Irrigation
F. Mirchooli; I. Gholami; M. Boroughani
Abstract
IntroductionFlood is one of the most destructive natural disasters that has a negative impact on social, economic and environmental dimensions. Floods usually occur following a prolonged period of rain or snowmelt in combination with unfavorable conditions. In this regard, all over the world, the occurrence ...
Read More
IntroductionFlood is one of the most destructive natural disasters that has a negative impact on social, economic and environmental dimensions. Floods usually occur following a prolonged period of rain or snowmelt in combination with unfavorable conditions. In this regard, all over the world, the occurrence of floods has intensified by 40% in the last two decades. In Asia, almost 90% of all human casualties caused by natural disasters are due to floods. The increase in flooding is usually due to increased environmental degradation such as urbanization, increased population growth, and deforestation. Periodic and regular occurrences of floods over a certain timeframe significantly amplify the detrimental impacts on living organisms. Urban areas in close proximity to rivers bear the brunt of these damages, owing to high population density, economic infrastructure, and transportation networks. However, these consequences can be alleviated through meticulous vulnerability analysis. One of the primary objectives pursued by researchers and policymakers is the precise modeling and zoning of floods to mitigate associated risks. Consequently, a myriad of methods and approaches have been devised for flood risk modeling and zoning to address this pressing issue. Among them, hydrological methods such as rainfall-runoff modeling and data-based techniques, which are unable to comprehensively analyze rivers and flood zones due to their one-dimensional nature. This is despite the fact that the morphology of the river is not stable and due to its high erosion potential, it also has a dynamic characteristic. In addition, these methods require fieldwork and large budgets for data collection. Hence, comprehensive flood management is necessary to reduce these effects. Therefore, this study was conducted with the aim of identifying areas sensitive to the risk of flooding in Famnat watershed located in Gilan province. Fomanat watershed is located in Gilan province and is considered a part of the first grade watershed of the Central Plateau. This area is located in the range of 36.89 to 37.57 degrees north latitude and 48.77 to 49.69 degrees east longitude. This region has an area of 3595 square kilometers, the highest point of which is 3088 meters and the lowest point is -69 meters. Materials and Methods To carry out the current research, firstly, by reviewing the sources and history of the research, as well as knowing the region, a map and layers of information related to the factors affecting flood susceptibility zoning were prepared. These layers include land use map, slope degree, geology, distance from waterway, digital map of height, direction, shape of land curvature, land curvature profile, rainfall and topographic humidity index, which are created using the collected data and also various additions in the environment. Geographic information system (Arcgis 10.4) was prepared. In this regard, machine learning models such as generalized linear model (GLM), multivariate adaptive regression model (MARS) and classification and regression tree model (CART) were used to zone the sensitivity of the watershed to floods. Also, among 100 flood events, 70% (70) were considered for training and 30% (30) for validation. In the following, using field survey and review of previous studies, 10 factors influencing the occurrence of floods in the watershed area were identified and used. Finally, the area under the ROC curve and the TSS index were used to evaluate the models.Results and Discussion The results of the evaluation of the most important factors affecting the sensitivity of the watershed to floods indicated that the distance from the river, the height and the curvature profile had the greatest impact on the sensitivity of the region, and on the other hand, the factors of slope, geology and topographic humidity index had the least impact. Based on the obtained results, the areas covered by very low, low, medium, high and very high classes in the CART model were 26.6, 17.6, 21.2, 0.1 and 34.0%, respectively. These results for the GLM model were 13.6, 12.7, 16.2, 25.1 and 32.4 percent, respectively. Based on the obtained results, the CART model performed better than other models, so that AUC for MARS model was equal to 0.76, CART model was equal to 0.9 and GLM model was equal to 0.84. Also, the better performance of CART model compared to other models was confirmed by other indicators. So, based on TSS, MARS model equal to 0.52, CART model equal to 0.77 and GLM model equal to 0.66 were obtained.ConclusionImplementing the findings of this study can facilitate the adoption of effective management strategies to mitigate losses and casualties. Moreover, in developing nations grappling with restricted access to hydrogeological and soil data, the utilization of geographic information systems (GIS) and data mining techniques assumes a pivotal role in conducting comprehensive studies. These technologies offer valuable insights and support decision-making processes, enabling proactive measures to address flood risks and enhance disaster resilience in vulnerable regions.
