Flood prevention solutions using remote sensing and agent-based modeling (Case study: Shoush city)

Minaei, Mojde; Vahidnia, Mohammad H.

doi:10.22111/jneh.2022.38718.1812

Flood prevention solutions using remote sensing and agent-based modeling (Case study: Shoush city)

Document Type : Research Article

Authors

Mojde Minaei ¹
Mohammad H. Vahidnia ²

¹ PhD Student, Department of Remote Sensing and GIS, Faculty of Natural Resources and Environment, Science and Research Branch, Islamic Azad University, Tehran, Iran

² Assistant Professor, Department of Remote Sensing and GIS, Faculty of Natural Resources and Environment, Science and Research Branch, Islamic Azad University, Tehran, Iran

10.22111/jneh.2022.38718.1812

Abstract

Among all the natural hazards in the country, according to recorded statistics and observations, floods have been the most destructive, and it has the highest frequency of occurrence. Floods are one of the known natural disasters that cause a lot of financial and human losses. This phenomenon can be controlled by identifying flood-prone areas and proper management. In the current era, due to human encroachment on rivers and land-use change or destruction of vegetation, flood damage has increased. These factors cause, in addition to increasing human and financial losses, damages such as soil erosion upstream and sedimentation downstream. In this research, using agent-based modeling in the NetLogo simulation environment, flood-prone areas in Shush city have been identified. The most important input was topography (digital elevation model) and then dynamic and temporal simulation was done by performing tessellation on the area and considering the rainfall in each cell as an agent. Using spatial analysis in ArcGIS software and comparing the simulation results with the location of the city and land use maps of the region, the possible causes of floods in this region have been investigated. Agent-based models with the incorporation of geospatial information systems (GIS) can be used as a new solution to solve spatial problems such as natural crises, destructive environmental impacts, and so on. Finally, preventive measures to prevent floods in this area are proposed.

Keywords

20.1001.1.26764377.1401.11.33.11.2

Main Subjects

Crisis-management

References

References (in Persian)

Sabzevar, A.D., et al. (1385). Application of GIS and remote sensing in flood zoning studies, in 7th International River Engineering Conference. [In Persian]

Soltani, A., M. Soltani, and K. Soleimani. (1397). Evaluation of groundwater quality in Shousha city for drinking. Ecohydrology. 5(4), 1135-1146, https://dx.doi.org/10.22059/ije.2018.257339.873 [In Persian]

Tabasi, M. and A.A. AleSheikh. (1395). NetLogo Simulation Environment: A tool for creating spatially based operating models, in The 2nd National Conference on Geospatial Information Technology. [In Persian]

References (in English)

(ERSS), E.R.C.C., Iran | Flood wrap-up March-April. 2019.

Alfieri, L., Bisselink, B., Dottori, F., Naumann. G., De Roo, A., Salamon, P., Wyser, K., Feyen, L. (2017). Global projections of river flood risk in a warmer world. Earth's Future. 5(2), 171-182, https://doi.org/10.1002/2016EF000485

Azari, H., Motakan, A.A., Shakiba, A.R., Pourali, Sh. (2009). Simulation and flood warning by integrating hydrological models in GIS and estimating precipitation through remote sensing. Geology of Iran. 3, 39-53.

Berglund, E.Z. (2015). Using agent-based modeling for water resources planning and management. Journal of Water Resources Planning and Management. 141(11), 04015025, https://doi.org/10.1061/(ASCE)WR.1943-5452.0000544

Bucovetchi, O., Georgescu, A., Badea, D., Stanciu, R. (2019). Agent-Based Modeling (ABM): Support for Emphasizing the Air Transport Infrastructure Dependence of Space Systems. Sustainability. 11(19), 5331-5348, https://doi.org/10.3390/su11195331

Coates, G., Li, C., Ahilan, S., Wright, N., Alharbi, M. (2019). Agent-based modeling and simulation to assess flood preparedness and recovery of manufacturing small and medium-sized enterprises. Engineering Applications of Artificial Intelligence. 78, 195-217, https://doi.org/10.1016/j.engappai.2018.11.010

Crooks, A.T. and S. Wise. (2013). GIS and agent-based models for humanitarian assistance. Computers, Environment and Urban Systems. 41, 100-111, https://doi.org/10.1016/j.compenvurbsys.2013.05.003

Dawson, R.J., R. Peppe, and M. Wang. (2011). An agent-based model for risk-based flood incident management. Natural hazards. 59(1), 167-189, https://doi.org/10.1007/s11069-011-9745-4

EPSG Geodetic Parameter Dataset Application User Guide. 2020.

Garner. (2013). R. NASA Earth Images. Available from: https://nasa.gov/content/nasa-earth-images.

Gude, V., Corns, S., Dagli, C., Long, S. (2020). Agent Based Modeling for Flood Inundation Mapping and Rerouting. Procedia Computer Science, 2020. 168, 170-176, https://doi.org/10.1016/j.procs.2020.02.279

Guzy, M.R., Smith, C., Bolte, J., Hulse, D., Gregory, S. (2008). Policy research using agent-based modeling to assess future impacts of urban expansion into farmlands and forests. Ecology and Society. 13(1), 37, http://www.ecologyandsociety.org/vol13/iss1/art37/

Hu, C., Li, J., Li, X., Chen, N., Yang, C. (2018). An observation capability semantic-associated approach to the selection of remote sensing satellite sensors: a case study of flood observations in the Jinsha River basin. Sensors. 18(5), 1649-1677, https://doi.org/10.3390/s18051649

Iranian Academy of Remote Sensing Available from: https://girs.ir/.

Iwamura, T., Lambin, E., Silvius, K.M., Luzar, J B. Fragoso, J.M.V. (2014). Agent-based modeling of hunting and subsistence agriculture on indigenous lands: Understanding interactions between social and ecological systems. Environmental Modelling & Software. 58, 109-127, https://doi.org/10.1016/j.envsoft.2014.03.008

Jia, M., Srinivasan, R.S., Ries, R., Weyer, N., Bharathy, G. (2019). A systematic development and validation approach to a novel agent-based modeling of occupant behaviors in commercial buildings. Energy and Buildings. 199, 352-367, https://doi.org/10.1016/j.enbuild.2019.07.009

Kilham, N.E., D. Roberts, and M.B. Singer. (2012). Remote sensing of suspended sediment concentration during turbid flood conditions on the Feather River, California—a modeling approach. Water Resources Research. 48(1), 1521-1538, https://doi.org/10.1029/2011WR010391

Lai, W.-Z., Li, W.-B., Huang, Y.-L., Wang, W.-X., Xiao, D. (2015). Rainstorm flood building risk dynamic assessment conceptual model utilization agent based modeling. in 2015 International Conference on Computer Science and Applications (CSA). IEEE, 191-195, https://doi.org/10.1109/CSA.2015.34

Linghu, B., Chen, F., Gue, X., Li, W. (2013). A conceptual model for flood disaster risk assessment based on agent-based modeling. in 2013 International Conference on Computer Sciences and Applications. IEEE, 369-373, https://doi.org/10.1109/CSA.2013.93

Lippe, M., Bithell, M., Gotts, N., Natalini, D., Barbrook.J, P., Giupponi, C., Hallier, M., Hofstede, G.J., Le.P, C., Matthews, R.B. (2019). Using agent-based modelling to simulate social-ecological systems across scales. GeoInformatica. 23(2), 269-298, https://doi.org/10.1007/s10707-018-00337-8

Manson, S.M., S. Sun, and D. Bonsal. (2012). Agent-based modeling and complexity, in Agent-based models of geographical systems. Springer, 125-139, https://doi.org/10.1007/978-90-481-8927-4_7

Mosavi, A., P. Ozturk, and K.-w. Chau. (2018). Flood prediction using machine learning models: Literature review. Water. 10(11), 1536, https://doi.org/10.3390/w10111536

NetLogo Gis Extension. Available from: https://ccl.northwestern.edu/netlogo/docs/gis.html.

Noji, E.K. and C. Lee. (2005). Disaster preparedness. Environmental health: From global to local, 745-780.

Quang, D.V., P. Schreinemachers, and T. Berger. (2014). Ex-ante assessment of soil conservation methods in the uplands of Vietnam: An agent-based modeling approach. Agricultural Systems. 123, 108-119, https://doi.org/10.1016/j.agsy.2013.10.002

Richards John, A. and J. Xiuping. (1999). Remote sensing digital image analysis: an introduction. Springer-Verlag, Berlin, 538-542, https://doi.org/10.1109/36.739109

Simmonds, J., J.A. Gómez, and A. Ledezma. (2020). The role of agent-based modeling and multi-agent systems in flood-based hydrological problems: a brief review. Journal of Water and Climate Change. 11(4), 1580-1602, https://doi.org/10.2166/wcc.2019.108

Spatial Reference. Available from: https://www.spatialreference.org/.

Tan, X.,Di, L., Deng, M., Chen, A., Huang, F., Peng, C., Gao, M., Yao, Y., Sha, Z. (2014). Cloud-and agent-based geospatial service chain: A case study of submerged crops analysis during flooding of the Yangtze River Basin. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 8(3), 1359-1370, https://doi.org/10.1109/JSTARS.2014.2376475

Walesh, S.G. (1991). Urban surface water management. John Wiley & Sons.

Wise, S. and A.T. Crooks. (2012). Agent-based modeling for community resource management: Acequia-based agriculture. Computers, Environment and Urban Systems. 36(6), 562-572, https://doi.org/10.1016/j.compenvurbsys.2012.08.004

Yang, L.E., Hoffmann, P., Scheffran, J., Ruhe, S., Fischereit, J., Gasser, I. (2018). An agent-based modeling framework for simulating human exposure to environmental stresses in urban areas. Urban Science. 2(2), 36-56, https://doi.org/10.3390/urbansci2020036

Zhuo, L. and D. Han. (2020). Agent-based modelling and flood risk management: a compendious literature review. Journal of Hydrology, 2020, https://doi.org/10.1016/j.jhydrol.2020.125600