-S.; Ho, H.-Y.; Lee, K. T.; Wang, Y.-C.; Guo, W.-D.; Chen, D. Y.-C.; Hsiao, L.-F.; Chen, C.-H.; Chiang, C.-C.; Yang, M.-J.; Kuo, H.-C., Assessment of sewer flooding model based on ensemble quantitative precipitation forecast. Journal of Hydrology 2013, 506, (0), 101-113.
  51. 10. Chen, N.; Wang, K.; Xiao, C.; Gong, J., A heterogeneous sensor web node meta-model for the management of a flood monitoring system. Environmental Modelling & Software 2014, 54, (0), 222-237.
  52. 11. Heilig, G. K., World urbanization prospects: the 2011 revision. United Nations, Department of Economic and Social Affairs (DESA), Population Division, Population Estimates and Projections Section, New York 2012.
  53. 16. Fujita, I.; Watanabe, H.; Tsubaki, R., Development of a non‐intrusive and efficient flow monitoring technique: The space‐time image velocimetry (STIV). International Journal of River Basin Management 2007, 5, (2), 105-114.
  54. 17. Qin, C. C.; Zhang, G. P.; Zhou, Y. C.; Tao, W. B.; Cao, Z. G., Integration of the saliency-based seed extraction and random walks for image segmentation. Neurocomputing 2014, 129, 378-391.
  55. 18. Ducournau, A.; Bretto, A., Random walks in directed hypergraphs and application to semi-supervised image segmentation. Comput. Vis. Image Underst. 2014, 120, 91-102.
  56. 20. Foggia, P.; Percannella, G.; Vento, M., Graph Matching and Learning in Pattern Recognition in the Last 10 Years. International Journal of Pattern Recognition and Artificial Intelligence 2014, 28, (1).
  57. 23. Borga, M.; Anagnostou, E. N.; Bloschl, G.; Creutin, J. D., Flash flood forecasting, warning and risk management: the HYDRATE project. Environmental Science & Policy 2011, 14, (7), 834-844.
  58. 24. Marin-Perez, R.; Garcia-Pintado, J.; Gomez, A. S., A Real-Time Measurement System for Long-Life Flood Monitoring and Warning Applications. Sensors 2012, 12, (4), 4213-4236.
  59. 27. Wei, R.; Sudau, A., Geodetic aspects of water-level gauge elevations/elevation changes and gauge set-points in coastal waters. Hydrologie Und Wasserbewirtschaftung 2012, 56, (5), 257-275.
  60. 28. Cretaux, J. F.; Jelinski, W.; Calmant, S.; Kouraev, A.; Vuglinski, V.; Berge-Nguyen, M.; Gennero, M. C.; Nino, F.; Del Rio, R. A.; Cazenave, A.; Maisongrande, P., SOLS: A lake database to monitor in the Near Real Time water level and storage variations from remote sensing data. Advances in Space Research 2011, 47, (9), 1497-1507.
  61. 29. Vittucci, C.; Guerriero, L.; Ferrazzoli, P.; Rahmoune, R.; Barraza, V.; Grings, F., River Water Level Prediction Using Passive Microwave Signatures-A Case Study: The Bermejo Basin. Ieee Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2014, 7, (9), 3903-3914.
  62. 30. Biswas, R. K.; Jayawardena, A. W., Water Level Prediction by Artificial Neural Network in a Flashy Transboundary River of Bangladesh. Global Nest Journal 2014, 16, (2), 432-444.
  63. 31. Zheng, G. L.; Zong, H. Y.; Zhuan, X. T.; Wang, L. J., High-Accuracy Surface-Perceiving Water Level Gauge With Self-Calibration for Hydrography. Ieee Sensors Journal 2010, 10, (12), 1893-1900.
  64. 37. Boon, J. D., Reducing Wave-Induced Microwave Water-Level Measurement Error with a Least Squares-Designed Digital Filter*. Journal of Atmospheric and Oceanic Technology 2014, 31, (2), 491-502.
  65. 38. Korostynska, O.; Mason, A.; Al-Shamma'a, A., Microwave sensors for the non-invasive monitoring of industrial and medical applications. Sensor Review 2014, 34, (2), 182-191.
  66. 40. Alsdorf, D. E.; Rodríguez, E.; Lettenmaier, D. P., Measuring surface water from space. Reviews of Geophysics 2007, 45, (2), RG2002.
  67. 45. Becker, M.; da Silva, J. S.; Calmant, S.; Robinet, V.; Linguet, L.; Seyler, F., Water Level Fluctuations in the Congo Basin Derived from ENVISAT Satellite Altimetry. Remote Sensing 2014, 6, (10), 9340-9358.
  68. 46. Ticehurst, C.; Guerschman, J.; Chen, Y., The Strengths and Limitations in Using the Daily MODIS Open Water Likelihood Algorithm for Identifying Flood Events. Remote Sensing 2014, 6, (12), 11791-11809.
  69. 48. Tarpanelli, A.; Brocca, L.; Barbetta, S.; Faruolo, M.; Lacava, T.; Moramarco, T., Coupling MODIS and Radar Altimetry Data for Discharge Estimation in Poorly Gauged River Basins. Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal of 2015, 8, (1), 141-148.
  70. 51. Long, S.; Fatoyinbo, T. E.; Policelli, F., Flood extent mapping for Namibia using change detection and thresholding with SAR. Environmental Research Letters 2014, 9, (3).
  71. 53. Martinis, S.; Kersten, J.; Twele, A., A fully automated TerraSAR-X based flood service. ISPRS Journal of Photogrammetry and Remote Sensing 2015, 104, (0), 203-212.
  72. 54. Giustarini, L.; Vernieuwe, H.; Verwaeren, J.; Chini, M.; Hostache, R.; Matgen, P.; Verhoest, N. E. C.; De Baets, B., Accounting for image uncertainty in SAR-based flood mapping. International Journal of Applied Earth Observation and Geoinformation 2015, 34, (0), 70-77.
  73. 56. Peters, J. M.; Schumacher, R. S., Mechanisms for Organization and Echo Training in a Flash-Flood-Producing Mesoscale Convective System. Monthly Weather Review 2015, 143, (4), 1058-1085.
  74. 59. Lin, G. F.; Jhong, B. C., A real-time forecasting model for the spatial distribution of typhoon rainfall. Journal of Hydrology 2015, 521, 302-313.
  75. 61. Khan, S.; Hong, Y.; Gourley, J.; Khattak, M.; De Groeve, T., Multi-Sensor Imaging and Space-Ground Cross-Validation for 2010 Flood along Indus River, Pakistan. Remote Sensing 2014, 6, (3), 2393-2407.
  76. 64. Roque, D.; Afonso, N.; Fonseca, A. M.; Heleno, S., OBIA Flood Delimitation Assisted by Threshold Determination with Principal Component Analysis. Photogrammetric Engineering and Remote Sensing 2014, 80, (6), 551-557.
  77. 65. Lo, S.-W.; Jyh-Horng, W.; Lun-Chi, C.; Chien-Hao, T.; Fang-Pang, L. In Flood Tracking in Severe Weather, International Symposium on Computer, Consumer and Control, 2014; 2014; pp 27-30.
  78. 66. Lo, S.-W.; Jyh-Horng, W.; Lun-Chi, C.; Chien-Hao, T.; Fang-Pang, L. In Fluvial monitoring and flood response, Sensors Applications Symposium (SAS), 2014 IEEE, 18-20 Feb. 2014, 2014; 2014; pp 378-381.
  79. 67. Zhang, Q.; Zhang, J.; Jiang, L.; Liu, X.; Tong, Z., Flood Disaster Risk Assessment of Rural Housings — A Case Study of Kouqian Town in China. International Journal of Environmental Research and Public Health 2014, 11, (4), 3787-3802.
  80. 68. Sowmya, K.; John, C. M.; Shrivasthava, N. K., Urban flood vulnerability zoning of Cochin City, southwest coast of India, using remote sensing and GIS. Natural Hazards 2015, 75, (2), 1271-1286.
  81. 71. Delgoda, D. K.; Saleem, S. K.; Halgamuge, M. N.; Malano, H., Multiple Model Predictive Flood Control in Regulated River Systems with Uncertain Inflows. Water Resources Management 2013, 27, (3), 765-790.
  82. 72. Liu, Y. C.; Liu, C. L., A Solution for Flood Control in Urban Area: Using Street Block and Raft Foundation Space Operation Model. Water resources management 2014, 28, (14), 4985-4998.
  83. 73. Castillo-Effer, M.; Quintela, D. H.; Moreno, W.; Jordan, R.; Westhoff, W., Wireless sensor networks for flash-flood alerting. 2004, 1, 142-146.
  84. 74. Basha, E. A.; Ravela, S.; Rus, D., Model-based monitoring for early warning flood detection. Proceedings of the 6th ACM conference on Embedded network sensor systems 2008, 295-308.
  85. 75. Krzhizhanovskaya, V. V.; Shirshov, G. S.; Melnikova, N. B.; Belleman, R. G.; Rusadi, F. I.; Broekhuijsen, B. J.; Gouldby, B. P.; Lhomme, J.; Balis, B.; Bubak, M.; Pyayt, A. L.; Mokhov, I. I.; Ozhigin, A. V.; Lang, B.; Meijer, R. J., Flood early warning system: design, implementation and computational modules. Procedia Computer Science 2011, 4, (0), 106-115.
  86. 76. Gilmore, T. E.; Birgand, F.; Chapman, K. W., Source and magnitude of error in an inexpensive image-based water level measurement system. Journal of Hydrology 2013, 496, 178-186.
  87. 77. Chakravarthy, S.; Sharma, R.; Kasturi, R., Noncontact level sensing technique using computer vision. Instrumentation and Measurement, IEEE Transactions on 2002, 51, (2), 353-361.
  88. 78. Takagi, Y.; Tsujikawa, A.; Takato, M.; Saito, T.; Kaida, M., Development of a noncontact liquid level measuring system using image processing. Water Science and Technology 1998, 37, (12), 381-387.
  89. 79. Yu, J.; Hahn, H., Remote Detection and Monitoring of a Water Level Using Narrow Band Channel. Journal of Information Science and Engineering 2010, 26, (1), 71-82.
  90. 80. Nguyen, L. S.; Schaeli, B.; Sage, D.; Kayal, S.; Jeanbourquin, D.; Barry, D. A.; Rossi, L., Vision-based system for the control and measurement of wastewater flow rate in sewer systems. Water Science and Technology 2009, 60, (9), 2281-2289.
  91. 81. Farias, P. S. C.; Martins, F. J. W. A.; Sampaio, L. E. B.; Serfaty, R.; Azevedo, L. F. A., Liquid film characterization in horizontal, annular, two-phase, gas-liquid flow using time-resolved laser-induced fluorescence. Experiments in Fluids 2012, 52, (3), 633-645.
  92. 86. Jablonski, I., Smart Transducer Interface-From Networked On-Site Optimization of Energy Balance in Research-Demonstrative Office Building to Smart City Conception. Ieee Sensors Journal 2015, 15, (5).
  93. 90. Ziegler, A. D., Water management: Reduce urban flood vulnerability. Nature 2012, 481, (7380), 145-145.
  94. 93. Lee, C. S.; Ho, H. Y.; Lee, K. T.; Wang, Y. C.; Guo, W. D.; Chen, D. Y. C.; Hsiao, L. F.; Chen, C. H.; Chiang, C. C.; Yang, M. J.; Kuo, H. C., Assessment of sewer flooding model based on ensemble quantitative precipitation forecast. Journal of Hydrology 2013, 506, 101-113.
  95. 94. Costa, D.; Guedes, L.; Vasques, F.; Portugal, P., Research Trends in Wireless Visual Sensor Networks When Exploiting Prioritization. Sensors 2015, 15, (1), 1760-1784.
  96. 96. Tabuada, P.; Caliskan, S. Y.; Rungger, M.; Majumdar, R., Towards Robustness for Cyber-Physical Systems. Ieee Transactions on Automatic Control 2014, 59, (12), 3151-3163.
  97. 97. Rajhans, A.; Bhave, A.; Ruchkin, I.; Krogh, B. H.; Garlan, D.; Platzer, A.; Schmerl, B., Supporting Heterogeneity in Cyber-Physical Systems Architectures. Ieee Transactions on Automatic Control 2014, 59, (12), 3178-3193.
  98. 98. Wassenberg, J.; Middelmann, W.; Sanders, P., An Efficient Parallel Algorithm for Graph-Based Image Segmentation. Computer Analysis of Images and Patterns, Proceedings 2009, 5702, 1003-1010.
  99. 99. Yap, F. G. H.; Yen, H. H., A Survey on Sensor Coverage and Visual Data Capturing/Processing/Transmission in Wireless Visual Sensor Networks. Sensors 2014, 14, (2), 3506-3527.