Detection of Susceptible Areas to Flooding and its Most Important Contributing Factors Using the Maximum Entropy Model in the Tashan Watershed, Khuzestan | ||
| پژوهش های آبخیزداری | ||
| Article 8, Volume 33, Issue 4 - Serial Number 129, January 2021, Pages 94-109 PDF (1.75 M) | ||
| Document Type: Research | ||
| DOI: 10.22092/wmej.2020.341563.1307 | ||
| Authors | ||
| Davoud Davoudi Moghaddam1; Ali Haghizadeh* 2 | ||
| 1Ph.D. Candidate, Department of Watershed Management, Faculty of Agriculture, Lorestan University, Khorramabad, Iran | ||
| 2Associate Professor, Department of Watershed Management, Faculty of Agriculture, Lorestan University, Khorramabad, Iran | ||
| Abstract | ||
| One of the most important measures for flood management is the preparation of a flood susceptibility map. The purpose of this study was to identify susceptible areas to flooding using the maximum entropy model in the Tashan Watershed, Khuzestan Province. A flood inventory map was prepared for statistical analysis. Of the 169 flooding occurrences, 70% were used for the model calibration and 30% were used for validation. Ten flooding factors, namely: altitude, slope aspect, distance from the river, drainage density, slope angle, land use, distance from the road, topographic wetness index, plan curvature and lithology were used. The effect and contribution of each environmental parameter were calculated using the response curves and the Jackknife method. Finally, a flooding susceptibility map was prepared in four classes. The receiver operating characteristic curve was used to evaluate the accuracy of modeling. The results indicated that the maximum entropy model had very good accuracy (AUC=0.885) in identifying the prone areas to flooding, and the land use and distance from the road with 50.5% and 20.6% contribution, respectively, had the most impact on the occurrence of flood inundation. Also, about 25% of the watershed area was highly sensitive to flooding. Regarding very good prediction accuracy of the maximum entropy model in detecting susceptible areas to flooding, it is recommended to apply this model for the preparation of the flooding map in other watersheds, especially areas lucking hydrometric stations. | ||
| Keywords | ||
| Flooding management; machine learning; maximum entropy; the Tashan Watershed; validation | ||
| References | ||
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