Prediction of rainfall using artificial neural networks for synoptic station of Mashhad: a case study

In this paper, we have utilized ANN (artificial neural network) modeling for the prediction of monthly rainfall in Mashhad synoptic station which is located in Iran. To achieve this black-box model, we have used monthly rainfall data from 1953 to 2003 for this synoptic station. First, the Hurst rescaled range statistical (R/S) analysis is used to evaluate the predictability of the collected data. Then, to extract the rainfall dynamic of this station using ANN modeling, a three-layer feed-forward perceptron network with back propagation algorithm is utilized. Using this ANN structure as a black-box model, we have realized the complex dynamics of rainfall through the past information of the system. The approach employs the gradient decent algorithm to train the network. Trying different parameters, two structures, M₅₃₁ and M₇₄₁, have been selected which give the best estimation performance. The performance statistical analysis of the obtained models shows with the best tuning of the developed monthly prediction model the correlation coefficient (R), root mean square error (RMSE), and mean absolute error (MAE) are 0.93, 0.99, and 6.02 mm, respectively, which confirms the effectiveness of the developed models.     

Lower Miocene oyster shells from the Southeast Zagros Basin (Zade Mahmud area, Iran) and their paleoecology

The Guri limestone Member of the Mishan Formation in the Zagros Basin consists of thick bedded limestone bearing benthic foraminifera and oyster shells. Seven species of Ostreidae and Gryphaeidae were identified as belonging to four genera (Crassostrea, Cubitostrea, Ostrea, and Hyotissa), i.e., Cubitostrea frondosa, Ostrea (Cubitostrea) dubertreti, Cubitostrea digitalina, Crassostrea gryphoides, Hyotissa virleti, Ostrea vesitata, and Ostrea plicatula. These fossils are reported for the first time from Iran. Miocene deposits in the studied area contain a rich benthic foraminiferal fauna dominated by Pseudotaberina, Meandropsina, Miogypsina, Flosculinella, Borelis, and other larger benthic foraminifera. We assign these sediments to a Burdigalian age based on Borelis melo curdica–Borelis melo melo Assemblage Zone. Paleoecological considerations also revealed that the beds were deposited in a near-shore non-agitated and shallow-water environment with moderate to low sedimentation rate. The studied oysters are located in one of more important paleogeographic settings in the world and very similar to many other Tethyan regions. The presence of these oysters suggests that the Zade Mahmud area located at margins of a sea way that connected the north and south of Zagros Basin during Burdigalian.     

Arsenic in the Muteh gold mining district, Isfahan, Iran

Following the appearance of symptoms of arsenic toxicity in the inhabitants of villages in the Muteh gold mining region, central Iran, the concentration of this element in various parts of biogeochemical cycle is investigated. For this purpose, rock, groundwater, soil, plant, livestock hair and wool, and human hair samples are collected and analysed. Total arsenic content ranges from 23 to 2,500?mg/kg in rock samples, 7?C1,061???g/l in water, 12?C232?mg/kg in soil, 0.5?C16?mg/kg in plant samples, 4.10?C5.69?mg/kg in livestock hair and wool, and 0.64?C5.82?mg/kg in human hair. Arsenic concentration in various parts of biogeochemical cycle near the gold deposit in a metamorphic complex, and also close to the gold-processing plant, is very high and decreases exponentially with increasing distance from them. Arsenic concentration in water from a well close to the Muteh gold mine is above 1?mg/L. Arsenic in hair samples taken from local inhabitants is above the recommended levels, and the control samples in Shahre-Kord city. Arsenic concentration is higher in male population and correlates positively with age. It is suggested that arsenic resulting from the decomposition of ore mineral such as orpiment (As₂S₃), realgar (As₂S₂) and arsenopyrite (FeAsS) is responsible for polluting natural resources and the human intake via drinking water and the food chain. Gold mining and processing has undoubtedly enhanced the release of arsenic and intensified the observed adverse effects in Muteh area.     

The biostratigraphy of the uppermost calcareous nannofossils of Cretaceous deposit in both Shahdar and Namazgah sections (NE Iran)

A moderately various calcareous nannofossil assemblage of 44 species assigned to 24 genera is identified in samples collected from the Shahdar section and 18 genera and 43 species in Namazgah section. Testing of calcareous nannofossil has permitted recognition of few important coccolith events in stratigraphic interval occupied by the uppermost layers of Cretaceous in the Shahdar and Namazgah sections. These events are correlated to the CC24–CC26 of Sissingh (Geol Minjbouw 56: 37–65, 1977) in two sections. According to these biozones, the age of the studied sections in Shahdar and Namazgah is Early Maastrichtian–Late Maastrichtian. On the basis of paleoecological interpretation, the last layers related to the Cretaceous deposits in two sections were deposited in shallow marine environment in relatively low latitude, and the depth of the basin from the Cretaceous deposits toward Fajan Formation is minimized.     

Burdigalian–Serravalian calcareous nannoplanktons from Qom Formation, North-Center Iran

The aim of this study is to determine calcareous nannofossil species found in Kamar-Kuh area and demonstrate this use for biostratigraphic studies. The Qom Formation is widely distributed in Kamar-Kuh section and has nine members(a–f). In this study, 12 samples were taken and prepared with smear slide. Some of the determined species are listed below, and some of them are photographed by light and scanning microscope. According to obtained nannofossils, 16 species, nine genera, and six families were determined. Obtained nannofossils indicated age of Burdigalian–Serravalian (middle Miocene) for this section.     

Landslide susceptibility mapping using Genetic Algorithm for the Rule Set Production (GARP) model

Investigation on landslide phenomenon is necessary for understanding and delineating the landslide prone and safer places for different land use practices. On this basis, a new model known as genetic algorithm for the rule set production was applied in order to assess its efficacy to obtain a better result and a more precise landslide susceptibility map in Klijanerestagh area of Iran. This study considered twelve landslide conditioning factors (LCF) like altitude, slope, aspect, plan curvature, profile curvature, topographic wetness index (TWI), distance from rivers, faults, and roads, land use/cover, and lithology. For modeling purpose, the Genetic Algorithm for the Rule Set Production (GARP) algorithm was applied in order to produce the landslide susceptibility map. Finally, to evaluate the efficacy of the GARP model, receiver operating characteristics curve as well as the Kappa index were employed. Based on these indices, the GARP model predicted the probability of future landslide incidences with the area under the receiver operating characteristics curve (AUC-ROC) values of 0.932, and 0.907 for training and validating datasets, respectively. In addition, Kappa values for the training and validating datasets were computed as 0.775, and 0.716, respectively. Thus, it can be concluded that the GARP algorithm can be a new but effective method for generating landslide susceptibility maps (LSMs). Furthermore, higher contribution of the lithology, distance from roads, and distance from faults was observed, while lower contribution was attributed to soil, profile curvature, and TWI factors. The introduced methodology in this paper can be suggested for other areas with similar topographical and hydrogeological characteristics for land use planning and reducing the landslide damages.     

Realization of rainfed wheat and barley production environment based on drought patterns in the northeast Iran

Theoretical and Applied Climatology - High spatiotemporal climate variability in the northeast Iran has led to increased production risk for rainfed crops. Hence, to explore potential opportunities...     

Applicability of generalized additive model in groundwater potential modelling and comparison its performance by bivariate statistical methods

Groundwater is the most valuable natural resource in arid areas. Therefore, any attempt to investigate potential zones of groundwater for further management of water supply is necessary. Hence, many researchers have worked on this subject all around the world. On the other hand, the Generalized Additive Model (GAM) has been applied to environmental and ecological modelling, but its applicability to other kinds of predictive modelling such as groundwater potential mapping has not yet been investigated. Therefore, the main purpose of this study is to evaluate the performance of GAM model and then its comparison with three popular GIS-based bivariate statistical methods, namely Frequency Ratio (FR), Statistical Index (SI) and Weight-of-Evidence (WOE) for producing groundwater spring potential map (GSPM) in Lorestan Province Iran. To achieve this, out of 6439 existed springs, 4291 spring locations were selected for training phase and the remaining 2147 springs for model evaluation. Next, the thematic layers of 12 effective spring parameters including altitude, plan curvature, slope angle, slope aspect, drainage density, distance from rivers, topographic wetness index, fault density, distance from fault, lithology, soil and land use/land cover were mapped and integrated using the ArcGIS 10.2 software to generate a groundwater prospect map using mentioned approaches. The produced GSPMs were then classified into four distinct groundwater potential zones, namely low, moderate, high and very high classes. The results of the analysis were finally validated using the receiver operating characteristic (ROC) curve technique. The results indicated that out of four models, SI is superior (prediction accuracy of 85.4%) following by FR, GAM and WOE, respectively (prediction accuracy of 83.7, 77 and 76.3%). The result of groundwater spring potential map is helpful as a guide for engineers in water resources management and land use planning in order to select suitable areas to implement development schemes and also government entities.     

Mineral chemistry and Ti in zircon thermometry: Insights into magmatic evolution of the Sangan igneous rocks,NE Iran

The Sangan Magmatic complex (SMC) is, a large I-type magmatic complex, located in the northeastern Iran. Zircons extracted from the intrusive and volcanic rocks within the SMC record a similar Hf compositions and REE patterns, indicating that these chemical signatures have likely been inherited from the same source and simple history of magmatic crystallization during the evolution of the orogeny. The zircon from volcanic rocks yield Ti-in-zircon crystallization temperatures of 667–1145?°C with average temperatures of 934?°C while those from granitoids indicate crystallization temperatures of 614–898?°C with an average of 812?°C. Ti-in-zircon, Ti in biotite thermometries also indicates that the crystallization temperatures of volcanic rocks are relatively higher than those of granitoids. The biotite chemistry studies reveal that this mineral crystallized at approximately 725°–800?°C and 758° to 816?°C for granitoid and volcanic rocks, respectively, which is similar to obtained temperatures by Zir-saturation of Eq. (1). T_zicsat and T_magma trend lines on the T-SiO₂ diagram cross at high silica contents of ～68?wt.%, at which temperature the magma becomes zircon-saturated and new zircons are crystallized. The zircon REE data including Ce/Ce*, Eu/Eu*, and Th/U ratios suggest that SMC igneous rocks are formed from oxidized magma. However, the zircon Th/U and Hf data suggest that the SMC became progressively more oxidized and also indicate lower temperatures from volcanic and plutonic rock with decreasing time.