Davood Jamini

Arsenic is one of the heavy metals that can directly and indirectly pose serious threats to the environment of a region in various ways, including soil and water pollution. Bijar county is one of the geographical areas located in Kurdistan province, whose environment is in an unfavorable condition in terms of arsenic pollution. Considering the importance of resilience in dealing with all kinds of hazards, measuring the resilience of residents exposed to pollution and predicting the factors affecting resilience can play an important role in reducing the risks caused by arsenic. Therefore, the main goal of the current research is to measure and predict the resilience of communities exposed to arsenic pollution in Bijar county. The statistical population of the research consists of residents of six villages of the county (Najaf Abad, Bashuki, Ibrahim Abad, Baba Nazar, Gundok, Ali Abad), which are exposed to more arsenic pollution than other villages and among them, 150 people are considered as a statistical sample. The main research tool for data collection is a researcher-made questionnaire, whose validity and reliability have been confirmed by following the principles of field research. SPSS software and machine learning algorithms (NBTree, Bayesian network, Naïve Bayes and Random Forest) have been used for data analysis. The results of the research showed that the state of resilience in the study area of the majority of respondents (78%) is at high and very high levels. The results of examining the importance of factors affecting arsenic resistance with the IGR method showed that the most important factors affecting resistance in order of importance are: Age (0.343), education (0.271), monthly household expenses (0.232), number of unemployed (0.226), knowledge (0.181), household size (0.171), ownership of capital resources ( 0.17), contingent events (0.116) and main job (0.108). The results of comparing the performance of machine learning algorithms for predicting resilience against arsenic showed that among the investigated algorithms, the NBTree algorithm had the best performance in predicting resilience against arsenic.

Identifying the effective factors in the occurrence of fire and zoning the sensitivity to its occurrence is one of the basic tools to achieve fire control and countermeasures. Forest fire modeling is very important to identify the distribution of forest fires based on scientific methods. In this research, the Information Gain Ratio (IGR) technique and the Average Merit index were used to evaluate the predictive power of factors affecting the occurrence of forest fires in Marivan County. The results of these methods showed that among the 14 effective factors considered at the beginning, 12 factors were involved in the occurrence of fire, which include: annual average wind speed, altitude, relative humidity, precipitation, average maximum temperature , distance from the road, land use, road density, distance from residential areas, NDVI, solar radiation and slope. Also, the results showed that the two factors of slope direction and topographic humidity index were removed from the final modeling due to the mean value of merit equal to zero. Meanwhile, the variables of average wind speed, altitude and relative humidity compared to other variables had the greatest impact on the occurrence of fire. Also, after training all three applied machine learning models, including random forest models, support vector machine and logistic regression, their performance in the field of finding the potential of fire occurrence was measured using statistical criteria. Therefore, in terms of training samples, the random forest model (0.98) had higher accuracy than the support vector machine model (0.931). The value of sensitivity index in random forest and support vector machine models was 0.982 and 0.934, respectively. This means that the random forest model is able to correctly classify 1.98% of fire pixels as fire-dominated areas, which has a higher predictive power than the support vector machine model. The prepared maps were classified into five classes of very low sensitivity, low sensitivity, medium sensitivity, high sensitivity and very high sensitivity based on the Natural Breaks classification method. Also, the area and the percentage of the area of the fire potential floors were extracted for all three models. The results of all three models for the potential of fire showed that the western and southwestern parts of Marivan have a higher fire risk potential than other parts of Marivan. ROC curve method was used to validate all three models. The results showed that among the random forest model, support vector machine and logistic regression, the highest accuracy was assigned to the support vector machine model (0.997).