Darab Watershed,  Flood, Data Mining, Maximum Entropy.

Zakerinejad, Reza; Ostad Hosseini, Zahra; Ghobishawi, Yasmin

doi:10.22111/jneh.2025.52982.2136

Darab Watershed, Flood, Data Mining, Maximum Entropy.

Articles in Press

Document Type : Research Article

Authors

¹ Assistant Professor, Faculty of Geographical Sciences and Planning, University of Isfahan, Isfahan, Iran

² Master of Remote Sensing and Geographic Information Systems, University of Isfahan, Iran

10.22111/jneh.2025.52982.2136

Abstract

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.

Keywords

Main Subjects

Environmental Hazards

References

References [in Persian]

Avand, M. T., Moradi, H. R., and Ramazanzadeh, M. (2020). Flood Susceptibility Mapping Using Random Forest Machine Learning and Generalized Bayesian Linear Model. Environment and Water Engineering, 6(1), 83-95. doi: 10.22034/jewe.2020.220593.1351. [in Persian]

Hanifinia, A. and Abghari, H. (2025). Predicting flood-prone areas using generalized linear and maximum entropy machine learning models. Journal of Natural Environmental Hazards, 14(43), 19-34. doi: 10.22111/jneh.2024.47730.2021. [in Persian]

Mir Mosavi, S. H., and Esmaeili, H. (2021). Zoning of Flood-prone Areas Using Geographic Information System (GIS) and Remote Sensing (RS), (Case Study: Darab City). Journal of Natural Environmental Hazards, 10(27), 21-46. doi: 10.22111/jneh.2020.32986.1613. [in Persian]

Nohani, E., darabi, F., maroofinia, E., and khosravi, K. (2016). Evaluation of Shannon entropy for flood probability and susceptibility mapping at the Haraz catchment. Journal of Natural Environmental Hazards, 5(10), 99-116. doi: 10.22111/jneh.2017.2958. [in Persian]

Rahimpour T, Rezaei Moghaddam M H. (2025). Modeling the Flood Hazard Potential in the Aji Chai Basin Using Data Mining Algorithms. E.E.R. 14 (4):19-38. http://magazine.hormozgan.ac.ir/article-1-862-en.html. [in Persian]

Sharifi, F., & Nowrouzi, G. (1999). An analysis of the August 1999 flood in Mazandaran and Golestan provinces. Journal of Forest and Rangeland, No. 42, pp. 40–43. [in Persian]

Tavakkoli M, Amirahmadi A, Goli Mokhtari L. (2024). Evaluation, Prediction, and Regional Analysis of Floods Using Data Mining Models (Frizi Watershed). GeoRes; 39 (2):161-168. http://georesearch.ir/article-1-1585-en.html. [in Persian]

Zakerinejad, R. “Evaluation of DEMs for the modeling of the potential of gully erosion using Maxent model (Case study: Semirom catchment in the south of Isfahan Province, Iran).” Journal of RS and GIS for Natural Resources, vol. 11, no. 3, 2020, pp. 106-122. 10.30495/girs.2020.674955. [in Persian]

Zakerinejad, R. and 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, 56(2), 51-69. doi: 10.22059/jphgr.2024.376692.1007829. [in Persian]

References [in English]
1. L. AchuORCID Icon,C. D AjuORCID Icon, M. C. Raicy, Arun BhadranORCID Icon, Amal George, U. Surendran. (2025). Improved flood risk assessment using multi-model ensemble machine-learning techniques in a tropical river basin of Southern India, https://doi.org/10.1080/02723646.2025.
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, AR., Costache, RD., 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.