The present study concerns the effects of different magnetically treated waters (distilled water (as control), domestic water, saline water (-3 MPa), waste water and purified water of Arak city) on emergence and growth of Medicago scutellata (Var. Rabinson) seedlings under greenhouse conditions. The various waters were treated by passing it through 250 mT magnetic fields at flow rate of 2 lit/min. One hundred seeds per treatment were soaked in magnetically-treated waters for 12 hours and cultivated in pots with sand bed. The number of seedlings emerged was counted on daily basis, whereas growth data was measured on the 20th day after planting. Seedlings from exposed seeds to magnetically treated waters showed an improvement of 5-10 percent in the emergence and a 5-14 and 2-16 percent increase in root length and weight, respectively. Dry weight of emerged seedlings in pots by magnetically-treated waters, in comparison with those in untreated pots (control) increased for distilled water (14.4%), domestic water (16.3%), saline water (9.18%), and purified water (2.92%). The results of seedling lengths in the pots of magnetically-treated waters showed that, 5.54, 14.82, 14.67, 13.75, and 14.04 percent increased, respectively. From a practical point of view, it was concluded that could be a promising technique for agricultural improvements.

Climate variability has a crucial role in rainfed farming, especially in dry climates and evaluation of these fluctuations under different climates provides a framework for further studies. Iran was classified into very dry, dry, semi-dry, and humid climates by using the FAO56 index. This study investigated the equations obtained from multiple linear regression (MLR) and the gap between observed and predicted yield of rainfed wheat and barley yield in different climates across Iran. Climatic data including; rainfall (R), mean temperature (Tmean), solar radiation (S), and wind speed (U2) collected from 44 synoptic stations during 1981–2020 and were used as the inputs of a MLR models to simulate rainfed wheat and barley yield. Global Performance Indicator (GPI) was utilized to evaluate the performances of the MLR models, which is a 5- statistical criteria index. The results showed that the lower statistical error criteria values of MLR models confirmed their better performance than MLR models in dry climates (R2 = 0.84 for wheat and R2 = 0.9 for barley) than in humid climates (R2 = 0.69 for wheat and R2 = 0.66 for barley). Also, the MLR models estimated the yield of rainfed wheat (GPI = 1559.3) better than rainfed barely (GPI = 1536) in all climates.

In this study, the least square support vector machines (LS-SVM) method was used to predict the longitudinal dispersion coefficient (DL) in natural streams in comparison with the empirical equations in various datasets. To do this, three datasets of field data including hydraulic and geometrical characteristics of different rivers, with various statistical characteristics were applied to evaluate the performance of LS-SVM and 15 empirical equations. The LS-SVM was evaluated and compared with developed empirical equations using statistical indices of root mean square error (RMSE), standard error (SE), mean bias error (MBE), discrepancy ratio (DR), Nash-Sutcliffe efficiency (NSE) and coefficient of determination (R2). The results demonstrated that LS-SVM method has the high capability to predict the DL in different datasets with RMSE = 58–82 m2 s−1, SE = 24–39 m2 s−1, MBE = -1.95–2.6 m2 s−1, DR = 0.08–0.13, R2 = 0.76–0.88, and NSE = 0.75–0.87 as compared with previous empirical equations. It can be concluded that the proposed LS-SVM model can be successfully applied to predict the DL for a wide range of river characteristics.

Geo-synthetic materials are being used with acceptable performance in soil and water projects worldwide. Geotextiles are one of the categories of geo-synthetics being used in drainage systems. First generation of geotextiles used in the late 1950’s as an alternative for gravel envelopes. In this research two methods (multiple regression and fuzzy interference system) evaluate to predict synthetic envelope clogging. In multiple regression method the correlation coefficients for PP450, PP700 and PP900 are 62.66%, 79.37% and 90.62%, respectively and results of fuzzy interference system and decision tree showed that this method have high potential in comparison with multiple regression and values of total classification accuracy for PP450, PP700 and PP900 are 98.6%, 97.3% and 98% respectively. Then final results of this research showed fuzzy interference systems by using decision tree have high potential to predict clogging in envelops.