Homa Dorostkar Gol Khili, Yadollah Yousefi, Mehdi Ramezanzadeh Lasboyee, Hematollah Roradeh ,
Volume 2, Issue 4 (1-2016)
Abstract
Natural disasters is one of the main challenges for developing countries, which not only cause death and emotional pain and suffering of survivors, but greatly affecting development. Reduction programs and prevention of disasters, including policies that countries to increase community capacity in disaster, are followed to improve the effects of these disasters. One of the risks that affect Iran, is flooding. Iran has a very high risk of flooding, which in most years, about 70% of annual credit plan is paied to reduce the effects of natural disasters. Floods in recent years has left a lot of damage in many parts of Iran. Because the flood event and can not be prevented, but we can assess the resiliency and vulnerability of risks to reduce the effects of flooding greatly. Planning in disaster management process can reduce the risks of accidents and improve the resilience. Thus, how and by what means we can increase the capacity of society to accept a certain level of risk is very important. In recent years, many researches, focused over concept of resilience and disaster risk reduction policy. This research study area is the Nekarud basin in Mazandaran province. Population growth and unethical uses of Nekarud and natural resources, humans and their facilities, infrastructure and natural resources of the basin are vulnerable. The aim of this study was to evaluate the resiliency and identify strengths and weaknesses in the flood affected villages Nekarud margin is based on random sampling of villages (8 villages) have been affected by floods in recent years, were selected. The research method is descriptive and analytical study of its nature. The aforementioned villages to assess the resilience, the four dimensions of economic, social, and institutional infrastructure based on the location of the axis (DROP) provided by Cutter and his colleagues in 2008, was used. According to the surveys and the results obtained, it can be stated that the model DROP, because of the location-based (geographic), and the integrity of the elections aspects and indicators to measure and assess the resilience of settlements is a good model. The dimensions considered to measure resilience include: economic, social, institutional and infrastructure. After determining the dimensions required components and indicators research, scientific references were identified by the study, questionnaires were prepared. Secondly, the need of the rural sample in the form of a questionnaire, collected and analyzed after coding in SPSS. The findings of the study showed that the settlements are in a different situation in terms of resilience in different dimensions. The economic resilience for the total sample is 8.96. The amount of this variable for Zarandin-e Olya, Zarandin-e Sofla, Abelo and Kuhsarkadeh rural settlements is higher than the average whole.
Dr Komei Abdi, Dr Hematolah Roradeh,
Volume 8, Issue 4 (1-2021)
Abstract
Objective: Floods are among the most significant natural disasters in Mazandaran Province, particularly in Sari County, where they cause widespread economic, social, and environmental damages each year. The main objective of this research is to identify and map flood hazard zones using machine learning algorithms, namely Random Forest (RF) and Support Vector Machine (SVM), and to apply an ensemble approach in order to enhance prediction accuracy and reduce model uncertainty.
Method: In this study, a set of spatial datasets including a Digital Elevation Model (DEM), land use/land cover derived from satellite imagery, geomorphological indices (slope, aspect, and drainage density), geological data, distance from roads and streams, vegetation index (NDVI), and climatic variables (precipitation and temperature) were collected. These datasets were processed using GIS and RS techniques and prepared for model training and validation. The models’ performance was assessed using evaluation metrics such as Accuracy, F1-score, AUC, and ROC curve analysis.
Findings: The results indicated that both RF and SVM demonstrated high performance in flood hazard mapping, as reflected by strong evaluation metrics. Moreover, the ensemble approach improved prediction reliability and reduced errors compared to single-model predictions. The generated maps revealed that a significant portion of Sari County falls within high and very high hazard zones, which overlap with are::as char::acterized by intense rainfall, high drainage density, and steep slopes.
Conclusion: This research highlights that machine learning algorithms, particularly when applied in an ensemble framework, are powerful tools for identifying flood-prone areas. The findings can serve as a scientific basis for urban planning, disaster management, and flood risk reduction strategies in Sari County and other comparable regions.
