نوع مقاله : مقاله پژوهشی
نویسندگان
1 استادیار گروه جغرافیای طبیعی، دانشکده علوم جغرافیایی و برنامهریزی دانشگاه اصفهان
2 کارشناسی ارشد سنجش ازدور و سیستم اطلاعات جغرافیایی دانشگاه اصفهان
چکیده
کلیدواژهها
موضوعات
عنوان مقاله [English]
نویسندگان [English]
Floods are among the most significant natural hazards, causing severe damage to lives, property, and the environment annually. The Darab watershed in Fars province, due to its specific climatic conditions, topography, and geological structure, is recognized as one of the areas at high risk of flooding. This study aims to prepare of flood risk map by using the Maximum Entropy (MaxEnt) model and analyze the factors influencing floods in the studied watershed. This is a quantitative and applied research that uses field and available data, focusing on areas with high flood risk. By utilizing data mining techniques, GIS, and remote sensing images, flood risk areas are classified. Various environmental and topographic factors were used as independent variables, including 19 environmental parameters such as precipitation, land slope, soil type, drainage density, land use, and moisture, and topographic indices and etc. These data were collected from reliable sources such as satellite imagery, Digital Elevation Models (DEM), meteorological stations, and geological maps. After data preprocessing, the models were run, and their accuracy was evaluated. Finally, the flood susceptibility map was validated using AUC, and high-risk areas were identified. Preparation of the flood risk map shows that approximately 20% of the Darab watershed is at high risk, especially in low-lying areas with poor vegetation and inappropriate land use. It was also determined that variables such as precipitation, distance from stream, and elevation have the most significant impact on flood occurrence. The results indicate that the MaxEnt model has acceptable capability in predicting high-risk areas and can play an effective role in crisis management and regional planning.
کلیدواژهها [English]
تقی آوند محمد، مرادی حمیدرضا، و رمضان زاده لسبوئی مهدی. (1399). تهیه نقشه حساسیت سیل با استفاده از مدل های یادگیری ماشین جنگل تصادفی و خطی تعمیم یافته بیزین. محیط زیست و مهندسی آب، دوره 6، شماره 1، صفحات: 95_83. 10.22034/jewe.2020.220593.1351
توکلی مهدی، امیراحمدی ابوالقاسم، گلی مختاری لیلی. (1403). ارزیابی، پیشبینی و تحلیل منطقهای سیلاب با استفاده از مدلهای دادهکاوی در حوضه آبریز فریزی. فصلنامه تحقیقات جغرافیایی. ۳۹ (۲) :۱۶۱-۱۶۸
حنیفی نیا، عبدالعزیز. عبقری، هیراد. (1404). پیش بینی مناطق مستعد سیل با استفاده از مدل های یادگیری ماشین خطی تعمیم یافته و پیشینه آنتروپی. مخاطرات محیط طبیعی، 14 (43)، 34-19. /Jneh.2024.47730.202110.22111
شریفی، فرود و غلامرضا نوروزی. (1378). تحلیلی بر سیل مرداد 78 مازندران و گلستان. نشریه جنگل و مرتع، شماره42، صص 43-40.
رحیم پور توحید، رضائی مقدم محمدحسین. (1403) مدلسازی پتانسیل خطر وقوع سیلاب در حوضه آبریز آجیچای با استفاده از الگوریتمهای دادهکاوی. پژوهش های فرسایش محیطی. ۱۴ (۴) :۱۹-۳۸
ذاکری نژاد, رضا و عیاش, کمال. (1403). ارزیابی خطر سیل و عوامل مؤثر بر آن در حوضه آبخیز زهر-جراحی در جنوب غرب ایران با استفاده از روش سلسلهمراتبی فازی. پژوهش های جغرافیای طبیعی, 56(2), 51-69. doi: 10.22059/jphgr.2024.376692.1007829
ذاکری نژاد، رضا. (1399). ارزیابی مدل های رقومی ارتفاع جهت تهیه نقشه پتانسیل فرسایش خندقی با استفاده از مدل مکسنت و سامانه اطلاعات جغرافیایی (مطالعه موردی: حوزه آبخیز سمیرم, جنوب استان اصفهان). سنجش از دور و سامانه اطلاعات جغرافیایی در منابع طبیعی (کاربرد سنجش از دور و GISدر علوم منابع طبیعی )، 11(3 (پیاپی 40) )، 21-22. SID. https://sid.ir/paper/359585/fa
میرموسوی، سید حسین و حسین اسمعیلی. (1400). پهنهبندی نواحی سیلخیز با استفاده از سامانه اطلاعات جغرافیایی (GIS) و سنجش از دور (RS)، مطالعه موردی : شهرستان داراب. مخاطرات محیط طبیعی، دوره 10، شماره 27، صص 21-46. https://doi.org/10.22111/jneh.2020.32986.1613
نوحانی، ابراهیم. دارابی،فریبا. معروفی نیا،ادریس. خسروی، خه بات. (1395(. ارزیابی مدل آنتروپی شانون در تهیه نقشه حساسیت و احتمال به وقوع سیل در حوضه آبخیز هراز. مخاطرات محیط طبیعی،سال پنجم،شماره دهم. /Jneh.2017.295810.22111
Al-Hinai, H., & Abdalla, R. (2021). Mapping coastal flood susceptible areas using Shannon’s entropy model: the case of Muscat governorate, Oman. ISPRS International Journal of Geo-Information, 10(4), 252. https://doi.org/10.3390/ijgi10040252
Ali, A., Rana, I. A., Ali, A., & Najam, F. A. (2022). Flood risk perception and communication: The role of hazard proximity. Journal of Environmental Management, 316, 115309. https://doi.org/10.1016/j.jenvman.2022.115309
Badri B, Zare R, Honarbakhsh A, Atashkhar, F. Prioritization of flood potential Beheshtabad Sub-watershed. Journal of Geographical Studies. 2016; 48(1): 143-158. https://doi.org/10.22059/JPHGR.2016.57032
Bhattarai, Y., Duwal, S., Sharma, S., & Talchabhadel, R. (2024). Leveraging machine learning and open-source spatial datasets to enhance flood susceptibility mapping in a transboundary river basin. International Journal of Digital Earth, 17(1). https://doi.org/10.1080/17538947.2024.2313857
Breugem, A. J., Wesseling, J. G., Oostindie, K., & Ritsema, C. J. (2020). Meteorological aspects of heavy precipitation in relation to floods – An overview. Earth-Science Reviews, 103171. https://doi.org/10.1016/j.earscirev.2020.10317
Chen, Y., Wang, B., Pollino, C. A., Cuddy, S. M., Merrin, L. E., & Huang, C. (2014). Estimate of flood inundation and retention on wetlands using remote sensing and GIS. Ecohydrology, n/a–n/a. https://doi.org/10.1002/eco.1467
Dovonce E. A. (2000). Physically based distributed hydrologic model. Master of Science Thesis, The Pennsylvania State University.
Duwal, S., Liu, D., & Pradhan, P. M. (2023). Flood susceptibility modeling of the Karnali river basin of Nepal using different machine learning approaches. Geomatics, Natural Hazards and Risk, 14(1). https://doi.org/10.1080/19475705.2023.2217321
El-Haddad, B. A., Youssef, A. M., Pourghasemi, H. R., Pradhan, B., El-Shater, A.-H., & El-Khashab, M. H. (2020). Flood susceptibility prediction using four machine learning techniques and comparison of their performance at Wadi Qena Basin, Egypt. Natural Hazards. https://doi.org/10.1007/s11069-020-04296-y
Gokceoglu, C., Sonmez, H., Nefeslioglu, H.A., Duman, T.Y., Can, T., 2005. The 17 March 2005 Kuzulu landslide (Sivas, Turkey) and landslide-susceptibility map of its vicinity. Eng. Geol. 81, 65–83. https://doi.org/10.1016/j.enggeo.2005.07.011
Harshasimha, A.C.; Bhatt, C.M. Flood Vulnerability Mapping Using MaxEnt Machine Learning and
Analytical Hierarchy Process (AHP) of Kamrup Metropolitan District, Assam. Environ. Sci. Proc. 2023, 25, 73. https://doi.org/10.3390/ECWS-7-14301
Hitouri S, Mohajane M, Lahsaini M, Ali SA, Setargie TA, Tripathi G, D’Antonio P, Singh SK, Varasano A. (2024). Flood Susceptibility Mapping Using SAR Data and Machine Learning Algorithms in a Small Watershed in Northwestern Morocco. Remote Sensing, 16(5):858. https://doi.org/10.3390/rs16050858
Huang, Chang & Wu, Jianping & Chen, Yun & Yu, Jia. (2012). Detecting floodplain inundation frequency using MODIS time-series imagery. 2012 1st International Conference on Agro-Geoinformatics, Agro-Geoinformatics 2012. 1-6. https://doi.org/10.1109/Agro-Geoinformatics.2012.6311668
Huang, C., Chen, Y., Wu, J. (2013). GIS-based spatial zoning for flood inundation modelling in the Murray-Darling Basin. 20th International Congress on Modelling and Simulation 2013 (MODSIM2013).
Huang, C., Chen, Y., & Wu, J. (2014). Mapping spatio-temporal flood inundation dynamics at large river basin scale using time-series flow data and MODIS imagery. International Journal of Applied Earth Observation and Geoinformation, 26, 350–362. https://doi.org/10.1016/j.jag.2013.09.002
Huang, C., Chen, Y., Wu, J., Chen, Z., Li, L., Liu, R., Yu, J., 2014c. Integration of remotely sensed inundation extent and high-precision topographic data for mapping inundation depth. In: 3rd International Conference on Agro-Geoinformatics, Beijing, China.
Javidan, N., Kavian, A., Pourghasemi, H.R. et al. (2021). Evaluation of the multi-hazard map produced using the MaxEnt machine learning technique. Sci Rep 11, 6496. https://doi.org/10.1038/s41598-021-85862-7
L., Achu & C D, Aju & MANI CHRISTY, Raicy & Bhadran, Arun & George, Amal & Udayar Pillai, Surendran & Girishbai, Drishya & Ajayakumar, P. & Gopinath, Girish & Pradhan, Biswajeet. (2025). Improved flood risk assessment using multi-model ensemble machine-learning techniques in a tropical river basin of Southern India. Physical Geography. 46. 1-29. https://doi.org/ 10.1080/02723646.2025.2464536.
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. https://doi.org/10.1080/01431169608948714
Menuka M., Sachin T., Santosh A., Bikram S., Bikram M., Amir S. (2024). Flood susceptibility assessment using a machine learning approach in the Mohana-Khutiya River of Nepal, Natural Hazards Research, 4(1): 32-45. doi.org/10.1016/j.nhres.2024.01.001.
Pandey, M., Arora, A., Arabameri, A.R., Costache, R.D., Kumar, Mishra, V., Nguyen, H., Mishra, J., Siddiqui, M., Ray, Y., Soni, S., Shukla, U. (2021). Flood Susceptibility Modeling in a Subtropical Humid Low-Relief https://doi.org/10.3389/feart.2021.659296
Prakash Mohanty, M., Nithya, S., Nair, A. S., Indu, J., Ghosh, S., Mohan Bhatt, C., … Karmakar, S. (2020). Sensitivity of various topographic data in flood management: Implications on inundation mapping over large data-scarce regions. Journal of Hydrology, 125523. https://doi.org/10.1016/j.jhydrol.2020.125523
Qasimi Abdul Baser, Isazade Vahid, Berndtsson Ronny. (2023). Flood Susceptibility Prediction Using MaxEnt and Frequency Ratio Modeling for Kokcha River in Afghanistan. Natural Hazards, 25 October 2023. https://doi.org/10.1007/s11069-023-06232-2
Qasimi, A.B., Isazade, V. & Berndtsson, R. (2024). Flood susceptibility prediction using MaxEnt and frequency ratio modeling for Kokcha River in Afghanistan. Nat Hazards 120, 1367–1394. https://doi.org/10.1007/s11069-023-06232-2.
Rahman, M., Ningsheng, C., Islam, M.M. et al. Flood Susceptibility Assessment in Bangladesh Using Machine Learning and Multi-criteria Decision Analysis. Earth Syst Environ 3, 585–601 (2019). https://doi.org/10.1007/s41748-019-00123-y
Razavi Termeh S. V., Kornejady A., Pourghasemi H. R., and Keesstra S.(2018). Flood susceptibility mapping using novel ensembles of adaptive neuro-fuzzy inference systems and metaheuristic algorithms. Sci Total Environ., 615, 438–451. https://doi.org/10.1016/j.scitotenv.2017.09.262
Sala, O.E., Chapin, F.S., Armesto, J.J., et al. (2000) Global Biodiversity Scenarios for the Year 2100. Science, 287, 1770-1774. http://dx.doi.org/10.1126/science.287.5459.1770
Shirani, K., Zakerinejad, R. (2021). Watershed prioritization for the identification of spatial hotspots of flood risk using the combined TOPSIS-GIS-based approach: a case study of the Jarahi-Zohre catchment in Southwest Iran. AUC Geographica 56(1), 120–128.
Shahabi H, Khezri S, Ahmad BB, Hashim M. (2014). Landslide susceptibility mapping at central Zab basin, Iran: A comparison between analytical hierarchy process, frequency ratio, and logistic regression models, Catena 115: 55-70. http://dx.doi.org/10.1016/j.catena.2013.11.014
Shrestha, R.M., Di, L., Yu, G., Shao, Y., Kang, L., & Zhang, B. (2013). Detection of flood and its impact on crops using NDVI Corn case. 2013 Second International Conference on Agro-Geoinformatics (Agro-Geoinformatics), 200-204. https://doi.org/10.1109/ARGO-GEOINFORMATICS.2013.6621907
Smith, K. (2001). Environmental hazard assessing risk and reducing disaster, Third edition, published by Routledge, 11 New Fetter Lane, London.
Sugianto S, Deli A, Miswar E, Rusdi M, Irham M. The effect of land use and land cover changes on flood occurrence in Teunom Watershed, Aceh Jaya. Land. 2022 Aug 8;11(8):1271. https://doi.org/10.3390/land11081271
Taherizadeh, M., Niknam, A., Nguyen-Huy, T. et al. Flash flood-risk areas zoning using integration of decision-making trial and evaluation laboratory, GIS-based analytic network process, and satellite-derived information. Nat Hazards 118, 2309–2335 (2023). https://doi.org/10.1007/s11069-023-06089-5
Tao, H., Al-Khafaji, Z. S., Qi, C., Zounemat-Kermani, M., Kisi, O., Tiyasha, T., … & Yaseen, Z. M. (2021). Artificial intelligence models for suspended river sediment prediction: state-of-the-art, modeling framework appraisal, and proposed future research directions. Engineering Applications of Computational Fluid Mechanics, 15(1), 1585-1612. https://doi.org/10.1080/19942060.2021.1984992
Thomas, David S.G. (2016). The Dictionary of Physical Geography, 4th Edition. John Wiley & Sons Ltd.https://doi.org/10.1002/9781118782323.ch06
Ticehurst, C., Chen, Y., Karim, F., Dushmanta, D., 2013. Using MODIS for mapping flood events for use in hydrological and hydrodynamic models: experiences so far. In: 20th International Congress on Modelling and Simulation, Adelaide, Australia.
Xu, H. (2006). "Modification of normalized difference water index (NDWI) to enhance open water features in remotely sensed imagery." International Journal of Remote Sensing, 27(14), 3025-3033. https://doi.org/10.1080/01431160600589179
Zakerinejad, R., Ayash, K. (2024). Analysis of Flood Risk and Influencing Factors in Zohr-Jarhari Basin in Zohr-Jarhari in Southwest of Iran using Fuzzy Analytic Hierarchy Process (FAHP) Approach. Physical Geography Research Quarterly, 56 (2), 51-69. http://doi.org/10.22059/JPHGR.2024.376692.1007829.
)