فهرست مطالب h.r. pourghasemi

Frequency analysis of daily rainfall or return period of rainfall and flooding events is very important considering the behavioral complexity in water resources management; because ignoring it can lead to urban destructive floods. In the present research, three distribution functions of Pearson, Beta, and Gamma were compared to investigate and select the most appropriate distribution function for the precipitation data acquired from meteorology stations and CHIRPS satellite in seven stations in the watershed of Bustan Dam. Statistical analyses showed that satellite data were ineffective to estimate daily precipitation due to high errors in RMSE, MAD, and NASH. Meteorological data were used to spot the best distribution. Google Earth Engine and Python programming language were used. Then, the selected distribution function was used to determine the maximum daily rainfall, frequency probability, and return period of 2, 10, 50, 100, and 200 years. The results of the goodness of fit test, Error Sum of Squares, Bayesian Information Criterion, Akaike Information Criteria well as Kullback-Leibler Divergence showed that in five stations of Kalaleh, Qarnaq, Golestan National Park, Golestan Dam, and Glidagh, the Pearson function is the most suitable distribution function. Also, in the other two stations (Gonbad and Tamar), the Beta function was recognized as a suitable function. However, Gamma distribution in the study area is not efficient. So, it can be concluded that heavy and irregular rainfall can be effective in choosing the best distribution function at each station. Therefore, it is recommended to consider the maximum possible rainfall and as a result of the possible occurrence of floods with principled and accurate management to prevent human and financial losses in susceptible areas, especially in the study area.

Assessing habitat changes is an important step in the spatial prioritization of management efforts, aimed at reducing conflicts. We assessed landuse/cover change in areas with high risk of human-bear conflict in Fars province. In the first step, we predicted the conflict hotspots, using bear damage incidents, a suit of predictors, and the Ensembles of Small Models (ESMs) approach. In the second step, we assessed the trend of landuse/cove changes in a 30-years period in the areas with medium to high risk of conflict, using remote sensing techniques. Results of conflict risk modeling showed that proportion of suitable habitats, distance to village, density of forest patches, and corridor bottlenecks were the main predictors, contributing to bear damaging risk. A total of 3.75 and 6.91% of the landscape were identified as the areas with high and medium risk, respectively. Assessment of landuse/cove changes showed that in a period of 30 years, the extent of croplands and orchards has increased from 12,167 to 52,662 hectares. Such a substanitial landuse/cover changes can increase the risk of bear damages. The obtained results emphasize that inter-organizational planning is an emergency effort in mitigating human-bear conflicts.

Population growth, urbanization, and land use change have increased disastrous floods. Iran is also among the countries at high risk of floods. The latest examples of flood damage are the devastating floods of the spring of 2019 with significant mortality and financial losses in more than ten provinces of the country. The purpose of this study is to prepare an urban flood risk map of District 4 City Shiraz. The vulnerability of the region was made using PROMETHEE Ⅱ and COPRAS multi-criteria decision-making models and urban flood hazard zones were prepared by partial least squares regression (PLSR) and ridge regression (RR) models and a risk map was obtained by multiplying the vulnerability and hazard in ArcGIS software. The highest percentage of the study area in the PROMETHEE Ⅱ and COPRAS models belongs to the moderate class of vulnerability. The evaluation of the vulnerability models using Boolean logic and RMSE and MAPE statistics, showed that the COPRAS model provided better results than the PROMETHEE model. The results of partial least square regression (PLSR) and ridge regression (RR) models in flood risk modeling were analyzed by the Taylor diagram, which showed the superiority of the ridge regression (RR) model and the accuracy of this model in preparing urban flood hazard maps. The risk map of the study area indicated that 34% of the area (973 ha) is in the range of high and very high flood risk.

The present study compares the performance of three statistical models including logistic regression, frequency ratio and maximum entropy in preparing the zoning map of landslide sensitivity on the watersheds of 24 and 25 (Malak-roud and Shen-roud) regions of Guilan province. For this purpose, all the roads of the two areas were survyed and the number of 844 landslides were detected. Then effective data layers on landslide formation were prepared and then zoning maps of the landslide sensitivity were extracted to the three methods. The results showed that the logistic regression model has an accuracy of 74.6 percent of the Area under ROC Curve in prediction of sensitive areas compared to the accuracy of 68.2 and 65.2 percent by the probabilistic frequency ratio model and Entropy model, respectively. Moreover, based on the logistic regression model, the distance and slope factors have the highest effect on the landslide of forest road trenches. According to this study, a total of 4.32 square kilometers (43.25%) of the landslide areas is categorized in very high and high susceptible groups which have occurred along 51.38 percent of the area. Despite the lack of consideration to superstructure conditions in the present study, a notable percentage of existing roads are exposed to the collapse of trenches and require adequate funding for repair operations. According to the logging ban as a new approach in forest utilization and lack of revenue regeneration of alternative conservation projects great dangers threaten the forest engineering installations and road structures.

In order to determine the spatial pattern of the probability of fire in the forests of Boyerahmad city, Belife evidence function models and support vector machines were used. For this purpose, at first 145 past fire positions were reported, MODIS data and field surveys were recorded using GPS, of which 70% were used for modeling and 30% for model validation. Next, 15 factors (altitude, slope gradient, slope direction, topographic position index, topographic moisture index, surface curvature, distance from village, distance from river, distance from road, geological formations, NDVI, land use, evapotranspiration annual, annual rainfall and annual temperature) were selected to assess the fire risk and maps were prepared. After performing a linear test between the independent variables, the Belife evidence function and the support vector machine models were used to create the fire zoning map. For modeling, past fire locations were identified and 70% of the data collected were used as training data for modeling and 30% for model validation. The results of the fire map study showed that areas with very high sensitivity cover 40% of the area. The results of the validation of the performance of the Belife evidence function models showed that the area under the curve was equal to 72.2% and the support vector machine with the area below the curve was 83%.Predication. Results of the current research can be used to plan and manage future fire hazards in the study area.

The aim of present research is landslide hazard zoning using Bayesian theory in a part of Golestan province. For this purpose, landslides inventory map was created by landslide locations of landslide database (392 landslide locations). Then, the maps of effective parameters in landslide such as slope degree, aspect, altitude, slope curvature, geology, land use, distance of drainage, distance of road, distance of fault, stream power index (SPI), sediment transport index (STI), and rainfall were prepared in GIS environment. Relationship between effective factors and landslide locations were considered using Bayesian probability theory. In the next step, parameters classes weights were found and the landslide susceptibility mapping was achieved by fourteen modeling approaches (using whole parameters and deleting parameters one by one). The verification results by ROC curve and 30% landslide locations showed that the Bayesian probability model has 71.37% accuracy for the second approach of modeling in the study area.