perپژوهشکده محیط زیست جهاددانشگاهیپژوهش و فناوری محیط زیست2676-30602676-30602026-051119articleSomayeh Tokasistokasi@yahoo.com1Mahmoud Tokasim_bidarlord@areeo.ac.ir2Plant Protection Research Department, Gilan Agricultural and Natural Resources Research and Education Center, Agricultural Research, Education and Extension Organization (AREEO), Rasht, IranForests and Rangelands Research Department, Gilan Agricultural and Natural Resources Research and Education Center, Agricultural Research, Education and Extension Organization (AREEO), Rasht, Iran<p style="text-align: left;">Alien species, due to their rapid growth, high adaptability and competitive ability, pose a significant threat to biodiversity. <em>Sida</em> <em>rhombifolia</em> has been observed as an alien plant species in the habitats of Gilan province. The provision of suitable strategies for the management of invasive species is contingent upon precise knowledge of their regional distribution. This study aimed to accurately identify this species and its map geographical distribution across Gilan province. Field surveys were conducted during 2023 and 2024 in various habitats throughout the province and distribution data were collected using GPS devices and the spatial distribution map was generated using ArcGIS software. Results revealed that <em>S.</em> <em>rhombifolia</em> has a widespread distribution in numerous locations across rural roadsides of Gilan province including Amlash, Siahkal, Langrud, Rudsar, Fuman, Rasht, Talesh, Astara and Soumesara. It is also proliferating in the tea plantation. The high frequency of records suggests that this plant is capable of effectively competing with native flora, suggesting the risk of it becoming invasive and posing significant challenges to farmers in future. Therefore, continuous monitoring and management planning are essential to mitigate its potential impacts on biodiversity and habitat structure.</p>http://journal.eri.acecr.ir/Article/51128alien plant invasive plant biodiversity biological invasion GISperپژوهشکده محیط زیست جهاددانشگاهیپژوهش و فناوری محیط زیست2676-30602676-30602026-051119articleseyede Mohadese Hatami Shahkhalimahdishatami1359@gmail.com1Seyyedeh Fatemeh Emamif_emami22@yahoo.com2Farzaneh Nasiri jan aghafzn_nasiri@yahoo.com3habib mahmoodi chenariH.mahmoodi@acecr.ac.ir4Head of the Student and Cultural Affairs Department, Higher Education Institute of the Academic Center for Education, Culture and Research (ACECR), Guilan Province, Rasht, IranPost-Doctoral Researcher, Geography, University of Guilan, Rasht, IranResearcher, Department of Regional Studies, Academic Center for Education, Culture and Research (ACECR), Rasht, IranAssistant Professor and Faculty Member, Academic Center for Education, Culture and Research (ACECR), Rasht, Iran<p style="text-align: justify;">Today, small and medium-sized enterprises (SMEs) are widely recognized as the backbone of national economies and play a vital role in employment creation and income generation. Accordingly, the present study aimed to investigate the factors affecting the development of rural handicraft business clusters. To achieve this objective, the major obstacles and challenges in this field were identified and analyzed. This applied research employed both qualitative and quantitative approaches. Initially, the existing barriers were examined based on the opinions of experts and specialists. Subsequently, the influencing factors were identified, and path analysis was performed using SPSS version 26 to evaluate the direct and indirect effects of each factor. The findings indicate that, considering the identified barriers and challenges, business development, awareness-raising, accessibility, service provision, proximity to key development centers, investor attraction, financial facilities, economic stability, establishment of credit funds, support from relevant organizations, provision of advisory services, and the availability of infrastructural and supporting facilities are among the most influential factors contributing to the development of businesses in the cultural heritage sector.</p>http://journal.eri.acecr.ir/Article/51871Small Businesses Gilan Province Influencing Factors Development Cultural Heritageperپژوهشکده محیط زیست جهاددانشگاهیپژوهش و فناوری محیط زیست2676-30602676-30602026-051119articleیاسر حسینیy_hoseini@uma.ac.ir1Professor, Department of Water Science and Engineering, Faculty of Agriculture and Natural Resources, University of Mohaghegh Ardabili, Ardabil, Iran<p style="text-align: justify;">This study evaluated the groundwater quality of the Moghan Plain based on the Wilcox Index using geostatistical methods. The evaluation results obtained using the cokriging method showed that the covariograms of the random variables electrical conductivity (EC) and sodium adsorption ratio (SAR) followed the Stable model. For the SAR variable, the covariogram had a nugget effect of 0.01 and a sill of 0.08, whereas these values were 0.02 and 1.07, respectively, for electrical conductivity. These findings indicate the dominance of the structured component of the covariogram over its unstructured component and confirm the strong spatial structure of both random variables. Furthermore, the results showed that geostatistical methods, particularly the simple cokriging model, provided accurate estimates of groundwater quality, with a coefficient of determination (R&sup2;) of 0.67 and a normalized root mean square error (NRMSE) of 0.34 for SAR, and an R&sup2; of 0.99 and an NRMSE of 0.007 for electrical conductivity. Overall, approximately 1% of the groundwater in the plain was classified as C₂S₁, indicating its suitability for agricultural use. Among the remaining 99% of the groundwater, which exhibited high salinity and was unsuitable for agriculture, nearly 14% also had excessive sodium levels, making it unsuitable for irrigation.</p>http://journal.eri.acecr.ir/Article/52429Assessment Geostatistics Water quality GIS Zoningperپژوهشکده محیط زیست جهاددانشگاهیپژوهش و فناوری محیط زیست2676-30602676-30602026-051119articleMarziyeh Heydari Marziehheydari1999@gmail.com1Ali FazlolahiFazlollahi@guilan.ac.ir2Seyed Pedram Nainiva Pedram.Nainava@gmail.com3M.Sc. Student, Department of Rang and Watershed Management, Faculty of Natural Resources, University of Guilan, Sowmehsara, IranAssistant Professor, Department of Rang and Watershed Management, Faculty of Natural Resources, University of Guilan, Sowmehsara, IranPh.D. in Watershed Management, Gorgan University of Agricultural Sciences and Natural Resources, Gorgan, Iran<p style="text-align: justify;">Landslides are among the most significant geomorphological hazards, posing a serious threat to natural resources, infrastructure, and human settlements. The aim of this study was to perform landslide susceptibility zonation in the Polrud Watershed using machine learning algorithms and to evaluate their performance comparatively. For this purpose, three machine learning algorithms&mdash;Random Forest (RF), Support Vector Machine (SVM), and Decision Tree (DT)&mdash;were employed, and 19 landslide conditioning factors, categorized into topographic, hydrological, and environmental&ndash;anthropogenic groups, were utilized in the modeling process. The landslide inventory data were randomly divided into training (70%) and testing (30%) datasets for model evaluation. The performance of the models was evaluated using metrics including the Area Under the Curve (AUC), overall accuracy, sensitivity, specificity, precision, correlation coefficient (R), F1-score, CV-ROC, and Cohen's kappa coefficient. The results showed that the RF algorithm outperformed the other models in identifying the spatial pattern of landslide susceptibility and, by providing a more realistic distribution of high-susceptibility zones, classified approximately 24% of the watershed area as high susceptibility. Accordingly, the AUC value of 0.75 and CV-ROC of 0.97 indicate appropriate model stability, although the overall accuracy of 0.62 reveals the impact of data imbalance. In contrast, the SVM model showed a greater tendency to classify areas as low susceptibility, whereas the DT model exhibited lower stability. Variable importance analysis confirmed the prominent role of the morphometric indices TRI and TPI, as well as the hydrological variables SPI and CN, in controlling slope instability. Overall, the research results confirm the superiority of the RF algorithm and its greater efficiency and reliability in landslide susceptibility zonation.</p>http://journal.eri.acecr.ir/Article/52667Zonation Random Forest Geographic Information System (GIS) Natural Hazards Slope Instability