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Sadegh Asghari Lafmejani, Mahdi Naderian Far,
Volume 2, Issue 1 (4-2015)

Every year, natural hazards happen severely around the world. Iran is included in the first 10 countries in the world susceptible to natural hazards, and has experienced 30 hazards out of total 35 hitherto. In this connection, moving sands, as a natural hazard, creates changes to ecological conditions which cause a rupture in the lives of people. The aforementioned hazards leave adverse effects on human habitations and impose wide environmental and socio-economic damages upon societies. Moreover, the sand mass covers arable lands and residential areas, generates air pollution, brings in destruction of topsoil, harms animals, and brings about many losses. This eases desertification and causes damages. Therefore, taking areas subject to moving sand into consideration is very significant in rural planning. Hirmand township in north of Sistan and Balouchestan province is an area open to moving sand onrush. Unfortunately, due to Sistan drought and Hamoon international wetland dryness as a result of the dominant120-day winds in the area, moving sands have come to affect rural settlements. This has put the villagers of Hirmand township to so much trouble. Hence, an investigation and analysis of rural settlements vulnerability to moving sand damages in the villages of Hirmand township is of great significance as a step toward better control of the problem.

      The present study is a descriptive-analytic survey containing documentary sources, field studies, as well as village and household questionnaires.  The statistical population consists 303 villages in Hirmand township, from which a total of 56 were selected as the sample of the study based on advice given by experts at Housing Foundation of Islamic Revolution, rural administrators and local council members. Analytic hierarchy process (AHP), statistical analyses, spatial analyses, and the software Expert Choice, SPSS, and ArcGIS were used in data analysis. This study, hence, attempts to identify vulnerable habitations and categorize them by employing 54 indexes and assessing and putting them together in different levels.

      According to the findings of this study, from the total villages subject to moving sand problem, 55.35 % are found with low or very low vulnerability and 30.38 % are placed in high or very high vulnerability ranges. The investigation of distribution of the villages under study given the vulnerability intensity to moving sand storm revealed that the villages of low or very low vulnerability are situated in the central and western parts of the area under study. These villages enjoy low vulnerability due to water resources, Tamarix hispida trees planted by state-run entities in the moving sand paths, and being away from dry bed of Hamoun Wetlands. On the other hand, the villages of high and very high vulnerability are placed mostly in the northern part of the area under study and adjacent to Hamoun Wetlands.   

There are several factors playing key roles in vulnerability of rural areas including environmental elements such as stopping of incoming water flow into Hamoun Wetlands, winds of 120 days, wide geomorphological functions of moving sands, and high reduction in the density of vegetation and trees around and in the villages due to drought. In addition to the above factors, inconsistency of physical context of villages with the movement direction of moving sands has caused accumulation of sands in villages which is effective in vulnerability intensification of many rural areas.

    Ruin of houses and cut of communicative roads by moving sands cause disruption in normal lives within the aforementioned villages. In addition to taking damages by moving sands into consideration, the evident role of state services is very significant in decreasing of the damages in all parts of Hirmand. In this connection, belt-like flood preventives built around the international Hamoun Wetlands has made moving sands accumulated behind them and this has decreased intensification of the damages and probable threats from sand onrush to the lower latitude regions.    

    Accordingly, the results of affecting level comparison of different factors in appearance or intensification of the moving sand’s effects in the villages under study revealed that the effects of weather factors and water restrictions sprung from hydrological droughts in which the incoming flow of Hirmand River into the area under study is cut or decreased remarkably, along with summer winds (winds of 120-days) and severe winter winds are more clear and stronger in intensification of soil erosion and formation of moving sands than other factors.   

    On the other hand, the results of impressionability level comparison of different contextual-spatial factors in the villages under study demonstrated that sand affects arable lands and water supply networks more than other factors.

     However, given that reduction or stop of incoming water flow of Hirmand River into Sistan region over the recent years has caused successive droughts, some factors like drying of Hamoun Wetlands, intensification of environmental dryness, reduction of vegetation and increase of soil erosion along with Sistan’s winds of 120-days have paved the way for increasing of dust storms and movement of sands toward the villages of the region.

Batol Zynali, Sayyad Asghari Saraskanroud, Vahid Saffarian Zangir,
Volume 4, Issue 1 (4-2017)

Drought is a concept that is generally understood on a basic level, but is difficult to quantify. Palmer defined a drought as a meteorological phenomenon that is characterized by ‘‘prolonged and abnormal moisture deficiency. A drought can alternatively be broadly defined as a temporary, recurring reduction in the precipitation in an area.

Aridity and drought are not synonymous. Aridity is a measure of long-term average climatic conditions. Both humid and arid regions experience droughts. However, the inter-year variation in precipitation is greater in arid regions and there is a greater probability of below average precipitation in any particular year. Arid regions are thus more prone to droughts and may experience more severe impacts from droughts.

In this research was used temperature and precipitation monthly data of Urmia, Tabriz, saghez, Maragheh, and Mahabad station in statistically period 1985-2014. Run test was used to study the homogeneity of data. Randomness and homogeneity of data was a confidence level of %95. SEPI Index and ANFIS model was used for determining and forecasting drought in Urmia lake basin. SEPI index is more complete than SPI. Results of SEPI were used in ANFIS model.

Fuzzy index SEPI[1]: Standardized precipitation index and evapotranspiration (SEPI) to address some of the disadvantages of SPI index is provided. Evapotranspiration and precipitation index SPI index and SEI standardized integration is achieved. The index is the result of drought monitoring phase of architectural models using fuzzy logic in a fuzzy inference system is designed. How to design this model and determine SEPI is described below.

Fuzzy architecture drought monitoring: for derivatization indices SPI and SEI using Fuzzy Inference System, Due to the structure of fuzzy models were considered.

SPI index[2]: Standardized Precipitation Index is an indicator widely used in Drought Monitoring. This index is one of the few indicators drought monitoring and could even say the only indicator that the time scale is considered. Depending on the time scale to determine the effect of different sources of agricultural drought, hydrological and so determined. Time scale can be determined from one month to several years. SPI index is used to calculate the only element rainy climate. Monthly precipitation amounts for each station in the desired time scale is calculated.

SEI index[3]: Since the index SPI Single Entry, rain, The SPI index values under the influence of changes in temperature and evapotranspiration parameter that is powerful factor in the drought, it will not be. So to enter the effect of temperature and evapotranspiration in SPI, SEI (evapotranspiration index Standard) To calculate this index, before any measures should reference evapotranspiration for the period to be estimated.

define the rules for combining indicators SPI and SEI: Different classes index SPI and SEI rules or the same combination of conditional statements in the form if, as a class of SEPI index in the lead, is defined. This rule only a combination of different modes SPI and SEI indices that lead to SEPI index shows. In this regard, the rules can be combined to fit different for successive written and stored in the knowledge base. Since the output of the resultant composition, indices SPI and SEI are involved in determining the status of SEPI, Weight each of the indicators with regard to the effect of precipitation and temperature parameters on the severity of the drought was considered As a result, SPI indices and weights 0.667  and 0.333, respectively SEI were included in the calculations.

According to the results, according to the research, education Anfis model with 75 percent of the data series is well done SEPI and much has been done to ensure education is nearly 100 percent. So that the graphic maximum of 0.26 percent error in saghez station on a scale of 6 months and the lowest average error of 0.10 percent in Urmia station is on a scale of 6 months. In modeling, validation data, the average error modeling is naturally higher than the average training error. Most average forecast error saghez on a scale of 6 months at the station 0.34 percent and 0.10 percent, the lowest on a scale of Urmia station is 6 months. But the coding maximum of 0.65 percent error in saghez station on a scale of 6 months and the lowest average error of 0.32 percent in Tabriz station is on a scale of 6 months. SEPI index in the time scale of 6 and 12 months is used for investigate the characteristics  of adaptive neuro-fuzzy inference system in order to drought and drought forecasting model. According to the findings in this study, the frequency of drought in the stations of Urmia and Saghez and Maragheh on a scale of 6 months is more than the scale of 12 months in the basin of Lake Urmia but in Tabriz and Mahabad Stations situation is the vice versa. The drought in Urmia Lake basin is increasing trend but temperature has increasing trend with more intensity. The highest and lowest percentage of drought was seen in Urmia and Mahabad station respectively. The results of the forecasting of index by ANFIS model showed that the most training error is in Tabriz station (0.51) and the lowest training error is in Maragheh station (0.36) in a scale of 12 months in coding. In validation data modeling the average of modeling error is higher than the average training error naturally. According to the definition of drought SEPI was presented based on amounts of 0.73 or higher or mild drought to higher floors as dry conditions arise The scale of 6 months in Urmia station with 13.14 percent to 10.89 percent saghez station, Tabriz stations with 5.58 percent, with a 5.1% Mahabad station and Maragheh with the amount of 4.82 percent, the drought has occurred. The time scale of 12 months in Tabriz station by 9%, saghez station with 7.26 percent, with 6.11 percent of Urmia station, Maragheh with 5.5% and the amount of Mahabad stations with a 3.44 percent, from months of study in the series, drought has occurred.

Results of SPEI are:

  1. Drought trend is increasing in urmia lake basin. Temperature has increasing trend extremely.
  2. The highest percentage of drought is in Urmia station and its lowest is in Mahabad station.
  3. Percent of frequency of drought in Urmia station, Saghez and Maragheh on a scale of 6 months is more than to 12 months, but the scale of Tabriz and Mahabad stations with the photos. Stations Tabriz and Mahabad is in the opposite situation.

Results of ANFIS Model are:

In study area and in ANFIS model whatever forecasting coming years is shorter; confidence of forecasting will be more.

Due to the errors amount obtained in model validation, in study area forecasting of drought by ANFIS model was done with confidence 94%.

[1] - The combination of indices SPI (Standardized Precipitation Index) and SEI (evapotranspiration index standard) based on the rules of the Fuzzy Inference System.

[2] - Standardized Precipitation Index

[3] - Standardized Evapotranspiration

Dr Sayyad Asghari, Hasan Mozafari,
Volume 6, Issue 4 (2-2020)

Evaluating and comparing the performance of Frequency ratio coefficient models and network analysis in Rock fall zoning
(A case study of Zanjan-Taham-Tarom Road)

Extended Abstract
One of the natural hazards of the collapse of rocks from the foothills of the mountains, causing great financial losses and loss of life. Especially when it comes to the path of communication. The rock fall is a rapid movement of a mass without cohesion in the powder or a mixture of soil and rock, so that the initial construction is not discernible, the level that occurs along that rupture it is often unclear. The falling stones of a mountain depend on several factors, which have the natural origin or origin of human origin. Natural factors influencing the fall can be rock factors, slopes and altitudes, geological structure, fault and slope of the geological layers, rainfall and temperature changes distance from the river, etc. human factors can also be referred to as road, land use and mining, destruction of vegetation, etc. in Iran, the collapse of rock parts on mountain roads causes massive loss of life and financial damage. Therefore, it is necessary to identify and classify the roads in terms of risk of suitable methods. In the north west of Iran it has mountainous topography and due to the state of tectonic and its seismic and climatic conditions, suitable conditions for landslide are provided in some domains. So, due to the fact that the area studied in the mountainous north - west region and the possession of all the crumbling conditions are very prone to collapse.
The research method is applicable in terms of practical purpose and the process of doing work on a combination of library and field methods. In this study, it has been used to determine the prone areas of collapse and zoning of anp models and frequency ratio. Two models that differ in terms of process and mechanism. In order to organize the research framework, first, a field study of the study area has been studied and the mathematical position of falling points is recorded with gps. Then, in order to model the mentioned models, the layers of GIs for the shape of the Georeferenced and digital were prepared. to provide the zoning layers of geology , slope and Aspect , elevation levels , land use and vegetation , fault , and land cover maps , annual temperature and precipitation , distance from the road , distance from the stream were used . The 20 m x 20 m contour line were originally prepared using the topography map of 1: 25,000 in the ArcGIS environment. Then, the contour line and Dem of the area were constructed. The slope and Aspect maps, elevation levels, Isothermal and isohyet map frost and stream network were created via Dem and meteorological data. Geological map and fault map were created using digital map 1: 100,000 Zanjan and map and vegetation map and road distance using Landsat 8 - 2017 OLI and ArcGIS images. To produce linear layers, the Distance function was used.
Using the statistical method, the frequency ratio and the network analysis method are using the landslide hazard zonation using the statistical method, the frequency ratio and analysis of network analysis to zoning the risk of falling by combination and sum of maps in class were low-risk to very high. From the tangible results of this study, the relationship between slope maps, elevation levels, rock material, Isothermal and isohyet is done. So that each side of the road had operated on the five factors that had happened. With regard to the output of the maps, the risk zones were high to very high for ANP models 14/17, 35/27 and FR 02/6, 35/14 percent. Ranges from high to very high with slopes between 40 and 80 percent and Sedimentary formations such as sandstone, siltstone with tuff layers, elevation levels 1,500 – 2300, Southern and Eastern slopes, Distance between 0 and 500 faults, Road and stream have adaptations . The changes in the percentage of area in both models show despite the difference in the size of the risk zones, Follow a similar process. To assess the zoning accuracy of these methods, two sets of quality and accuracy index (experimental probability) were used. The evaluation of the models showed that in the network analysis model, the indexes were 0.76 and 0.88, respectively, that the relation of frequency ratio coefficient model had optimal quality and accuracy.
In this research, various factors influencing the occurrence of rock falls on Zanjan-Taham-Tarom road were investigated. From there, mass movements such as rock fall on the roads act as a system, as a result, all factors play a role in the occurrence of such phenomena. But some elements have a more vibrant role. In the studied area, among the factors affecting lithology, slop, elevation levels, precipitation, temperature changes, number of freezing days and distance from the road and land use are more than other factors in the occurrence of rock fall. Assessing the quality and accuracy of zoning maps while confirming zoning accuracy showed that the network analysis method has better performance. The risk of collapse on Zanjan – taham- tarom road is always exists. Therefore, we need to use sustainable methods to reduce the risks. Domain stabilization methods are generally done in the form of mechanical, biological and bio-mechanical which, according to the long course of the road and the duration of the road, Mechanical methods such as unloading , embankment , drainage , use of separation walls as well as the use of  net Grid are suggested .
Keywords: Frequency ratio, Network analysis, Zanjan Road - Taham - Tarom

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