سید حسین آرامی

Throughout recent years, the Iranian government has mainly been challenged by assessing the risk of dust storms and choosing the best method for their management. In this regard, Golestan province is subject to the high risks of intensified dust phenomenon due to its vicinity to Turkmenistan’s vast deserts, inappropriate management of its northern desert lands, and the dryness of some of its wetlands and borderlands. Therefore, considering the increasing number of intensified dust storm occurrences in Golestan province within the past few years, this study sought to perform statistical and climatic analysis of the phenomenon.  The dust storm occurrences in Golestan province were analyzed based on their spatio-temporal frequency as recorded by seven synoptic weather stations throughout a 10-year period (2013-2022). Moreover, the collected data were checked daily in the form of synoptic codes during eight observations using statistical methods. Finally, the average hourly, monthly, and annual occurrences of dust storms and their frequency for the hot and cold periods in all synoptic stations were calculated using the R software.  The results of the annual analysis of the dust storm revealed that the Incheh-Borun and Maraveh-Tappeh regions experienced the greatest number of dusty days in the entire study area with an average frequency of eight days per year. Moreover, it was found that the highest frequency of dust storms in the province occurred in May, June, and July, and the lowest frequency of dust storms took place in November and December, respectively. Also, the analysis of dust storm occurrences during the statistical period showed that 26.21% and 73.79% of the dusty days originated in extra-local and local origins, respectively. On the other hand, the interpretation of synoptic codes of the statistical period indicated that 79.8% of extra-local dust events in the province occurred from 9:30 A.M. to 18:30 local time and 80.64% of the events took place from 9:00 A.M. 30 to 18:30 local time. Finally, the analysis of the duration of dust events in the study area suggested that currently, the majority of dust storms in Golestan province last for one to two days.

Landslide is one of the natural hazards that causes a lot of human and financial damage every year in mountainous, rainy and seismic areas. Mass movements play an effective role in destroying communication roads, pastures, mountainous areas and causing erosion and sedimentation in watersheds. Identifying landslide-prone areas through risk zoning with appropriate models is unique of the main methods in reducing potential damage and hazard managing. Landslide susceptibility map preparation is known as the cornerstone of landslide research and is used as a management tool in times of crisis. Considering that the identification of landslide sensitive areas based on traditional methods and expert opinions is not accurate enough, the use of modern machine learning methods such as the support vector machine method seems to be necessary and necessary. The purpose of this research is to spatially model landslide susceptibility using two methods generalized linear model (GLM) and support vector machine (SVM) and compare the efficiency of these models in zoning landslide susceptibility in Karganeh Watershed, Lorestan Province.

Karganeh Watershed is one of the large sub-watersheds of Khorramabad with an area of 294.2 square kilometers. The minimum height of the this watershed is 1300 and the maximum is 2700 meters, and 60% of the area of this watershed has a slope of more than 12% (relatively high slope and more).The research method in this study is applied in terms of purpose and in terms of descriptive-analytical nature, library methods, field visits and modeling are used. On behalf of this goal, the distribution map layer of landslides in the watershed including 95 cases of landslides was prepared and separated into two sets for model training (70%) and model validation (30%) randomly. Also, 16 causes disturbing the happening of landslides in this watershed were selected permitting to the review of sources and the usage of principal component analysis (PCA), Tolerance and VIF tests. Digital layers of effective factors in geographic information system were equipped. Slope factors, slope direction, elevation classes, geology, drainage network (distance from the river), road (distance from the road), fault (distance from the fault), topographic indicators (river power index (SPI), topographic moisture index (TWI) and Slope length index (LS)), geomorphological indices (topographic position index (TPI), topographic roughness index (TRI) and power vector index or surface roughness measurement (VRM) of land use, distance from the village, and rain lines as the most effective factors Landslide occurrences in Karganeh Watershed were known. Then, the landslide hazard map was prepared based on the two mentioned methods in the ModEco software environment. Next, in order to evaluate the accuracy of the modeling and compare the efficiency of the models, the relative performance recognition index (ROC) was used.

By correcting the landslide data provided by the General Directorate of Natural Resources with the help of Google Earth satellite images and field visits, 95 landslides were identified, which cover an area of 1483.24 hectares of this watershed. Established on the fallouts of the maximum likelihood diagram, geological, land use, slope, topographic roughness index (TRI), slope length and slope direction are the best significant factors inducing the event of landslides in Karganeh Watershed. The results showed that the support vector machine (SVM) method with ROC equal to 0.913 was chosen as the best model for the basin. The generalized linear model with ROC equal to 0.803 also showed high efficiency in terms of landslide susceptibility evaluation. Permitting to the results of the support vector machine, about 19.3% of the Karganeh Watershed is in the high and very high hazard class of landslide happening. Based on the landslide susceptibility map with the support vector machine model, the villages of Jamshidabad Haider, Milmilk, Garmabala Bala, Bene Soura, Mahmoudabad Bala, Skin Bala, Cheshme Papi, Dareh Qasim Ali and Sheikh Haider are at high and very high hazard of landslides. It were located About 117 kilometers of communication roads in this area were classified as high and very high hazard.

Established on the fallouts of this study, the maximum entropy algorithm provides practical results in order to zone the hazard of landslides in the Karganeh Watershed; By matching the obtained results by the existing real conditions, there is a very high agreement between the fallouts of the landslide hazard zoning map using this model and the real indication in this watershed. Assuming the concentration of management operations in high-sensitivity classes, about 70% of the area of the region will be removed from the management process and will cause the allocation of financial resources and less time. The implementation of landslide management programs based on the results of this research on a local and regional scale can explain the difficulties of domain instability and lead to the development of watershed management actions and the sustainability of the development of the Karganeh Watershed.

Numerous forms of natural dangers and related tragedies containing earthquakes, volcanic eruptions, tsunamis, cloud burst, floods, soil erosion etc. happening the world and amongst such troubling natural hazards landslides are the awful types of greatest recurrent occurrences all everywhere the world. Every year, landslides have affected huge damages of life and stuff, concluded the damages of forests, fruitful cultivated land, habitation area, and network communication in addition to tourist adverts. Additionally, alteration of the earth surface is also responsible for devastating landslides. Iran has confronted numerous categories of natural threats and disasters, for example severe soil erosion concluded gully expansion, vulgar floods, and disturbing landslides. So, because of the numerous occurrences of landslides and huge financial damages have develop national disasters of Iran. The landslide event in Iran has caused about 500 billion financial damages. Landslide susceptibility assessment be able to assistance the planners for final management of environmental squalor and natural resources from delicate damages and eventually development of economic action of this watershed area. The planned methods use both the capability thoughts and ground fact at the like time. This could be taken as a brand-new methodology toward landslide zoning difficulties. The goal of the study is to zonate the hazard of landslide occurrence using maximum entropy (ME) algorithm and compare the effectiveness of this method in locating the sensitivity of landslide occurrence in Karganeh Watershed, Lorestan Province.

Karganeh Watershed is located between 33° 25′ 12″ to 33° 37′ 12″ latitude and 48° 23′ 59″ to 48° 44′ 24″ longitude, occupying about 294.2 sq km in the Lorestan Province, west of Iran. This watershed is one of the main sub basins of Karkheh River. One of the most important phases of landslide susceptibility assessment is to identify and prepare a distribution map of current landslides in the watershed. This map was prepared via assembly the information associated with landslides or via analyzing the data from remote sensing and GIS techniques. On behalf of this goal, the distribution map layer of landslides in the watershed was prepared and separated into two sets for model training (70%) and model validation (30%) randomly. Also, 16 causes disturbing the happening of landslides in this watershed were selected permitting to the review of sources and the usage of principal component analysis (PCA), Tolerance and VIF tests. Digital layers of effective factors in geographic information system were equipped. In the next step, the landslide risk map was equipped based on the maximum entropy machine (ME) method. So that evaluate the accurateness of the modeling and compare the efficiency of the method, the index of the area under the virtual performance recognition curve (ROC) was used. Established on the fallouts of the maximum likelihood diagram, geological, land use and slope are the best significant factors inducing the event of landslides in Karganeh Watershed.

Landslide inventory map indicated that there are 95 scattered landslides in the Karganeh Watershed. Exaggerated total area through landslide is 635 ha (2.23% of the watershed area). Based on the results of the PCA index, the KMO coefficient was calculated as 0.61, which confirms the necessary correlation between the input variables to perform principal component analysis. Among the 19 components as the number of variables investigated in landslide risk assessment, considering the eigenvalue higher than one, the number of the first two main components was investigated. The results showed that these two main components express nearly 67% of the changes. After investigation, slope, slope direction, elevation classes, geology, distance from the river, distance from the road, distance from the fault, river power index (SPI), topographic moisture index (TWI) and slope length index (LS), topographic position index (TPI), topographic roughness index (TRI) and vector roughness measurement index (VRM), land use, distance from the village, and rainfall were selected as the most effective factors of landslide occurrence in the Karganeh Watershed. According to the Kappa index diagram, geological indicators, land use, slope, topographic roughness index (TRI), slope length and slope direction are the most important influencing parameters. The area under the curve (AUC) based on the relative performance detection curve indicates good accuracy (AUC=0.787) in the validation stage. Permitting to the results of the maximum entropy method, about 28% of the Karganeh Watershed is in the high and very high hazard class of landslide happening.

In this study, it was tried to use all effective factors in order to evaluate landslide susceptibility in Karganeh Watershed. Principal component analysis (PCA), tolerance and VIF were used to determine the relationship between the factors influencing the occurrence of landslides and to determine the most effective factors. It is the carrying capacity of the waterway. It actually determines the effect of topography on erosion. The longer the slope is, the higher the sediment carrying capacity is, and the condition of landslides on the side of the waterway increases. An increase in the topographic roughness index (TRI) indicates uplift and nontectonic activity. Changing the rangeland to rain fed farming and road construction is completed severely in Karganeh Watershed throughout recent years and led to awarding great role of human factors on landslide in comparing other factors. Based on the results obtained from this model, 50.4% of the area of the basin is in the very low and low sensitivity class, 21.6% is in the medium sensitivity class, and 28% is in the high and very high sensitivity regional level. An increase in this index leads to more surface roughness and an increase in slope, which makes landslides more prone to occur.The implementation of landslide management programs based on the results of this research on the Karganeh Watershed can explain the difficulties of domain instability.

This research has been conducted in Khuzestan Province covering an area of approximately 64057 square kilometers, over a 28 year time (1995- 2022) period and with the aim of evaluating the spatiotemporal pattern of risk and generating a risk map for dust storms. The utilized data includes hourly measurements from the ground stations (8 time slots within 24 hours). Different models were fitted to the empirical change patterns for spatial variations, and the model with minimum RMSE was selected. Subsequently, using this selected model, interpolation was performed with various neighborhood approaches, resulting in a large number of maps. These maps were converted into a cellular grid network within a GIS environment. Each cell was considered as a zone for calculating the computations related to the risk map. Then the information related to the class of exposed elements, the vulnerability class of elements, and the hazard class were separately applied in each cell. Finally, by calculating the risk equation, a cell-based risk map was obtained. The significant difference in areas was tested using the Chi-squared test. The results of the annual study on the phenomenon of dust storms have shown that the Abadan region has the highest average frequency of 18 days per year, this is the highest number of days observed in the entire study area. 61.11% of regional dust events occur between 9:30 am and 6:30 pm local time, and 81.35% of localized dust storm events occur at 3:30 am. Furthermore, the percentage distribution of regional and extra- regional dust storms during the statistical period indicates that 78.81% of dust storm days originated from extra- regional sources, while 21.19% originated from regional sources. The spatial distribution pattern of the dust storm intensity hazard map exhibits a “moderate clustering” and follows a specific order. The area percentage of risk classes shows that more than 88% of the area in Khuzestan province is in high and very high risk classes, which is one of the reasons related to the larger population in these areas.

Knowledge about vegetation cover and rangelands is the first and most important means of understanding rangeland ecosystems for proper management and optimal use of watersheds, accepted by all relevant departments in this field. This study was conducted in the Bahmai section of Kohgiluyeh and Boyer-Ahmad Province with an area of 1068.54 hectares to provide a suitable biological management plan for the restoration of the watershed according to the natural factors and ecological conditions of the region. After the integration of basic maps in GIS software, a map of biological operations was prepared based on the physical and ecological characteristics of working units. Planting, seeding, enclosure, intercropping, and conservation are for the study area biological programs that have been found suitable. The results obtained from the specifications of the working units of the basin were presented to decide and choose the best management plans according to the characteristics of the operational units by dividing the area of each management plan. Also, most of the area of the proposed operation is dedicated to grazing management and conservation, and due to the unfavorable conditions of the area for agriculture, agricultural lands have the lowest share in terms of area.