Flood vulnerability zoning of the basin based on the remote sensing indicators and decision making techniques (Case study: Rakat basin of Khuzestan)

Introduction

The expansion of cities in the margins of rivers, alluvial cones, low-altitude coasts, deltas and downstream areas of storage dams has led to an increase in the vulnerability of watersheds to the risk of flooding. This study was carried out with the aim of flood risk zoning and prioritization of flood-prone areas using multi-criteria decision making techniques and remote sensing indicators using Fuzzy AHP and VIKOR model in Rakat Khuzestan watershed. One of the solutions used to identify flood risk and prepare maps of its sensitivity is the use of bivariate and multivariate statistical models, data mining and machine learning. But since many of these models require a lot of data and their calibration is complex, therefore, in recent years, many models have been tested to prepare a flood susceptibility map, among which, the combination of statistical models And decision-making with remote sensing techniques and geographic information system has been of great interest to researchers due to increasing the ability of the model in forecasting. The difference between this study and the studies carried out so far is that in this study, for the first time, multi-criteria decision making techniques and remote sensing indicators are used in the zoning of flood risk in the watershed simultaneously in the watershed. Mountainous and flowing rakat will be used in Khuzestan province and its efficiency will be measured.

Materials and Methods

After making the necessary corrections on the Sentinel 2 satellite images of the region, vegetation indices (EVI, NDVI and SAVI), vegetation density and land use of the region were extracted. Then, by using two multi-criteria decision making techniques (FAHP and VIKOR), weighting of indicators and prioritization of sub-basin flooding were carried out. Finally, after extracting the topography, elevation, soil and geological maps and producing 15 morphometric indicators effective in the flooding situation of the basin, using two multi-criteria decision making techniques FAHP and VIKOR, the weighting of the indicators and the prioritization of the flood proneness of the basin were carried out. became In order to validate and evaluate the multi-criteria decision making models, in the future, with field survey, the use of remote sensing indicators such as NDVI and MNDVI, twenty-five points, flood-prone areas of the basin were randomly selected and placed, and the output of the multi-criteria decision-making models FAHP and VIKOR were validated with these points.



Results and Discussion

It was concluded that among all the indicators, the runoff curve number index, vegetation cover and land use and distance from the waterway account for about 50% of the total flood share of the basin and have The greatest impact on the flood phenomenon is in mountain basins, including the Barkat basin in Dehdez County. Also, the direction of the slope range and the rainfall index (due to the uniformity of the index at the basin level) were found to have the least effect (total less than 5%) among the investigated parameters. It can be said that due to the combination of land use and soil maps, vegetation and rainfall of the basin, as well as the simultaneous effect of land use and soil hydrological group on the flood potential of the basin, it can be a more effective indicator in determining the flood benefit of the basin. The results obtained in this study are consistent with the results of Nouri et al., (2019). Another influential parameter in the flooding of the Rakat basin area (19 percent) is vegetation and land use. The vegetation cover of the area includes agricultural lands, medium pastures, oak forest, and high quality pastures, which respectively had the highest and lowest values in the occurrence of floods, belonging to agricultural lands and high quality pastures. The distance from the waterway is the next influential parameter with a weighted value (about 15 percent), the smaller the distance from the waterway, the higher the value in the occurrence of floods, and the greatest flood potential of the region is in this area. The results of EVI, NDVI and SAVI spectral indices in the two methods of Fuzzy AHP and VIKOR showed that the EVI index has an overestimate and vice versa the SAVI index has an underestimation, but the NDVI index has shown more accurate results of locating the flood prone areas of Rakat Basin.

Conclusion

The results of the study showed that out of the 15 indicators used in the flood zoning of Rakat basin, the vegetation cover indicators are 19%, the runoff curve number is 15% and the distance from the waterway is 15%. The effect was among the investigated parameters. The maps extracted from the two fuzzy AHP and VIKOR methods were determined by using the EVI, NDVI and SAVI spectral indices. On the contrary, the SAVI index has shown the percentage of flooding in high-risk areas with a low estimate, but the NDVI index has shown more accurate results of locating the flood-prone areas of the basin. By summarizing the obtained results, it can be stated that the evaluation of the flood risk maps of the Rakat watershed based on the Vikor model and fuzzy AHP shows the highest agreement with an accuracy of about 68% compared to the Vikor model map with an accuracy of about 40%. with the basic information of the region compared to other models and it is suggested as the optimal model in this region. Finally, the final flood risk map of the basin was located using the fuzzy AHP method, the high risk flood prone areas exactly according to the hydrographic network of the basin, it can be considered the reason for the superiority of this method over the VIKOR method.