Integrating rapid assessment of flood proneness into urban planning under data constraints: a fuzzy logic and bricolage approach

REFERENCES

• Aksoy, H., Kirca, V. S. O., Burgan, H. I., & Kellecioglu, D. (2016, May 18–20). Hydrological and hydraulic models for determination of flood-prone and flood inundation areas.7th International Water Resources Management Conference of ICWRS, Bochum, Germany, IWRM2016-86-1.
   Google Scholar
• Al-Abadi, A. M., Shahid, S., & Al-Ali, A. K. (2016). A GIS-based integration of catastrophe theory and analytical hierarchy process for mapping flood susceptibility: A case study of Teeb area, Southern Iraq. Environmental Earth Sciences, 75(8), 1–19. doi:10.1007/s12665-016-5523-7
   Web of Science ®Google Scholar
• Araya-Muñoz, D., Metzger, M. J., Stuart, N., Wilson, A. M. W., & Carvajal, D. (2017). A spatial fuzzy logic approach to urban multi-hazard impact assessment in Concepcion, Chile. Science of the Total Environment, 576, 508–519. doi:10.1016/j.scitotenv.2016.10.077
   PubMed Web of Science ®Google Scholar
• Ashrafi, M., Hock Chye Chua, L., Quek, C., & Qin, X. (2016). A fully-online Neuro-Fuzzy model for flow forecasting in basins with limited data. Journal of Hydrology, 545, 424–435. doi:10.1016/j.jhydrol.2016.11.057
   Web of Science ®Google Scholar
• Balica, S. F., Wright, N. G., & van der Meulen, F. (2012). A flood vulnerability index for coastal cities and its use in assessing climate change impacts. Natural Hazards, 64. doi:10.1007/s11069-012-0234-1
   Web of Science ®Google Scholar
• Bardhan, R., Debnath, R., & Bandopadhyay, S. (2016). A conceptual model for identifying the risk susceptibility of urban green spaces using geo-spatial techniques. Modeling Earth Systems and Environment, 2(3), 144. doi:10.1007/s40808-016-0202-y
   Web of Science ®Google Scholar
• Bardhan, R., Kurisu, K., & Hanaki, K. (2015). Does compact urban forms relate to good quality of life in high density cities of India? Case of Kolkata. Cities, 48, 55–65. doi:10.1016/j.cities.2015.06.005
   Web of Science ®Google Scholar
• Bezuijen, M. R, Morgan, C, & Mather, R.J. (2011). A rapid vulnerability assessment of coastal habitats and selected species to climate risks in chanthaburi and trat(Thailand), Koh Kong and Kampot (Cambodia), and Kien Giang, Ben Tre, Soc Trang and Can Gio (Vietnam). Gland, Switzerland: IUCN.
   Google Scholar
• Blazkova, S., & Beven, K. (1997). Flood frequency prediction for data limited catchments in the Czech Republic using a stochastic rainfall model and TOPMODEL. Journal of Hydrology, 195(1–4), 256–278. doi:10.1016/S0022-1694(96)03238-6
   Web of Science ®Google Scholar
• Bubeck, P., Botzen, W. J. W., & Aerts, J. C. J. H. (2012). A review of risk perceptions and other factors that influence flood mitigation behavior. Risk Analysis, 32(9), 1481–1495. doi:10.1111/j.1539-6924.2011.01783.x
   PubMed Web of Science ®Google Scholar
• Butler, C., & Pidgeon, N. (2011). From ‘flood defence’ to ‘flood risk management’: Exploring governance, responsibility, and blame. Environment and Planning C: Government and Policy, 29(3), 533–547. doi:10.1068/c09181j
   Web of Science ®Google Scholar
• Cao, C., Xu, P., Wang, Y., Chen, J., Zheng, L., & Niu, C. (2016). Flash flood hazard susceptibility mapping using frequency ratio and statistical index methods in coalmine subsidence areas. Sustainability (Switzerland), 8, 9. doi:10.3390/su8090948
   Web of Science ®Google Scholar
• Dasgupta, S., Gosain, A. K., Rao, S., Roy, S., & Sarraf, M. (2013). A megacity in a changing climate: The case of Kolkata. Climatic Change, 116(3–4), 747–766. doi:10.1007/s10584-012-0516-3
   Web of Science ®Google Scholar
• De Brito, M. M., & Evers, M. (2016). Multi-criteria decision-making for flood risk management: A survey of the current state of the art. Natural Hazards and Earth System Sciences, 16(4), 1019–1033. doi:10.5194/nhess-16-1019-2016
   Web of Science ®Google Scholar
• Faulkner, H., McCarthy, S., & Tunstall, S. (2010). Flood risk communication. In G. Pender & H. Faulkner (Eds.), Flood Risk Science and Management (pp. 386–406). Oxford: Wiley-Blackwell. https://doi.org/10.1002/9781444324846.ch19
   Google Scholar
• Fayne, J. V., Bolten, J. D., Doyle, C. S., Fuhrmann, S., Rice, M. T., Houser, P. R., & Lakshmi, V. (2017). Flood mapping in the lower Mekong River Basin using daily MODIS observations. International Journal of Remote Sensing, 38(6), 1737–1757. doi:10.1080/01431161.2017.1285503
   Web of Science ®Google Scholar
• Feng, Q., Gong, J., Liu, J., & Li, Y. (2015). Flood mapping based on multiple endmember spectral mixture analysis and random forest classifier-the case of Yuyao, China. Remote Sensing, 7(9), 12539–12562. doi:10.3390/rs70912539
   Web of Science ®Google Scholar
• Fernández, D. S., & Lutz, M. A. (2010). Urban flood hazard zoning in Tucuman Province, Argentina, using GIS and multicriteria decision analysis. Engineering Geology, 111(1–4), 90–98. doi:10.1016/j.enggeo.2009.12.006
   Web of Science ®Google Scholar
• Ganaie, H. A., Hashaia, H., & Kalota, D. (2013). Delineation of flood prone area using Normalized Difference Water Index (NDWI) and transect method: A case study of Kashmir Valley. International Journal of Remote Sensing Applications, 3(2), 53–58.
   Google Scholar
• Garg, P. K., & Garg, R. D. (2016). Geospatial techniques for flood inundation mapping Geomatics Engineering Group, Civil Engineering, Indian Institute of Technology Roorkee, India. In Geoscience and Remote Sensing Symposium (IGARSS), 2016 IEEE International Geoscience and Remote Sensing Symposium, (pp. 4387–4390). Beijing: IEEE. https://doi.org/10.1109/IGARSS.2016.7730143
   Google Scholar
• Gnecco, G., Morisi, R., Roth, G., Sanguineti, M., & Taramasso, A. C. (2016). Supervised and semi-supervised classifiers for the detection of flood-prone areas. Soft Computing. doi:10.1007/s00500-015-1983-z
   PubMed Web of Science ®Google Scholar
• He, Y., Zhou, J., Kou, P., Lu, N., & Zou, Q. (2011). A fuzzy clustering iterative model using chaotic differential evolution algorithm for evaluating flood disaster. Expert Systems with Applications, 38(8), 10060–10065. doi:10.1016/j.eswa.2011.02.003
   Web of Science ®Google Scholar
• Herk, S. V., Zevenbergen, C., Ashley, R., & Rijke, J. (2011). Learning and Action Alliances for the integration of flood risk management into urban planning: A new framework from empirical evidence from The Netherlands. Environmental Science and Policy, 14(5), 543–554. doi:10.1016/j.envsci.2011.04.006
   Web of Science ®Google Scholar
• Hundecha, Y., Bardossy, A., & Werner, H.-W. (2001). Development of a fuzzy logic-based rainfall-runoff model. Hydrological Sciences Journal, 46(3), 363–376. doi:10.1080/02626660109492832
   Web of Science ®Google Scholar
• IPCC. (2012). Managing the risks of extreme events and disasters to advance climate change adaptation. IPCC. doi:10.1596/978-0-8213-8845-7
   Google Scholar
• Islam, A. S., Bala, S. K., & Haque, M. A. (2010). Flood inundation map of Bangladesh using MODIS time-series images. Journal of Flood Risk Management, 3(3), 210–222. doi:10.1111/j.1753-318X.2010.01074.x
   Web of Science ®Google Scholar
• Jameson, S., & Baud, I. (2016). Varieties of knowledge for assembling an urban flood management governance configuration in Chennai, India. Habitat International, 54, 112–123. doi:10.1016/j.habitatint.2015.12.015
   Web of Science ®Google Scholar
• Jihad, F., & Jacques, T. (2012). Bricolage planning: Understanding planning in a fragmented city. In D. S. Polyzos (Ed), Urban Development. Available from: https://www.intechopen.com/books/urban-development/bricolage-planning-understanding-planning-in-a-fragmented-city
   Google Scholar
• Kazakis, N., Kougias, I., & Patsialis, T. (2015). Assessment of flood hazard areas at a regional scale using an index-based approach and analytical hierarchy process: Application in Rhodope-Evros region, Greece. Science of the Total Environment, 538, 555–563. doi:10.1016/j.scitotenv.2015.08.055
   PubMed Web of Science ®Google Scholar
• Khosravi, K., Pourghasemi, H. R., Chapi, K., & Bahri, M. (2016). Flash flood susceptibility analysis and its mapping using different bivariate models in Iran: A comparison between Shannon’s entropy, statistical index, and weighting factor models. Environmental Monitoring and Assessment, 188(656), 5665–5669. doi:10.1007/s10661-016-5665-9
   Web of Science ®Google Scholar
• Kia, M. B., Pirasteh, S., Pradhan, B., Mahmud, A. R., Sulaiman, W. N. A., & Moradi, A. (2012). An artificial neural network model for flood simulation using GIS: Johor River Basin, Malaysia. Environmental Earth Sciences, 67(1), 251–264. doi:10.1007/s12665-011-1504-z
   Web of Science ®Google Scholar
• Lévi-Strauss, C. (1967). The savage mind. Chicago: University of Chicago Press.
   Google Scholar
• Li, C., Cheng, X., Li, N., Du, X., Yu, Q., & Kan, G. (2016). A framework for flood risk analysis and benefit assessment of flood control measures in Urban Areas. International Journal of Environmental Research and Public Health, 13, 8. doi:10.3390/ijerph13080787
   Web of Science ®Google Scholar
• Li, Z., Wang, C., Emrich, C. T., & Guo, D. (2017). A novel approach to leveraging social media for rapid flood mapping: A case study of the 2015 South Carolina floods. Cartography and Geographic Information Science, 0(0), 1–14. doi:10.1080/15230406.2016.1271356
   Google Scholar
• Lin, L., Di, L., Yu, E. G., Kang, L., Shrestha, R., Rahman, M. S., … Hu, L. (2016). A review of remote sensing in flood assessment. 2016 5th International Conference on Agro-Geoinformatics, Agro-Geoinformatics 2016. 10.1109/Agro-Geoinformatics.2016.7577655
   Google Scholar
• Lohani, A. K., Kumar, R., & Singh, R. D. (2012). Hydrological time series modeling: A comparison between adaptive neuro-fuzzy, neural network and autoregressive techniques. Journal of Hydrology, 442–443, 23–35. doi:10.1016/j.jhydrol.2012.03.031
   Web of Science ®Google Scholar
• McFeeters, S. K. (1996). The use of the normalized difference water index (NDWI) in the delineation of open water features. International Journal Of Remote Sensing, 17(7), 1425–1432. doi:10.1080/01431169608948714
   Web of Science ®Google Scholar
• McFeeters, S. K. (2013). Using the normalized difference water index (NDWI) within a geographic information system to detect swimming pools for mosquito abatement: A practical approach. Remote Sensing, 5(7), 3544–3561. doi:10.3390/rs5073544
   Web of Science ®Google Scholar
• Meyer, V., Haase, D., & Scheuer, S. (2009). A multicriteria flood risk assessment and mapping approach. Flood Risk Management Research and Practice, (January), 1687–1694. doi:10.1201/9780203883020.ch200
   Google Scholar
• Mukerji, A., Chatterjee, C., & Raghuwanshi, N. S. (2009). Flood forecasting using ANN, Neuro-Fuzzy, and neuro-GA models. Journal of Hydrologic Engineering, 14(6), 647–652. doi:10.1061/(ASCE)HE.1943-5584.0000040
   Web of Science ®Google Scholar
• Nitivattananon, V., Tu, T.T., Rattanapan, A., & Asavanant, J. (2012). Chapter 31: Vulnerability and Resilience of Urban Communities under Coastal Hazard Conditions in Southeast Asia. In D. Hoonweg, M. Freira, M. Lee, P. Bhada, & B. Yuen, (Eds.), Cities and climate change: Responding to an urgent agenda. Washington, D.C.: World Bank.
   Google Scholar
• Pacheco-Torres, R., Roldán, J., Gago, E. J., & Ordóñez, J. (2017). Assessing the relationship between urban planning options and carbon emissions at the use stage of new urbanized areas: A case study in a warm climate location. Energy and Buildings, 136, 73–85. doi:10.1016/j.enbuild.2016.11.055
   Web of Science ®Google Scholar
• Qiu, L., Du, Z., Zhu, Q., & Fan, Y. (2017). An integrated flood management system based on linking environmental models and disaster-related data. Environmental Modelling & Software, 91, 111–126. doi:10.1016/j.envsoft.2017.01.025
   Web of Science ®Google Scholar
• Raines, G. L., Sawatzky, D. L., & Bonham-Carter, G. F. (2010). Incorporating expert knowledge – New fuzzy logic tools in ArcGIS 10 (ArcUser, Spring, 8–13). Retrieved from www.esri.com
   Google Scholar
• Ran, J., & Nedovic-Budic, Z. (2016). Integrating spatial planning and flood risk management: A new conceptual framework for the spatially integrated policy infrastructure. Computers, Environment and Urban Systems, 57, 68–79. doi:10.1016/j.compenvurbsys.2016.01.008
   Web of Science ®Google Scholar
• Rathjens, H., Bieger, K., Chaubey, I., Arnold, J. G., Allen, P. M., Srinivasan, R., … Volk, M. (2016). Delineating floodplain and upland areas for hydrologic models: A comparison of methods. Hydrological Processes, 30(23), 4367–4383. doi:10.1002/hyp.10918
   Web of Science ®Google Scholar
• Rosser, J. F., Leibovici, D. G., & Jackson, M. J. (2017). Rapid flood inundation mapping using social media, remote sensing and topographic data. Natural Hazards, 1–18. doi:10.1007/s11069-017-2755-0
   Web of Science ®Google Scholar
• Sander, S., Hoppe, H., & Schlobinski, S. (2011). Integrating climate change in the urban planning process – A case study (Book Chapter, 1–10). Retrieved from papers://ef64220a-a077-48ec-ae81-be13b32d2073/Paper/p395
   Google Scholar
• Sofia, G., Roder, G., Dalla Fontana, G., & Tarolli, P. (2017). Flood dynamics in urbanised landscapes: 100 years of climate and humans’ interaction. Scientific Reports, 7(40527), 1–12. doi:10.1038/srep40527
   PubMed Web of Science ®Google Scholar
• Soulis, K. X., & Valiantzas, J. D. (2012). SCS-CN parameter determination using rainfall-runoff data in heterogeneous watersheds-the two-CN system approach. Hydrology and Earth System Sciences, 16(3), 1001–1015. doi:10.5194/hess-16-1001-2012
   Web of Science ®Google Scholar
• Suriya, S., & Mudgal, B. V. (2012). Impact of urbanization on flooding: The Thirusoolam sub watershed – A case study. Journal of Hydrology, 412–413, 210–219. doi:10.1016/j.jhydrol.2011.05.008
   Web of Science ®Google Scholar
• Tehrany, M. S., Pradhan, B., Mansor, S., & Ahmad, N. (2015). Flood susceptibility assessment using GIS-based support vector machine model with different kernel types. Catena, 125, 91–101. doi:10.1016/j.catena.2014.10.017
   Web of Science ®Google Scholar
• Teng, J., Jakeman, A. J., Vaze, J., Croke, B. F. W., Dutta, D., & Kim, S. (2017). Flood inundation modelling: A review of methods, recent advances and uncertainty analysis. Environmental Modelling & Software, 90, 201–216. doi:10.1016/j.envsoft.2017.01.006
   Web of Science ®Google Scholar
• Tien Bui, D., Pradhan, B., Nampak, H., Bui, Q.-T., Tran, Q.-A., & Nguyen, Q.-P. (2016). Hybrid artificial intelligence approach based on neural fuzzy inference model and metaheuristic optimization for flood susceptibility modeling in a high-frequency tropical cyclone area using GIS. Journal of Hydrology, 540, 317–330. doi:10.1016/j.jhydrol.2016.06.027
   Web of Science ®Google Scholar
• UN-Habitat. (2011). The state of Asian cities. Retrieved from www.unhabitat.org
   Google Scholar
• Vakhshoori, V., & Zare, M. (2016). Landslide susceptibility mapping by comparing weight of evidence, fuzzy logic, and frequency ratio methods. Geomatics, Natural Hazards and Risk, 7(5), 1731–1752. doi:10.1080/19475705.2016.1144655
   Web of Science ®Google Scholar
• World Bank. (2011). India – Vulnerability of Kolkata metropolitan area to increased precipitation in a changing climate (53282), 1–101. Retrieved from https://doi.org/Report No. 53282-IN
   Google Scholar
• Xiao, B., Wang, Q. H., Fan, J., Han, F. P., & Dai, Q. H. (2011). Application of the SCS-CN model to runoff estimation in a small watershed with high spatial heterogeneity. Pedosphere, 21(6), 738–749. doi:10.1016/S1002-0160(11)60177-X
   Web of Science ®Google Scholar
• Yahaya, S., Ahmad, N., & Abdalla, R. F. (2010). Multicriteria analysis for flood vulnerable areas in Hadejia-Jama’are River Basin, Nigeria. European Journal of Scientific Research, 42(1), 71–83.
   Google Scholar
• Yao, L., Chen, L., & Wei, W. (2017). Exploring the linkage between Urban flood risk and spatial patterns in small urbanized catchments of Beijing, China. International Journal of Environmental Research and Public Health, 14(3), 239. doi:10.3390/ijerph14030239
   Web of Science ®Google Scholar
• Zadeh, L. A. (1965). Fuzzy sets. Information and Control, 8(3), 338–353. doi:10.1016/S0019-9958(65)90241-X
   Google Scholar