Flood Risk Spatial Modeling Based on Geographical Information Systems and Remote Sensing in the Pemangkat Regensi

Authors

Ajun Purwanto , Paiman

DOI:

10.29303/jppipa.v9i11.5264

Published:

2023-11-25

Issue:

Vol. 9 No. 11 (2023): November

Keywords:

Flood risk, GIS, Remote sensing, Spatial modeling

Research Articles

Downloads

How to Cite

Purwanto, A. ., & Paiman, P. (2023). Flood Risk Spatial Modeling Based on Geographical Information Systems and Remote Sensing in the Pemangkat Regensi . Jurnal Penelitian Pendidikan IPA, 9(11), 9554–9563. https://doi.org/10.29303/jppipa.v9i11.5264
Crossref
Scopus
Google Scholar
Europe PMC

Downloads

Download data is not yet available.

Metrics

Metrics Loading ...

Abstract

Flood is a disaster that occurs every year in Indonesia with various risks. This study aims to create a spatial model of flood risk and determine the distribution of flood risk in Pemangkat, Sambas Regency. The method used is surveying and interpreting secondary data from the Digital Elevation Model, topographic maps, and land cover images. The data collected includes area, elevation, slope, distance from the river, land use, and rainfall. The tool used is a set of Geographic Information System tools, namely Arcgis 10.8. Data analysis using Weighted Sum for generated Flood risk map and Geographically Weighted Regression for flood risk spatial modeling. The results showed that the Pemangkat sub-district had flood risk classes, namely very low, low, moderate, high, and very high classes. Very high to high flood-risk classes are spread in the cities of Pemangkat and Sabatuan. In contrast, medium to deficient classes are spread in Jelutung, Gugah Sejahtera, Penjajap, Harapan, Lonam, and Parapakan. Very low flood risk area is 8.17 ha (8.16%), low 16.97 ha (16.97%, medium 28.17 ha (28.16%), high 32.28 ha (32.28%) and very high 14.41ha (14.39%). The values obtained from the analysis show that GWR modeling is excellent because R2 is relatively tiny, 0.39.

References

Agus, F. (2006). Judicious use of land resources for sustaining Indonesian rice self-sufficiency. In International Rice Conference, 12-14 September, Denpasar, Bali, Indonesia, 2005. Retrieved from https://cir.nii.ac.jp/crid/1573668924794173696

Althuwaynee, O. F., Pradhan, B., & Lee, S. (2012). Computers & Geosciences Application of an evidential belief function model in landslide susceptibility mapping. Computers and Geosciences, 44, 120–135. https://doi.org/10.1016/j.cageo.2012.03.003

Arrighi, C., Pregnolato, M., Dawson, R. J., & Castelli, F. (2019). Preparedness against mobility disruption by floods. Science of the Total Environment, 654, 1010–1022. https://doi.org/10.1016/j.scitotenv.2018.11.191

Bhatt, C. M., Rao, G. S., Diwakar, P. G., & Dadhwal, V. K. (2017). Development of flood inundation extent libraries over a range of potential flood levels: a practical framework for quick flood response. Geomatics, Natural Hazards and Risk, 8(2), 384–401. https://doi.org/10.1080/19475705.2016.1220025

Bhola, P. K., Leandro, J., & Disse, M. (2018). Framework for offline flood inundation forecasts for two-dimensional hydrodynamic models. Geosciences, 8(9), 346. https://doi.org/10.3390/geosciences8090346

Brunsdon, C., Fotheringham, A. S., & Charlton, M. E. (1996). Geographically weighted regression: a method for exploring spatial nonstationarity. Geographical Analysis, 28(4), 281–298. https://doi.org/10.1111/j.1538-4632.1996.tb00936.x

Bui, D. T., Khosravi, K., Shahabi, H., & Daggupati, P. (2019). Flood spatial modeling in northern Iran using remote sensing and gis: A comparison between evidential belief functions and its ensemble with a multivariate logistic regression model. Remote Sensing, 11(13), 1589. https://doi.org/10.3390/rs11131589

Cheng, Y., Sang, Y., Wang, Z., & Guo, Y. (2021). Effects of Rainfall and Underlying Surface on Flood Recession — The Upper Huaihe River Basin Case. International Journal of Disaster Risk Science, 12(1), 111–120. https://doi.org/10.1007/s13753-020-00310-w

Choubin, B., Moradi, E., Golshan, M., Adamowski, J., Sajedi-Hosseini, F., & Mosavi, A. (2019). An ensemble prediction of flood susceptibility using multivariate discriminant analysis, classification and regression trees, and support vector machines. Science of the Total Environment, 651, 2087–2096. https://doi.org/10.1016/j.scitotenv.2018.10.064

Coumou, D., & Rahmstorf, S. (2012). A decade of weather extremes. Nature Climate Change, 2(7), 491–496. https://doi.org/10.1038/nclimate1452

Danandeh Mehr, A., & Akdegirmen, O. (2021). Estimation of Urban Imperviousness and its Impacts on Flashfloods in Gazipaşa, Turkey. Knowledge-Based Engineering and Sciences, 2(1 SE-Articles), 9–17. https://doi.org/10.51526/kbes.2021.2.1.9-17

Das, P., Behera, M. D., Patidar, N., Sahoo, B., Tripathi, P., Behera, P. R., Srivastava, S. K., Roy, P. S., Thakur, P., Agrawal, S. P., & Krishnamurthy, Y. V. N. (2018). Impact of LULC change on the runoff, base flow and evapotranspiration dynamics in eastern Indian river basins during 1985–2005 using variable infiltration capacity approach. Journal of Earth System Science, 127(2), 19. https://doi.org/10.1007/s12040-018-0921-8

Demirkesen, A. C., Evrendilek, F., Berberoglu, S., & Kilic, S. (2007). Coastal Flood Risk Analysis Using Landsat-7 ETM+ Imagery and SRTM DEM: A Case Study of Izmir, Turkey. Environmental Monitoring and Assessment, 131(1), 293–300. https://doi.org/10.1007/s10661-006-9476-2

Dettinger, M. (2011). Climate change, atmospheric rivers, and floods in California–a multimodel analysis of storm frequency and magnitude changes 1. JAWRA Journal of the American Water Resources Association, 47(3), 514–523. https://doi.org/10.1111/j.1752-1688.2011.00546.x

Di Baldassarre, G., & Uhlenbrook, S. (2012). Is the current flood of data enough? A treatise on research needs for the improvement of flood modelling. Hydrological Processes, 26(1), 153–158. https://doi.org/10.1002/hyp.8226

Gianotti, A. G. S., Warner, B., & Milman, A. (2018). Flood concerns and impacts on rural landowners: An empirical study of the Deerfield watershed, MA (USA). Environmental Science & Policy, 79, 94–102. https://doi.org/10.1016/j.envsci.2017.10.007

Grayson, R. B., & Ladson, A. R. (1991). Digital terrain modelling: a review of hydrological, geomorphological, and biological applications. Hydrological processes, 5(1), 3-30. https://doi.org/10.1002/hyp.3360050103

Hlodversdottir, A. O., Bjornsson, B., Andradottir, H. O., Eliasson, J., & Crochet, P. (2015). Assessment of flood hazard in a combined sewer system in Reykjavik city centre. Water Science and Technology, 71(10), 1471–1477. https://doi.org/10.2166/wst.2015.119

Hu, A., & Demir, I. (2021). Real-time flood mapping on client-side web systems using hand model. Hydrology. https://doi.org/10.3390/hydrology8020065

Isazade, V., Baser, A., Pinliang, Q., Gordana, D., & Esmail, K. (2023). Integration of Moran ’ s I , geographically weighted regression (GWR), and ordinary least square (OLS) models in spatiotemporal modeling of COVID ‑ 19 outbreak in Qom and Mazandaran Provinces , Iran. Modeling Earth Systems and Environment, 0123456789. https://doi.org/10.1007/s40808-023-01729-y

Jaya, A. (2022). The Effect of Land Use and Land Cover Changes on Flood Occurrence in Teunom Watershed, Aceh Jaya.

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, 251-264. https://doi.org/10.1007/s12665-011-1504-z

Kim, M.-K., & Graefe, D. (2021). Geographically weighted regression to explore spatially varying relationships of recreation resource impacts: a case study from Adirondack Park, New York, USA. Journal of Park and Recreation Administration, 39(2). https://doi.org/10.18666/JPRA-2020-10515

Kresch, D. ., Mastin, O. ., & Olsen, T. (2002). Fifty-Year Flood-Inundation Maps for Olanchito, Honduras. Tacoma, Washington, USA, US Geological Survey. https://doi.org/10.3133/ofr02257

Kuo, C.-C., Wardrop, N., Chang, C.-T., Wang, H.-C., & Atkinson, P. M. (2017). Significance of major international seaports in the distribution of murine typhus in Taiwan. PLoS Neglected Tropical Diseases, 11(3), e0005430. https://doi.org/10.1371/journal.pntd.0005430

Lamichhane, N., & Sharma, S. (2017). Development of flood warning system and flood inundation mapping using field survey and LiDAR data for the Grand River near the city of Painesville, Ohio. Hydrology. https://doi.org/10.3390/hydrology4020024

Li, X., Zhang, Y., Ma, N., Li, C., & Luan, J. (2021). Contrasting effects of climate and LULC change on blue water resources at varying temporal and spatial scales. Science of The Total Environment, 786, 147488. https://doi.org/10.1016/j.scitotenv.2021.147488

Li, Z., Mount, J., & Demir, I. (2020a). Evaluation of model parameters of HAND model for real-time flood inundation mapping: iowa case study. eartharxiv.org. Retrieved from https://eartharxiv.org/repository/object/127/download/262/

Li, Z., Mount, J., & Demir, I. (2020b). Model Parameter Evaluation and Improvement for Real-Time Flood Inundation Mapping Using HAND Model: Iowa Case Study. eartharxiv.org. https://doi.org/10.31223/OSF.IO/HQPZG

Marfai, M. ., Mardiatno, J., Cahyadi, A., Nucifera, F., & Prihatno, H. (2017). Pemodelan Spasial Bahaya Banjir Rob Berdasarkan Skenario. Bumi Lestari, 13(2). https://doi.org/10.31227/osf.io/wzter

McGrath, H., Bourgon, J. F., Proulx-Bourque, J. S., Nastev, M., & Abo El Ezz, A. (2018). A comparison of simplified conceptual models for rapid web-based flood inundation mapping. Natural Hazards, 93, 905-920. https://doi.org/10.1007/s11069-018-3331-y

Meraj, G., Romshoo, S. A., Yousuf, A. R., Altaf, S., & Altaf, F. (2015). Assessing the influence of watershed characteristics on the flood vulnerability of Jhelum basin in Kashmir Himalaya. Natural Hazards, 77, 153–175. https://doi.org/10.1007/s11069-015-1605-1

Mojaddadi, H., Pradhan, B., Nampak, H., Ahmad, N., & Ghazali, A. H. B. (2017). Ensemble machine-learning-based geospatial approach for flood risk assessment using multi-sensor remote-sensing data and GIS. Geomatics, Natural Hazards and Risk, 8(2), 1080-1102. https://doi.org/10.1080/19475705.2017.1294113

Morris, J., Beedell, J., & Hess, T. M. (2016). Mobilising flood risk management services from rural land: principles and practice. Journal of Flood Risk Management, 9(1), 50–68. https://doi.org/10.1111/jfr3.12110

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

Mumbai, G. (2020). frequency ratio and fuzzy gamma operator models in GIS : a case study of Urban flood susceptibility zonation mapping using evidential belief function , frequency ratio and fuzzy gamma operator models in GIS : a case study of Greater Mumbai, Maharashtra, India Veerappan Ramesh & Sayed Sumaira Iqbal. Geocarto International, 0(0), 1–26. https://doi.org/10.1080/10106049.2020.1730448

Nahib, I., Ambarwulan, W., Rahadiati, A., Munajati, S. L., Prihanto, Y., Suryanta, J., Turmudi, T., & Nuswantoro, A. C. (2021). Assessment of the impacts of climate and LULC changes on the water yield in the Citarum River Basin, West Java Province, Indonesia. Sustainability, 13(7), 3919. https://doi.org/10.3390/su13073919

Pourali, S. H., Arrowsmith, C., Chrisman, N., Matkan, A. A., & Mitchell, D. (2016). Topography wetness index application in flood-risk-based land use planning. Applied Spatial Analysis and Policy, 9, 39–54. https://doi.org/10.1007/s12061-014-9130-2

PPID. (2023). Bupati Sambas Satono Tinjau Bendungan yang Meluap di Kecamatan Pemangkat. Retrieved from https://ppid.sambas.go.id/berita/28/

Purwanto, A., Rustam, R., Andrasmoro, D., & Eviliyanto, E. (2022). Flood Risk Mapping Using GIS and Multi-Criteria Analysis at Nanga Pinoh West Kalimantan Area. Indonesian Journal of Geography, 54(3). https://doi.org/10.22146/ijg.69879

Riadi, B., & Gaol, Y. A. L. (2018). Spatial Modeling of Flood Risk in Karawang. Advances in Science, Technology, and Engineering System Journal, 3(5), 200-206. https://doi.org/10.25046/aj030524

Rizeei, H. M., Saharkhiz, M. A., Pradhan, B., & Ahmad, N. (2016). Soil erosion prediction based on land cover dynamics at the Semenyih watershed in Malaysia using LTM and USLE models. Geocarto International, 31(10), 1158–1177. https://doi.org/10.1080/10106049.2015.1120354

Ronchail, J., Espinoza, J. C., Drapeau, G., Sabot, M., Cochonneau, G., & Schor, T. (2018). The flood recession period in Western Amazonia and its variability during the 1985–2015 period. Journal of Hydrology: Regional Studies, 15, 16–30. ttps://doi.org/10.1016/j.ejrh.2017.11.008

Rosser, J. F., Leibovici, D. G., & Jackson, M. J. (2017). Rapid flood inundation mapping using social media, remote sensing and topographic data. Natural Hazards, 87, 103-120. https://doi.org/10.1007/s11069-017-2755-0

Sahoo, S., Dhar, A., Debsarkar, A., & Kar, A. (2018). Impact of water demand on hydrological regime under climate and LULC change scenarios. Environmental Earth Sciences, 77(9), 341. https://doi.org/10.1007/s12665-018-7531-2

Sayama, T., Tatebe, Y., Iwami, Y., & Tanaka, S. (2015). Hydrologic sensitivity of flood runoff and inundation: 2011 Thailand floods in the Chao Phraya River basin. Natural Hazards and Earth System Sciences, 15(7), 1617–1630. https://doi.org/10.5194/nhess-15-1617-2015

Seo, B.-C., Keem, M., Hammond, R., Demir, I., & Krajewski, W. F. (2019). A pilot infrastructure for searching rainfall metadata and generating rainfall product using the big data of NEXRAD. Environmental Modelling & Software, 117, 69–75. https://doi.org/10.1016/j.envsoft.2019.03.008

Sermet, Y., & Demir, I. (2019). Towards an information centric flood ontology for information management and communication. Earth Science Informatics, 12(4), 541–551. https://doi.org/10.1007/s12145-019-00398-9

Sermet, Y., Demir, I., & Muste, M. (2020). A serious gaming framework for decision support on hydrological hazards. Science of The Total Environment, 728, 138895. https://doi.org/10.1016/j.scitotenv.2020.138895

Sermet, Y., Villanueva, P., Sit, M. A., & Demir, I. (2020). Crowdsourced approaches for stage measurements at ungauged locations using smartphones. Hydrological Sciences Journal, 65(5), 813–822. https://doi.org/10.1080/02626667.2019.1659508

Shafapour, M., Lee, T. M., & Lee, S. (2014). Flood susceptibility mapping using integrated bivariate and multivariate statistical models. Environmental earth sciences, 72, 4001-4015. https://doi.org/10.1007/s12665-014-3289-3

Singh, Y. K., Dutta, U., Prabhu, T. S. M., Prabu, I., Mhatre, J., Khare, M., Srivastava, S., & Dutta, S. (2017). Flood response system—A case study. Hydrology, 4(2), 30. https://doi.org/10.3390/hydrology4020030

Tadesse, Y. B., & Fröhle, P. (2020). Modelling of flood inundation due to levee breaches: sensitivity of flood inundation against breach process parameters. Water, 12(12), 3566. https://doi.org/10.3390/w12123566

Tehrany, M. S., & Kumar, L. (2018). The application of a Dempster–Shafer-based evidential belief function in flood susceptibility mapping and comparison with frequency ratio and logistic regression methods. Environmental Earth Sciences, 77, 1–24. https://doi.org/10.1007/s12665-018-7667-0

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. https://doi.org/10.1016/j.envsoft.2017.01.006

Tewari, A., Kshemkalyani, V., Kukreja, H., Menon, P., & Thomas, R. (2021). Application of LSTMs and HAND in rapid flood inundation mapping. In 2021 5th International Conference on Intelligent Computing and Control Systems (ICICCS) (pp. 515-521). IEEE. https://doi.org/10.1109/ICICCS51141.2021.9432332

Tunas, I. G., Azikin, H., & Oka, G. M. (2021, November). Impact of Extreme Rainfall on Flood Hydrographs. In IOP Conference Series: Earth and Environmental Science (Vol. 884, No. 1, p. 012018). IOP Publishing. https://doi.org/10.1088/1755-1315/884/1/012018

Xiang, Z., & Demir, I. (2020). Distributed long-term hourly streamflow predictions using deep learning–A case study for State of Iowa. Environmental Modelling & Software, 131, 104761. https://doi.org/10.1016/j.envsoft.2020.104761

Xu, H., Windsor, M., Muste, M., & Demir, I. (2020). A web-based decision support system for collaborative mitigation of multiple water-related hazards using serious gaming. Journal of Environmental Management, 255, 109887. https://doi.org/10.1016/j.jenvman.2019.109887

Yildirim, E., & Demir, I. (2021). An integrated flood risk assessment and mitigation framework: A case study for middle Cedar River Basin, Iowa, US. International Journal of Disaster Risk Reduction, 56, 102113. https://doi.org/10.1016/j.ijdrr.2021.102113

Zhou, Q., Leng, G., Su, J., & Ren, Y. (2019). Comparison of urbanization and climate change impacts on urban flood volumes: Importance of urban planning and drainage adaptation. Science of the Total Environment, 658, 24–33. https://doi.org/10.1016/j.scitotenv.2018.12.184

Author Biographies

Ajun Purwanto, IKIP PGRI Pontianak

Paiman, IKIP PGRI Pontianak

License

Copyright (c) 2023 Ajun Purwanto Purwanto

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Authors who publish with Jurnal Penelitian Pendidikan IPA, agree to the following terms:

  1. Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution 4.0 International License (CC-BY License). This license allows authors to use all articles, data sets, graphics, and appendices in data mining applications, search engines, web sites, blogs, and other platforms by providing an appropriate reference. The journal allows the author(s) to hold the copyright without restrictions and will retain publishing rights without restrictions.
  2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in Jurnal Penelitian Pendidikan IPA.
  3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).