1- Univ of Tabriz
Abstract: (668 Views)
Extended Abstract
Introduction and Objective: One of the most important natural hazards that annually inflicts irreparable devastation across the country is flood. Aji Chai basin's large area and special geographical conditions make it one of the basins with high hazards of flooding. As a result, the main aim of the current study is to use a statistical model and multi-criteria decision making techniques to create a flood hazard map for this basin. For this reason, flood hazard maps were created using 18 parameters that are effective in occurrence of floods. The parameters under investigation were: Elevation, Slope, Aspect, Topographic wetness index, Sediment transport index, Stream power index, earth curvature, Drainage texture, Rainfall, Distance to the river, River density, lithology, Hydrological soil groups, Geomorphology, Distance to bridge, Distance to dam, Normalized Difference Vegetation Index, and land use.
Material and Methods: To achieve the research aim the Analytical Network Process (ANP) model was employed as a multi-criteria decision analysis method and the Statistical Index (SI) model was utilized as a two-variable statistical method. To implement the research models, the location of 274 flood points that happened in the past was used. The map of the location of the flood points in the area was prepared through the information of the regional water company of East Azerbaijan province, field survey, and also the Landsat 8 satellite image of the OLI-TIRS sensor.
Results: The results of parameter weighting using the ANP model showed that the three parameters of rainfall, geomorphology, and slope had the highest weight with coefficients of 0.137, 0.104, and 0.101, respectively, which indicates the great influence of these factors in the occurrence of floods in the region. On the other hand, the two parameters of sediment transport index and stream power index have the lowest weight. The evaluation of the importance of the parameters using the SI model also showed that areas near rivers and bridges and low-altitude and low-slope areas are susceptible to flood hazards. The final maps were prepared from the product of the weights of each of the parameters in their information layers and in five classes, from very low to very high potential. Examining the final maps showed that the distribution pattern of hazard zones was similar in both models, and flat and plain surfaces were identified as areas with a high hazard of flooding.
Conclusion: Examining the area of each flood hazard class in the research models showed that in the ANP model, about 34% and in the SI model, 46% of the area of the region were in high and very high areas in terms of flooding. The evaluation of the accuracy of the models using the ROC curve and the area under the curve showed that the SI model with a coefficient of 0.945 performed better than the ANP model in identifying high-hazard areas.
Introduction and Objective: One of the most important natural hazards that annually inflicts irreparable devastation across the country is flood. Aji Chai basin's large area and special geographical conditions make it one of the basins with high hazards of flooding. As a result, the main aim of the current study is to use a statistical model and multi-criteria decision making techniques to create a flood hazard map for this basin. For this reason, flood hazard maps were created using 18 parameters that are effective in occurrence of floods. The parameters under investigation were: Elevation, Slope, Aspect, Topographic wetness index, Sediment transport index, Stream power index, earth curvature, Drainage texture, Rainfall, Distance to the river, River density, lithology, Hydrological soil groups, Geomorphology, Distance to bridge, Distance to dam, Normalized Difference Vegetation Index, and land use.
Material and Methods: To achieve the research aim the Analytical Network Process (ANP) model was employed as a multi-criteria decision analysis method and the Statistical Index (SI) model was utilized as a two-variable statistical method. To implement the research models, the location of 274 flood points that happened in the past was used. The map of the location of the flood points in the area was prepared through the information of the regional water company of East Azerbaijan province, field survey, and also the Landsat 8 satellite image of the OLI-TIRS sensor.
Results: The results of parameter weighting using the ANP model showed that the three parameters of rainfall, geomorphology, and slope had the highest weight with coefficients of 0.137, 0.104, and 0.101, respectively, which indicates the great influence of these factors in the occurrence of floods in the region. On the other hand, the two parameters of sediment transport index and stream power index have the lowest weight. The evaluation of the importance of the parameters using the SI model also showed that areas near rivers and bridges and low-altitude and low-slope areas are susceptible to flood hazards. The final maps were prepared from the product of the weights of each of the parameters in their information layers and in five classes, from very low to very high potential. Examining the final maps showed that the distribution pattern of hazard zones was similar in both models, and flat and plain surfaces were identified as areas with a high hazard of flooding.
Conclusion: Examining the area of each flood hazard class in the research models showed that in the ANP model, about 34% and in the SI model, 46% of the area of the region were in high and very high areas in terms of flooding. The evaluation of the accuracy of the models using the ROC curve and the area under the curve showed that the SI model with a coefficient of 0.945 performed better than the ANP model in identifying high-hazard areas.
Type of Study: Research |
Subject:
بلايای طبيعی (سيل، خشکسالی و حرکت های توده ای)
Received: 2024/01/10 | Accepted: 2024/05/19
Received: 2024/01/10 | Accepted: 2024/05/19
Send email to the article author
| Rights and permissions | |
 |
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. |
