بهناز سرائی

As a dynamic system, the river always changes its location and morphological characteristics according to time, geomorphic, geological, hydrological factors and sometimes due to human intervention. Masuleh Rudkhan is one of the most important rivers in Iran that the formation of bed geometry in different periods is very different from each other. This river has a dynamic morphological behavior under the influence of various factors such as the geology of the region, the characteristics of the alluvial structure, the hydrological characteristics of its upstream basin, the structures in it and the hydraulic conditions of the flow. The purpose of this paper was to identify and extract the changes of Masouleh Rudkhan in Guilan province in the period 2000 to 2020 using satellite image processing. The images used were the images of Landsat 5 satellite on 06/06/2000 and Landsat 8 on 13/06/2020. SVM classification method and NDWI, MNDWI, AWEI and WRI indices were used for image processing. Changes in the SVM classification method showed that the river area has decreased by 314.26 hectares from 2000 to 2020. These changes mean an increase in construction on the riverbed and a decrease in the amount of water in the river. Comparing kappa coefficient and overall image processing accuracy, it was observed that AWEI index with kappa coefficient and overall accuracy of 0.93 and 0.95 in 2000 and kappa coefficient and overall accuracy of 0.94 and 0.96 in 2020 had the highest accuracy and Masouleh Rudkhan route in this index to Google earth moved. By examining the river route in a period of 20 years, it was observed that at 5 km from the beginning of the basin, the river route is 100 meters to the south, at 7.5 km, the river route is 50 meters to the south, at 29.3 km, it is 45 meters to On the south side, at km 48, it is 38 meters to the north and at km 50 to 56, it has changed continuously and the reason for this change is the high erosion in this part of Masouleh River.

Land subsidence is one of the environmental hazards that are detrimental consequences cities, facilities, communications facilities and agricultural land in the long term. This research has done through field works such as sampling plains deposits, measuring the thickness of stratifications, the type of formations, granulometry and drawing related Charts with Imaging, reviews documents, experimental and laboratory methods. The maximum thickness of alluvial deposits accumulated (Quaternary) is more than 160 meters according to geological section at the highest level subsidence in Hadishahr plain and in Harzandat plain is more than 200 meters. Quaternary alluvium huge with large stones, associated with strong floods, especially in the early Holocene and violence roughness in the past is interpreted and explained. The thickness of materials along the Pir Eshagh-Zal valley from 20 meters in the village of Pir Eshagh is variable to 110 meters in the East Village of Zal. In fact, Harzandat , Hadishahr and Gollfaraj plains with an area 165 km2, are Graben cumulative plains where are develop and evolution within Horst fault zones. The form of structural stratifications of the plain and Horst following the directions of the lateral and vertical forces along with north-west folding have found faulting and due to tectonic uplifting and sinking the level of local area, the main channels have been cased the typical fluvial terraces at the top of the alluvial fan deposits with drilling and digging

Today, burying waste is the most common method in many countries due to its lower cost and the existence of a wider spectrum of waste. In the location of the waste burial site, . For this purpose, several multi-criteria decision making methods are used in prioritizing suitable places. In the research, 10 important factors were used to locate the waste disposal site, which are: land use, slope of the area, altitude of the area, geology of the area, distance from roads, distance from rivers, distance from springs, distance from wells, distance from villages. and distance from faults. ANP model was used to prepare the final zoning map. In the first stage, the criteria were prepared and converted into GIS format. Then it was converted to raster format and fuzzification was done. According to the results obtained, the weight of land use criteria, the slope of the area, the height of the area, the geology of the area, the distance from roads, the distance from rivers, the distance from springs, the distance from wells, the distance from villages and the distance from faults for the study area is 0.167. 0.087, 0.036, 0.028, 0.138, 0.064, 0.068, 0.047, 0.120, 0.243 were obtained. Therefore, the criterion of the distance from the faults has the highest weight and the geological criterion has the least influence in locating waste burial areas according to experts. The obtained map was classified in 5 different classes. By examining the overlapping criteria map, it was observed that suitable areas for landfilling are located near rivers and springs and residential and rural areas. By examining the final zoning map, it has been observed that the most suitable areas for landfilling are located in the westernmost part of the study area, with a focus on Agha Seyed Sharif road.

Natural crises such as landslides are capable of inflicting irreparable damage on humans and the environment. Therefore, risk assessment is critical in order to properly manage and reduce damage. Risk assessment is characterized by the process of estimating the probability of an event occurring and the significance or severity of its adverse effects. The aim of the present study is landslide risk zoning in Nekarud watershed using fuzzy logic and remote sensing.

The most important indicators used in the research include: precipitation classes, altitude classes, geology, NDVI, distance from the river, distance from the fault, slope were identified after reviewing the research literature and expert opinions.

In the AND operator fuzzy inference zoning maps, 1% are in the very low risk category, 2% in the low risk category, 3% in the medium risk zone, 18% in the high risk zone, and 76% in the very high risk zone. The logic of AND, because of the sharing between the criteria, has zoned many highly exposed areas and identified 80% of the basin as high risk. High-risk areas in this model are located in the whole basin. In the OR zoning model, 72, 12.4, 10, 2% are located in very low, low, medium, high and very high risk areas, respectively. Therefore, most of the area in this zoning model is in a very low class, and this logic considers many areas as safe due to the aggregation between criteria, which are located in the west and center of the basin, and only a very small part of the center of the study area is very risky. Highly detected in the eastern basin. Finally, the GAMA operator zoning model was 49%, 9%, 14%, 15%, 13%, respectively, in the very low, low, medium, high, very high risk classes, and the very low risk class covered most of the study area.

In this zoning, most of the vulnerable areas are located in the eastern part of the study basin and are more in line with reality. As the results of the present research showed, the use of RS and GIS technology is a useful and powerful tool in the field of zoning and landslide risk assessment.

Floods are a natural phenomenon that causes heavy losses of life and property and ‎human societies every year and people have accepted it as an inevitable event. In this research, to predict the flooding in Sardabrood basin ‎ SWAT hydrological model was used. Information needed for this research, ‎including topographic maps, land use, soil data, and meteorological data, data about daily ‎rainfall, temperature, and flow rate were prepared beforehand. SUFI2 program was used for model ‎calibration. After the calibration and optimization of the model, validation of the model in the ‎study area was done. The calibration of the model wad performed for the years 2003 to 2009 and validation was performed for ‎the years 2010 to 2013. To analyze the results of statistical indicators R2, bR2, and Nash Sutcliffe ‎coefficient were used. After model calibration the respective coefficients were, 0.77, 0.63, and 0.77  and ‎the respective validation coefficients were  0.79, 0.76, and 0.71. The sensitivity results of 31 parameters that are influential on runoff water showed that fixed parameters of groundwater base flow, its time, and the minimum amount of water necessary for groundwater base flow are more sensitive than other parameters. Number Sardabrood parameter curves were used for flood basin. Sardabrood basin was divided into 24 areas to study the flooding pattern. Finally using the above mentioned models it was concluded that sub basin No. (6)  had the first-rank with runoff 123.05, ‎‎sub basin No. (10) had the second rank with runoff in terms of flooding and sub basin No. 1 with a 12.33 has the least amount of runoff.‎