Identifying flood-prone areas in the ‎Yamchi ‎watershed ‎by monitoring spectral indices and ‎satellite ‎data

Document Type : کاربردی

Authors

1 Professor of Climatology, Department of physical Geography, Faculty of Social Sciences, University of Mohaghegh Ardabili

2 Ph.D. Student of Climatology, Department of Physical Geography, Faculty of Social Sciences, University of Mohaghegh Ardabili, Ardabil, Iran‎

Abstract

In this study, first, daily discharge data of Lai, Nir, and Yamchi stations were collected from the Regional Water Company of Ardabil Province during the years 2014 to 2021. Then, effective environmental parameters including land slope, distance from the waterway, land use, and soil moisture were plotted in ArcGIS and Google Earth Engine. Also, spectral indices NDWI, AWEI, WRI, and LSWI were extracted from Landsat 8 images. After standardizing the variables, a regression random forest model was trained with 100 decision trees. 70% of the data was used for training and 30% for testing the model. The performance of the model was evaluated with statistical indices R² and MSE equal to 0.9353 and 0.000210, respectively. Finally, flood-prone areas were identified. For validation purposes, the April 2017 flood event was analyzed as a case study. The results showed that the northern part of the basin has the highest potential for flooding due to its high elevation, steep slope, 35% soil moisture, and high AWEI and WRI values. In contrast, the central and southern areas showed a lower probability of flooding due to their gentler slope and lower soil moisture. The random forest model confirmed the accuracy of this model, and its performance was assessed as acceptable with an ROC curve and an AUC value of 0.616. Radar data analysis also showed that the signal reflectance in the northern areas changed significantly before and after the flood, indicating the concentration of water resources in this part of the basin.

Keywords

Main Subjects

References

Abdolazimi, H., Roshun, S. H., Shamsnia, S. A., & Shahinifar, H. (2021). Identification of potential areas to flood inundation in Shiraz city using TOPSIS-GIS. Hydrogeomorphology, 7(25), 159-139. https://doi.org/10.22034/hyd.2021.43413.1565(in persian).
Adnan, M. S. G., Dewan, A., Zannat, K. E., & Abdullah, A. Y. M. (2019). The use of watershed geomorphic data in flash flood susceptibility zoning: A case study of the Karnaphuli and Sangu river basins of Bangladesh. Natural Hazards, 99(2), 425–448. https://doi.org/10.1007/s11069-019-03749-3.
Ahmadzadeh, H., Saeedabadi, R., & Noori, E. (2015). A study and zoning of the areas prone to flooding with an emphasis on urban floods (Case study: City of Maku). Hydrogeomorphology, 2(2), 1-24. https://dor.isc.ac/dor/20.1001.1.23833254.1394.2.2.1.0(in persian).
Asghari Saraskanroud, S., Khonkham, S., & Abdi, O. (2024). Evaluation of water indicators using Landsat and Sentinel satellite images (Case area: Zaribar Lake). Remote Sensing and GIS Applications in Environmental Sciences, 4(12), 115–95. https://doi.org/10.22034/rsgi.2024.64194.1111‎(in persian).‎
Asghari, S., Jalilyan, R., Pirozineghad, N., Madadi, A., & Yadeghari, M. (2020). Evaluation of water extraction indices using Landsat satellite images (Case study: Gamasiab River of Kermanshah). Journal of Geographical Studies, 20(58), 53-70. https://doi.org/10.29252/jgs.20.58.53(in persian).
Azadtalab, M., Shahabi, H., Shirzadi, A., & Chapi, K. (2020). Flood hazard mapping in Sanandaj using combined models of statistical index and evidential belief function. Motaleate Shahri, 9(36), 27-40. https://doi.org/10.34785/J011.2021.801(in persian).
Chezgi, J., & Poyan, S. (2024). Determining flood-prone areas using machine learning models in the Shahrestank Watershed Area of Khosef City. Journal of Water Management and Soil Erosion in Iran, 17(63), 4. Retrieved from http://jwmsei.ir/article-1-1127-fa.html(in persian).
Dasallas, L., Kim, Y., & An, H. (2019). Case study of HEC-RAS 1D–2D coupling simulation: 2002 Baeksan flood event in Korea. Water, 11(10), 2048. https://doi.org/10.3390/w11102048.
Faraji, M., & Fatemi, B. (2022). Comparative analysis of remote sensing water indexes for wetland water body monitoring using Landsat images and the Google Earth Engine platform (Case study: Meighan Wetland, Iran). Journal of Geomatics Science and Technology, 10(2), 39–62. http://jgit.kntu.ac.ir/article-1-871-fa.html ‎(in persian).‎
Feyzolahpour, M. (2024). Investigating the flood-affected areas of Khuzestan in the period from 7 March 2019 to 24 April 2019 using NDVI, NDBaI, and NDWI indices and analyzing the degradation process of Hourol Azim wetland from 2000 to 2023 using Random Forest Model (RTC). Geography and Human Relationships, 6(4), 423–449. https://doi.org/10.22034/gahr.2023.413223.1931(in persian).
Hemmati, M., Shahnazi, M., Ahmadi, H., & Salarjazi, M. (2017). Flood peak flow simulation and determination of flood source area in the Qaranqu watershed using hydrological Mod-Clark model and GIS. Irrigation and Water Engineering, 7(4), 65-80. Retrieved from https://www.waterjournal.ir/article_74156.html?lang=en(in persian).
‎Jahangir, M. H., Mousavi Reineh, S. M., & Abolghasemi, M. (2019). Spatial prediction of flood zonation mapping in Kan River Basin, Iran, using artificial neural network algorithm. Weather and Climate Extremes, 25, 100215. https://doi.org/10.1016/j.wace.2019.100215.
Jalaliyan, S. I. (2022). Evaluating and zoning flooding on a temporal and spatial scale (Study Area: Gorgan River Watershed in Golestan Province). Geographical Planning of Space, 11(42), 143-162. https://doi.org/10.30488/gps.2020.213834.3157(in persian).
Javadi, F., Rezayan, S., & Jozi, S. A. (2020). Evaluating satellite indicators in determining the level of aquatic areas using satellite sensors (Case study: Zaribar Wetland, Kurdistan Province). Journal of Ecohydrology, 7(2), 539-550. https://doi.org/10.22059/ije.2020.295355.1267(in persian).
Kieu, Q. L. (2021). Flash flood hazard mapping using satellite images and GIS integration method: A case study of Lai Chau province, Vietnam. Geographia Technica, 16(2), 105-115. https://doi.org/10.21163/gt_2021.162.09.
Madadi, A., Faal Naziri, M., & Piroozi, E. (2022). Evaluation of land use changes and its effects on soil erosion in the basin upstream of Yamchi Dam in Ardabil, using ARAS multi-criteria decision algorithm and modern remote sensing methods. Quantitative Geomorphological Research, 11(2), 52-70. https://doi.org/10.22034/gmpj.2022.315438.1314(in persian).
Mirzaee, N., & Sarraf, A. (2022). Application of data fusion models in river flow simulation using signals of large-scale climate, case study: Jiroft Dam Basin. Watershed Engineering and Management, 13(4), 672–689. https://doi.org/10.22092/ijwmse.2021.343547.1816(in persian).
Mohammad Doust, A., & Shamsnia, S. A. (2023). Identification and zoning of flood-prone areas using AHP - GIS (Case study: Dayyer County, Bushehr Province). Journal of Geography and Environmental Studies, 47, 152-167. https://dorl.net/dor/20.1001.1.20087845.1402.12.47.9.5(in persian).
Mohammadi, M., Ebrahimnezhadian, H., Asgarkhan Maskan, M., & Vaziri, V. (2022). Evaluation of the one and two-dimensional HEC-RAS models' performance in determining flood zone of rivers. Journal of Water and Soil Science, 26(2), 187-201. Retrieved from http://jstnar.iut.ac.ir/article-1-4149-fa.html(in persian).
Noroozi, A. A., Saneie, M., & Rezghi, Z. (2019). Identification and differentiation of rice fields using semi-automatic in north Iran. Journal Name, 4(1), 11-21. https://sanad.iau.ir/en/Journal/iapb/Article/1095958/FullText (in persian).
Pham, B. T., Avand, M., Janizadeh, S., Phong, T. V., Al‐Ansari, N., Ho, L. S., Das, S., Le, H. V., Amini, A., Bozchaloei, S. K., Jafari, F., & Prakash, I. (2020). GIS based hybrid computational approaches for flash flood susceptibility assessment. Water, 12(3), 683. https://doi.org/10.3390/w12030683.
Rezaei Moghaddam, M. H., Hejazi, A., Valizadeh Kamran, K., & Rahimpour, T. (2020). Flood analysis of subbasins using WASPAS model (Case study: Aland Chai Basin, Northwest of Iran). Hydrogeomorphology, 7(24), 83–106. https://doi.org/10.22034/hyd.2020.39815.1534 ‎(in persian).‎
ShahiriParsa, A., Heydari, M., Sadeghian, M. S., & Moharrampour, M. (2015). Flood Zoning Simulation ‎by HEC-RAS Model (Case Study: Johor River-Kota Tinggi Region). 1(1):  ‎https://doi.org/10.5281/zenodo.18264.
Sims, N. C., & Colloff, M. J. (2012). Remote sensing of vegetation responses to flooding of a semi-arid floodplain: Implications for monitoring ecological effects of environmental flows. Ecological Indicators, 18, 387-391. https://doi.org/10.1016/j.ecolind.2011.12.007.  
Sinsomboonthong, S. (2022). Performance Comparison of New Adjusted Min‑Max with Decimal Scaling and Statistical Column Normalization Methods for Artificial Neural Network Classification. International Journal of Mathematics and Mathematical Sciences, Article ID 3584406. https://doi.org/10.1155/2022/3584406.
Swain, K. C., Singha, C., & Nayak, L. (2020). Flood susceptibility mapping through the GIS-AHP technique using the cloud. MDPI, 9(12), 720. https://doi.org/10.3390/ijgi9120720.
Tahmasebi, M. R., Shabanlou, S., Rajabi, A., & Yosefvand, F. (2021). Flood probability zonation using a comparative study of two well-known random forest and support vector machine models in northern Iran. Water and Irrigation Management, 11(2), 223-235. https://doi.org/10.22059/jwim.2021.317527.856 (in persian).
Talaei, R., & Shadfar, S. (2023). Landslide susceptibility modeling using artificial neural network and logistic regression methods at the Saqezchay Basin, south of Ardabil Province. Watershed Engineering and Management, 15(3), 481–503. https://doi.org/10.22092/ijwmse.2022.360475.1996 (in persian).
Vishnu, C. L., Sajinkumar, K. S., Oommen, T., Coffman, R. A., Thrivikramji, K. P., Rani, V. R., & Keerthy, S. (2019). Satellite-based assessment of the August 2018 flood in parts of Kerala, India. Geomatics, Natural Hazards and Risk, 10(1), 758-767. https://doi.org/10.1080/19475705.2018.1543212.
Yousefzadeh, A., Zeinali, B., Valizadeh Kamran, K., & Asghari Sar Eskanrood, S. (2019). The extraction of flood potential of Simineh River Basin applying satellite images, topographic wetness index and morphological features. Geography and Environmental Sustainability, 9(3), 49-61. https://doi.org/10.22126/ges.2019.4294.2071(in persian).
Ziari, K., Rajai, S. A., & Darabkhani, R. (2021). Flood zoning using hierarchical analysis and fuzzy logic in GIS: Case study: Ilam City. Emergency Management, 10(1), 21-30. https://dor.isc.ac/dor/20.1001.1.23453915.1400.10.1.2.2(in persian).
 
Journal of Hydrogeomorphology
Volume 12, Issue 44 - Serial Number 44
October 2025
Pages 150-132

Files

History

  • Receive Date: 24 March 2025
  • Revise Date: 04 July 2025
  • Accept Date: 07 July 2025

Share

How to cite

Statistics