Search

An important topic in reservoir studies is to separate different reservoir compartments. Recently, the Fourier Transform Infrared (FTIR) analysis has been regarded for this purpose. Developed in the Persian Gulf, southern Iran, the Yammama Formation is a thick carbonate reservoir that has been dated back to the Lower Cretaceous. Dealing with the compartmentalization problem of Yammama has been a challenging task due to its heterogeneous nature as a major fault cuts through the reservoir. In this research, eleven oil samples were collected from various wells penetrating the Yammama Reservoir to classify its compartments using FTIR and multivariate statistical methods. Five different indexes were extracted from the FTIR analysis diagrams. These represented the aliphatic content, aromatic content, long-chain compounds, and substitution 1 and 2. The extracted information was used as input for hierarchical and k-means clustering methods. The obtained results showed that the Yammama Reservoir comprises two distinguished compartments in terms of the oil chemical composition. The wells located in the northwestern and southeastern parts of the study area were classified under two different clusters. The obtained compartmentalization from the FTIR analysis and the clustering methods was in good agreement with previous findings. [ABSTRACT FROM AUTHOR]

Automotive Industry has an important place all around the world and sales forecasting process supports companies to meet their goals such as sales revenue increase, efficiency improvement, capacity planning and customer care. Traditional methods such as time series and econometrics have been applied by scientists during last decades. However, recently sales forecast problem by means of machine learning techniques are welcomed by data scientists because of increasing power of information technology in both hardware and software aspects. In this research, the hybridization of clustering method, Adaptive network Fuzzy Inference System (ANFIS) and Particle Swarm Optimization (PSO) are developed to forecast annual automotive sales in Iran automotive market. Furthermore, in regard to evaluate the developed model, Artificial Neural Network (ANN) and ARIMA are introduced and comparative analysis in three different scenarios are provided. The results illustrate that proposed method outperforms the rest of techniques and would be more applicable in forecasting problem especially in uns table macro- environments. [ABSTRACT FROM AUTHOR]

Drought is one of the natural disasters that causes a great damage to human life and natural ecosystems. The main differences are in the gradual effect of drought over a relatively long period, impossibility of accurately determining time of the beginning and end of drought, and geographical extent of the associated effects. On the other hand, lack of a universally accepted definition of drought has added to the complexity of this phenomenon. In the last decade, due to increasing frequency of drought in Iran and reduction of water resources, its consequences have become apparent and have caused problems for planners and managers. So in this research, regional frequency analysis using L-moments methods was performed to investigate severity and duration of Standardized Precipitation Index (SPI), Standardized Evapotranspiration Index (SEI), Standardized Runoff Index (SRI), and Standardized Soil Moisture Index (SSI) and to study of meteorological, agricultural, and hydrological droughts in Karkheh River Basin in Iran. Using K-means clustering method, basin was divided into four homogeneous areas. Uncoordinated stations in each cluster were removed. The best regional distribution function was selected for each homogeneous region, and it was found that Pearson type (3) has the highest fit on the data set in the basin. Based on Hosking and Wallis heterogeneity test, Karkheh Basin with H1 < 1 was identified as acceptable homogeneous in all clusters. The results showed that hydrological drought occurs with a very short time delay in Karkheh River Basin after the meteorological drought, and two indicators show meteorological and hydrological drought conditions well. Agricultural drought occurs after meteorological and hydrological drought, respectively, and its severity and duration are less than the other indicators. Meteorological, hydrological, and agricultural droughts do not occur at the same time in all of the years. In general, the SPI drought index shows the most severe droughts compared with the other three indices. By this way, in 5- to 20-year return period with severity of 3SPI and in 20- to 100-year return period with severity of 7SPI, region IV or the western and northwestern areas of the basin has been affected by severe meteorological drought. By using the regional standardized quantities, it is possible to estimate the probability of drought in any part of the catchment that does not have sufficient data for hydrological studies. [ABSTRACT FROM AUTHOR]

Nowadays, one of the growing and remarkable branches of tourism is ecotourism. Ecotourism has a great impact on the development of a region from different perspectives. Hence, by improving and developing sustainable ecotourism in a region, it will be possible to develop and improve its economic, social, and environmental situation. The Lafour region is one of the potential hubs for ecotourism activities in Iran. Despite the high potential of this region for ecotourism activities, it has not yet achieved its suitable position in this domain. Hence, the main goal of this study is to develop sustainable goals for ecotourism in the region. To meet the target, first, the Strengths, Weaknesses, Opportunities, and Threats (SWOT factors) are identified through interviews with tourists, local residents, and the experts, filed observation, and considering similar works in the literature review. Then, some practical and useful strategies are determined based on SWOT analysis and consulting with the experts. The important point of the study is proposing an applicable heuristic clustering method based on SWOT analysis to classify the strategies into different clusters. To prioritize the strategies in each cluster, the commonly used Multi-Criteria Decision Making (MCDM) method called the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method is applied. This paper concludes and reveals that the three strategies including ST6 "Encouraging investors and entrepreneurs to establish ecotourism centers in the region", ST5 "Establishing and improving the hygiene units and the medical clinics in the region", and ST3 "Improving and diversifying tourism services and products to attract tourists and increase their satisfaction" have the greatest impact on accomplishing sustainable development goals in the region. • The main goal of this study is to develop sustainable ecotourism in Lafour region, Iran. • The SWOT are identified by the help of interviews with tourists, tourism experts, ecotourism activists, local residents and academic experts. • Eight strategies are determined based on SWOT analysis and consulting with tourism experts. • A heuristic clustering method based on SWOT analysis is proposed and the strategies are ranked to different clusters. • The strategies in each cluster are prioritized by TOPSIS method. [ABSTRACT FROM AUTHOR]