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Introduction and GoalIn recent years, problems caused by human interference in the Salmas Plain, such as an increase in the irrational use of surface water resources, an increase in the process of groundwater discharge, and a change in land use have caused pressure on the groundwater resources of the region; hence, land subsidence is at least a natural response to the decrease in groundwater level. Therefore, it is important to study and identify subsidence areas in the region.Materials and MethodsIn this research, groundwater information and Sentinel 1 radar images were used. Groundwater information was obtained from the regional water company of West Azarbaijan province. In the present research, the DInSAR algorithm was used to measure the ground surface displacement, and the phase difference of the SAR signals was determined using the repeated-pass interference methods. Finally, by using the manual of differential radar interferometry with synthetic aperture and 52 even numbers of Sentinel 1 image, the subsidence in the Salmas Plain was determined between 2014 and 2015 and 2018 and 2019. Validation of the radar interferometry method using Qareqeshlaq Geodynamic station data, as well as groundwater level changes, underground water drop and field observations was conducted.Results and DiscussionThe results of the subsidence study using the DInSAR method showed that the average annual displacement rate is about 5 cm. Investigation of groundwater changes also indicated a decreasing trend in most of the wells. The wells located in the eastern and southeastern regions (e.g., Kangarlu, Qareqeshlaq, Yushanlu, etc.) have the highest amount of groundwater drop. In the study period (2014 to 2019), where the wells with an increase in groundwater level were affected by their proximity to the Zola dam reservoir and the water supply channels. The wells of Qezeljeh, East Qareqeshlaq, and Maidan Doab have the highest water level reductions, but they do not have sufficient water supply from the dam. Mahlam, West Tazeshahr, and Ian wells, which are in the western part of the plain and close to the dam lake, respectively, have a suitable increase. Checking the subsidence maps of the Salmas plain showed that most of the subsidence is concentrated in the southern areas, especially in the southeast of the plain, in comparison to other parts of the plain the depth of alluvium is also greater in these areas. This subsidence trend continued from 2014 to 2019 and this trend is also prevalent in the final map. During this period, the average water level changes in the Salmas plain aquifer had a downward trend, which was in harmony with land subsidence. By reviewing the groundwater drop map, it was found that the areas with the highest drop coincided with the subsidence areas. More drop in underground water indicated more water extraction. Therefore, the discharge of underground water in the region has caused subsidence.Conclusion and SuggestionsBefore adopting any operational plan and policy for implementation, it is the most important task to identify the exact location of land subsidence in any area, in particular residential areas. Sentinel1 radar images are sufficiently capable of solving this problem at all stages; however, a longer observation time would be more useful for decision making and implementation. Land subsidence in the Salmas plain is fully confirmed by the change of the underground water level and field visits. According to the DInSAR approach, the average displacement rate due to subsidence in the region was 5 cm, which can be fully verified in terms of observations of changes in the groundwater level. The piezometric data show a reduction of in the water level in most wells compared to the past, which depicts the occurrence of excessive extraction of groundwater. In addition, field surveys have been conducted in different areas of the Salmas Plain and the subsidence areas have been recorded. The process of subsidence in the studied area is ongoing, and the attention and care of the relevant authorities is needed to reduce the damage caused by this phenomenon and adjust the possible damages. It is recommended that people should be aware of the consequences of excessive harvesting and recommend alternative crops with a low annual demand for water and early yields.

One of the most important types of erosion that causes widespread land degradation is gully erosion which has many changes in different climates. In this study, environmental factors such as soil type information layers, Lithology, land use, NDVI, precipitation, Drainage density and distance to drainage and climate type were obtained using GIS for Sarab Halil watershed of Kerman province. Then, using PCA method, the most important environmental factors were identified and finally, using entropy maximum model, the map of the areas prone to gully erosion was obtained using environmental factors. The results showed that the most important environmental factors affecting gully erosion in the study area are climate, lithology, precipitation and soil type. Map of susceptible areas of gully erosion to environmental factors showed that environmental factors in east, south and southeast have more effects on reducing gully erosion and increase to the center of watershed and north and west of the watershed and the effects of environmental factors in increasing gully erosion increase. Meanwhile, the AUC in calibration modes of entropy maximum model was 0.861 and in validation mode was 0.837. Therefore, the entropy maximum model showed excellent ability to predict prone areas to gully erosion and the model is assumed to be excellent.

Introduction and Objective Floods and earthquakes are the most common and unpredictable natural events that harm communities by affecting life and property and affect the landforms of the basin. Therefore, in ways to reduce the effects of natural events, The main goal has changed to public awareness and emphasis on informing the people in line with preventive measures before the occurrence of abnormal events. Considering these explanations and the need to compare and evaluate the effectiveness of probabilistic statistical methods, The main goal of this research is to investigate the morphological, hydrological and climatic factors affecting the occurrence of floods, Modeling and evaluation of land sensitivity potential in Gorganrood watershed to flood occurrence. Also, the most important innovation of the current research compared with to the previous researches related to risk zonation in the country, the comparison of two probabilistic methods of Shannon entropy and weight of evidence in order to determine the most efficient model and increase the accuracy of flood prediction maps.Materials and Methods 1- Selection of the study area, Collecting and preparing maps of effective factors as independent variables, 15 factors were considered in this research. 2- The flood event distribution map was prepared using the information received from the Ministry of Energy and field observations as a dependent variable, and it was divided into two groups of test points (30%) and training points (70%). 3- Quantifying the effective factors with the help of the frequency ratio index and also performing the multilinear test (independence of the effective factors). 4- Implementation of entropy and weight of evidence models, Preparation of flood sensitivity zoning maps and classifying them into five categories: very low, low, medium, high and very high. 5- Assessing the accuracy of classification and validation of zoning maps and predicting sensitivity to flood events. 6- Determining the importance of effective factors and choosing the most suitable model.Results and Discussion In total, based on the calculated weights of both entropy index and weight of evidence models, in the order of factors land use, altitude, Normalized difference vegetation index (NDVI), soil, Topographic Wetness Index (TWI), Drainage density, Stream power index, slope (SPI), Topographic position index (TPI), lithology, average annual precipitation, Infiltration number, Profile curvature, Stream frequency and Intensity of daily rainfall are effective in flood occurrence. In this research, the land use factor and then the vegetation cover factor have a high priority in the occurrence of floods due to land use changes. The intensity of rainfall is in the last rank, but its importance and influence should not be ignored along with other factors. The maps resulting from flood risk zonation by both models indicate the high accuracy of zone classification and proper distribution of flood points in high and very high zone. Also, based on the receiver operator characteristic (ROC) curve and accuracy classification levels, the efficiency of both models is in the very good category (0.8-0.9) In terms of comparing the models to each other, the results of the area under the curve (AUC) values of the Shannon entropy model show a higher score accuracy in both testing and training situations (0.94 and 0.98) than the Bayesian weighting method (83.83). 0 and 0.88) which indicates a high correlation between the prepared flood sensitivity map and the distribution map of the number of flood events.Conclusion and SuggestionsIn this research, in order to prepare a map of flood event zoning, 368 flood zones with a repetition number of 782 flood events and 15 factors affecting flood events were used. Based on the fitting of both models and the prioritization of the factors affecting the occurrence of floods, it was determined altogether that land use, altitude and vegetation factors have importance and priority in the occurrence of floods in the study area and Profile curvature, Stream power index and Intensity of daily rainfall factors were placed in the last priorities. The results showed that Shannon's entropy model has the appropriate class threshold in classification, and by using 30% of flood occurrence zones, it has more priority for zonation and flood occurrence prediction. But in general, both models are effective in zoning and predicting flood occurrences.

Analysis of statistical relationships and quantitative factors and parameters affecting the production of sediment and soil loss is one of the problems of the watershed. The purpose of this study is to model the relationship between sediment production rate using MPSIAC experimental model factors and observed sediment rate using geomorphic parameters and their relationship with sediment yield in the watershed. Rastamabad watershed of Ilam province was selected by a simple random method with four sub-watersheds identified and equipped with a hydrometric station from the southern basins of Ilam province. In this study, an experimental model was used to estimate the sediment yield. Flow and sediment data of four hydrometric stations and 12 meteorological stations from 1991 to 2020 for 30 years were prepared by the Regional Water Company and the General Meteorological Office of Ilam Province. Physiographic information of the sub-watersheds was calculated from topographic maps with a scale of 1.25000 and the geomorphic features of the sub-watershed were extracted from the digital elevation model. Using factor analysis and cluster analysis, influential factors and variables were identified and sub-domains were classified and divided into homogeneous regions. In order to investigate the correlation between independent and dependent variables, the data normality test was performed by Shapiro-Wilk and Kolmogorov-Smirnov tests in SPSS software. The statistical multiple regression method was used to analyze the relationship between experimental model factors and geomorphic variables with sediment yield of each watershed. The results showed that the sediment yield had a positive correlation with geological factors, land use, upland erosion, river erosion and topography of the watershed and was significant (P≤ 0.001). The amount of observed sediment had a positive correlation with slope, circulatory ratio, rainfall, topography, and area of the watershed and was significant (P≤ 0.001). In order to influence the factors and variables on the amount of sediment in the sub-watersheds, the method of principal component analysis and cluster analysis were used. The results showed that the land use explained 25.24% of the variance of all research variables. Finally, the three factors of land use, upland erosion, and geology, and two parameters of circulatory ratio and slope were explained at 86%. Of the variance of all research variables.

AbstractMarl is a type of mixed carbonate–silicate sediment and contains clay and carbonate minerals deposited in different environments. The terms marl and marlstone are still imprecisely used in geology. I n this study, to revise the nomenclature and sedimentary classification of marl deposits, 99 samples were taken from different terrestrial and marine marl formations. The texture and percentage of carbonate and salt contents in the marls were measured. According to Folk's classification, the majority of the samples are in the range of sandy silt, sandy mud, silt, muddy sand, and silty sand. Based on the electric conductivity parameter (EC), all samples have a high content of salt and are classified in saline and very saline classes. The results based on the classification method of Haldar and Tisljar also confirm that most of the samples are in the category of "calcite clayey siltstone", clayey calcite siltstone, and “calcite-silt clay” and a few examples are "calcite – silt clay” and clayey- silt limestone, except for one sample of the Kond Formation, which is within the marl field. The main samples are not classified by the Pettijohn method and samples are mostly silty, and muddy and the name chalk-salt siltstone and mudstone were found to be more suitable for the terrestrial marl deposits under investigation in this research.Keywords: Continental marl, Carbonate content, Marine marl, Marl Classification, Salinity IntroductionIn geology, the term marl refers to chemical-detrital sediments, which have between 35% to 65% carbonate or clay content. Various definitions have been presented by different researchers about marl, despite relatively different descriptions in all of them, and marl nomenclature has been established on carbonate and clay contents. There is no unanimity regarding the nomenclature of marl sediments and many of the definitions are not precise, and in some of them, different origins are mentioned for marl, such as Terzaghi and Peck (1967), exclusively considered marine origin for marl sediments. Mitchell (1993) related these sediments to the biochemical process, while there is a wide range of marl deposits from terrestrial to marine in nature. Investigating the sedimentary characteristics and granulometry of evaporite marl samples in Iran by Farzami et al. (2014), Hatmian Zarami et al. (2014), Shaban et al. (2015), Haqiqhat et al. (2006) Abazade Chavandarqh et.al. (2006) and Samadi Tabrizi et al. (2013) all point to the fact that among the components of clay, silt and sand, the dominant texture of the samples is silty; in addition, carbonate is not the major chemical component of these samples and instead chalk and salt minerals are dominant. All of them have preferred the terms chalky and salty siltstone and mudstone rocks rather than marl.Marls of Iran can be divided into two categories: terrestrial and marine marls. Terrestrial marls have evaporite particles consisting of silt and clay and chemical substances such as calcite, halite, carbonate and sulfate salts, gypsum and anhydrite or one of these minerals. Marl deposits belong to two groups of Paleogene and Neogene marls in Iran. The origin of these marls are mostly salty lakes and they do not contain marine fossils. They are often red to reddish and pea colored. The surface of the soil has the effects of salt particles, salt crust and the effects of puffiness (Puffy Soil). These marls are often younger and belong to the Neogene period. From a geochemical point of view, they have harmful substances for plant growth, and for this reason, they show great sensitivity to various forms of surface, rill, gully and tunnel erosion (Peyrowan et al. 2014). Marine marls consist of clay and silt and calcite chemical substances. They have little or no evaporite minerals with high solubility such as halite, carbonate and sulfate salts, gypsum and anhydrite. Their origin is an ancient marine environment with normal salinity. They are often green in color. These marls in Iran are pre-Neogeneic in terms of age, i.e., Paleozoic and Mesozoic eras with more vegetation, and erosion mainly surfaces and shallow rills (Peyrowan et al. 2014).The terms marl and marlstone are still used imprecisely in geology (Donovan 2006). According to Donovan and Pickerill (2013), more precise lithological terms should be used for these deposits attributed to marl. This point is noticed by Picard (2010) and Alvarez (2009) stating that the interchangeable use of the terms "marl" and "marlstone" has continued into the 21st century. Picard (1953) has been concerned about the imprecise use of the term marl since almost seven decades ago. Prominent authors such as Alvarez (2009) and Tucker (2011) have also faced certain inaccuracies in the definition of marl or marlstones, and this problem still continues with its effects on published texts of geosciences. The two words "marl" and "marl stone" belong to the past, and surely it is time to be more careful in using such words. These terms have had more of an economic aspect than a precise geological scientific term and have been used for the remedial uses of agricultural soils that have been faced with carbonate deficiency (Neuendorf et al. 2005). As such, the nomenclature of marl sediments in Iran and other countries does not have a correct scientific classification, and this problem is clearly visible even in the maps produced by the Geological Survey and Mineral Exploration of Iran. The problems in the nomenclature of marls in previous research and studies are based on the fact that the amount of carbonate and clay are the classification criterion, while many of these deposits, which are among the marls of the detrital kind, have a silty and muddy texture instead of a clay texture. Also, they have a low amount of carbonate; instead, they have a lot of gypsum and salts. Even marls of marine origin, such as the Qom Formation, do not have the range of marl in terms of the amount of carbonate and clay, except for a few cases. The present research was conducted in the marl areas of Tehran province to determine the sedimentological characteristics of the marls in the study area in order to provide a suitable classification method regarding the sedimentological nomenclature of these deposits. Materials & MethodsThe studied area is located in the south and east of Tehran city, which includes Pakdasht, Varamin, Ivanki and Hassanabad. In general, the region has a dry semi-desert (desert) climate with little annual rainfall. Marl deposits are exposed in the form of hills at the foot of the Alborz highlands and in the southern parts of Tehran and Varamin plains. Due to their high erodibility, these deposits have different forms of surface, rill and gully erosion. Also, they are exposed in the form of single and witness hills during the severe erosion cycle. On the surface of the slopes of these sediments, there are many gypsum crystals, salt crust and sodium fatty stains (slicken slide). Due to the severe soil erosion of these slopes, vegetation is difficult to establish. In the parts where these slopes are covered by Quaternary gravel cover, erosion is less and plant establishment is improved.In this research, different marl units including Pliocene marls, Qom Formation marl and three Upper Red marl members and two Lower Red chalk and salt members were selected for investigation and the following steps were done:1- Sampling of 99 samples from Varamin, south of Varamin, south of Hassanabad, Ivanki and Pakdasht from Upper Red marl members (M1, M2, M3, Lower Red members (OLg, OLS), Eocene marl units of Kond Formation (Ek), marl unit Pliocene (PlM), marl unit of Qom Formation (OlM) and marl alluviums (QtM);2- Determining physical characteristics including granulometry through three steps: Sieve analysis, hydrometer and a combination of both;3- Determining the percentage of carbonate content in marls of the region by acid neutralization method;4- Determining the rate of salinity of the samples based on the electrical conductivity (EC) criterion. Discussion of Results & ConclusionIn this research, with the aim of revising the nomenclature and sedimentary classification of marl deposits from South Varamin, South Hassan Abad, Ivanki and Pakdasht areas, samples were taken from different continental and marine marl units. Granulometry, carbonate percentage and salinity were measured. Sediment classification (Folk 1974) has been used to name the marl deposits of the study area. The samples are plotted in six fields of Folk's triangle, which show predominantly sandy silt, sandy mud, silt, mud, muddy sand and silty sand respectively. The examination of marl samples in the present research shows that the amount of carbonate in the marl samples of marine formations such as the Qom Formation is higher than the continental marl formations and even reaches 68.53%, but in the Neogene continental marl formations of the region including the Lower and Upper Red formations, it ranges from 5.1 to 33.34%, with an average of 20.98%. In these formations, the amount of clay varies in the range of 2.5 to 49%, and the average clay in all samples is 25.49%. The chemical part is not limited to carbonate; chalk and salt are also present in addition to carbonate. Therefore, most of the samples are not classified as marl sediments.The results of the classification method (Haldar and Tisljar 2014) also confirm that the application of marl nomenclature to the studied fine-grained deposits does not have scientific precision and accuracy. Except for one sample of the Kond Formation, which is within the marl class, most of the samples are in the category of "calcite clayey siltstone", clayey calcite siltstone, and “calcite-silt clay” and a few examples are "calcite – silt clay” and clayey- silt limestone.The salinity of the samples was estimated by measuring the electrical conductivity (EC) of an extracted solution. The salinity of the studied marl samples showed that all the mentioned samples have high degrees of EC, which indicates the presence of high salts. The presence of abundant gypsum crystals, white salt crust on the surface of marl, dark spots of sodium fatty color and puffiness of the soil surface (Puffy Soil) are the field evidence of the presence of salts in the chemical composition of the samples. Since the samples are more silty and muddy in terms of texture and have a percentage of carbonate content less than 35% (the threshold for naming marl in the Pettijohn classification) and on the other hand, they are rich in salts, thus the names of “chalky – Salty” siltstone and mudstone is more suitable for the studied marl deposits. Based on the results of the present study, the studied marls physically have more silt than clay, and chemically, more gypsum and salt content than carbonate. This is in line with the findings of other researchers including Abbasi and Amini (2008), Hatmian Zarmi et al. (2012), Shaban et al. (2012) and Farzami et al. (2015). The results of this research are also in line with other researchers including Alvarez (2009) and Neuendorf et al. (2005), Picard (2010), Donovan and Pickerill (2013). Based on the results, it is suggested to revise the nomenclature and classification of the deposits attributed to the marly formations of Iran. The texture of the sediment, the type of mineral composition present in terms of the abundance of calcite or evaporite minerals, as well as the color and marine or continental origin, should be used as criteria, and naming should be done based on the two destructive and chemical components of the sediments with accurate measurements.

Nowadays, landslides are among the geological hazards that data mining methods based on machine learning have been developed to model and predict. This paper addresses the development of a landslide susceptibility assessment that uses machine learning techniques and GIS. Artificial Neural Network (ANN) and Random Forest (RF) were compared for the landslide spatial modeling. The landslide susceptibility zoning maps consider 15 layers including geologic, morphologic, hydrologic, man-made parameters (independent variables) for landslide susceptibility assessment, and Doab Samsami watershed in Chaharmahal Bakhtiari province was chosen for the application of models due to data availability and the 174 total landslide occurrences (dependent variables). The relationship between effective factors and landslide occurrences was quantified and weighted using frequency ratio. Data independence was tested using linear multivariate regression analysis, tolerance, and VIF indices. In order to implement and validate the model, the landslide locations were randomly divided into two subsets, namely, training (70% of the total) and testing (30%), respectively. Subsequently, RF and ANN models were developed and the landslide susceptibility zonation map was produced. Maps were evaluated and validated using frequency ratio & seed cell area index, success rate, area under of receiver Operating characteristic (AUC-ROC). Results illustrated that the two factors of slope length and topographic wetness index have multicollinearity or information overlap and were removed from the modeling process in later stages. Effective factor analysis in both models showed that lithology, land use, and aspect slope factors had a significant effect on landslides, respectively. Also, the results of classification and validation of models showed that the random forest (RF) model (AUC-ROC = 0.919) was more accurate and efficient than the artificial neural network (AUC-ROC = 0.845) for landslide occurrence prediction. The results of this study can be used by executive administrations for management and planning in development and executive plans, including rural-urban development, accurate estimation in erosion models in watersheds, soil conservation, and natural resources protection.

Abstract Besides preventing valuable natural resources from going to waste, using stone waste from stone processing plants in concrete helps reduce environmental pollution and, therefore, offers a convenient route to sustainable development. The present study aims to use granite waste (GW) in high-strength refractory concrete. Sixteen high-strength refractory concrete mixes, including two water-to-binder ratios (W/B = 0.17 and 0.2), two silica-fume-to-binder ratios (SF/B = 0.15 and 0.2), two binder contents (B = 1200 and 1400 kg/m3), and two replacement ratios of silica sand by granite waste (GW/Agg = 0 and 50%) were designed and prepared with high-alumina cement (HAC). The concrete specimens were exposed to 1200 °C. Compressive and flexural strength and scanning electron microscopy (SEM) tests were performed on specimens of concrete mixes before and after heating. It was found that in specimens with high binder content (1400 kg/m3), replacing 50% silica sand with GW (GW/Agg = 50%) in refractory concrete improves compressive and flexural strengths by 3–15 and 4–24% before heating, respectively. It was also shown that using GW to replace silica aggregates in concrete specimens with a 1200 kg/m3 binder content not only did not undermine, but also improved the compressive and flexural strengths of refractory concrete after heating by 20–78% and 15–60%, respectively, as a result of sintering. Meanwhile, in the case of the concrete with 1400 kg/m3 binder content, adding GW exacerbated its loss of compressive and flexural strengths after heating due to little or lack of sintering.

Background: Sexual and marital dissatisfaction seems to be one the most common complications among patients on methadone maintenance therapy (MMT), which may decrease adherence to continue treatment in patients. Efficacy of adding bupropion to MMT protocols has been investigated in previous studies; but evidence for implication of permission, limited information, specific suggestions, intensive therapy (PLISSIT) method for these patients is limited. So, this study was designed to investigate the efficacy of adding PLISSIT as an adjuvant therapy to MMT protocol on sexual dysfunction and marital satisfaction among men patients. Methods: During a clinical trial study conducted in the addiction quitting clinics of Isfahan University of Medical Sciences, Isfahan, Iran, 50 men patients with sexual dysfunction receiving MMT were randomized into two equal treatment group of 6-week PLISSIT program and control. Both groups received slow-release bupropion with a usual dosage (150 to 300 mg/day) for improving sexual function. Sexual function and marital satisfaction for both groups were assessed accordingly using Arizona sexual experience scale (ASEX), and revised dyadic adjustment scale (RDAS). Findings: During the intervention, the mean changes in the overall scores of sexual dysfunction and marital satisfaction were significantly different between the two groups (P < 0.001 for all). Conclusion: Adding PLISSIT to bupropion could improve sexual function and marital satisfaction among men under MMT. Further studies can support these findings for implication of PLISSIT by counselors in addiction treatment clinics.

The optimal utilization management is indispensable due to the limited groundwater resources and water resources high demand. The artificial recharge of aquifers is one of the most important approaches for this purpose, because of high performance and relatively low cost. In order to find the optimum sites for artificial recharge of aquifers, it is necessary to consider the whole effective factors in relation to recharge. In this study, the Rockworks software, widely employed in previous subsurface studies, was used for site selection analysis and final map generation. The case study is located in Surander watershed, Saravan countryside, Sistan and Baluchestan province in Iran. In order to find the suitable sites, it was used for analyzing data layers including slope, infiltration, permeability, alluvium thickness, alluvial quality, land use, watershed discharge, and aquifer layers transmissivity. These factors were weighted in Rockworks interface. Then, each of them was interpolated using Krigging method. All of the maps were produced by using Boolean operation. In the next step, generated Boolean maps were overlaid by using Algebra multiplication operation. Finally, the map of suitable places for artificial recharge was produced. As the result, the susceptible and insusceptible areas of artificial recharge were calculated as 2104.57 (17.33 percent) and 10036.53 hectares (82.67 percent), respectively.