JWSS - Journal of Water and Soil Science

The prediction of local scouring as a dynamic and nonlinear phenomenon using methods of acceptable predictive capability has always been of interest to researchers. The shape of the bridge pier is one of the important factors in the formation and magnitude of the scour hole. In this paper, the scour depth of three bridge piers with cylindrical, sharp nose and rectangular shapes was predicted in two scenarios using the support vector machine algorithm with 395 field data obtained from the US Geological Survey and Froehlich (1988), based on different combinations of dimensionless parameters as the water attack angle (α), Froud number (Fr), the ration of pier length to width (l/b), and the ratio of mean sediment size to pier width (D50/b). The results of the study, while confirming the acceptable performance of the SVM algorithm for all piers in both scenarios, showed that in the first and second scenarios, the most optimal performance was related to the rectangular pier shape with correlation coefficient of 0.8702 and 0.8838, with and maximum Ds _(DDR) values of 0.854 and 1.229 respectively, during the testing phase. The positive effect of increasing the number of data on the performance of the SVM algorithm was also confirmed by further probing the evaluation indicators. The results of the comparison pointed out the overestimation of the predicted scour depth values of absolute error between 11% to 35%.

The effect of climate change on agricultural productivity and efficiency is a major concern and challenge for the agricultural industry. Different hydrometeorological variables, such as extreme temperature, precipitation, and their variations, affect the growth and yield of agricultural products. Saffron is one of the most important agricultural products in Iran. Iran produces the largest amount of Saffron globally, and Hamadan Province is one of the major saffron-producing regions in Iran. This study uses different Artificial Intelligence methods not only for clustering and sensitivity analysis of the hydroclimatological variables but also for evaluating the impacts of climate change on Saffron yield in Hamadan Province. Results indicated that the Random Forest algorithm performs the best for sensitivity analysis among all algorithms. Extreme climate change indices, particularly those related to the monthly maximum and minimum temperatures, have the highest negative impact on saffron yield compared to other hydroclimatological indices. Furthermore, the minimum temperature has a more significant negative impact on saffron yield compared to the maximum temperature. Additionally, the counties of Malayer, Nahavand, and Asadabad, located in the south and west of Hamadan Province, exhibited the highest accuracy in sensitivity analysis. The findings suggest that monthly extreme temperatures can be used to assess the risk of saffron production, increase agricultural productivity, and improve decision-making for the cultivation of this product.
 

Gully erosion is one of the most important types of water erosion. Since the amount of soil loss due to this erosion is directly related to environmental factors, the amount of soil loss due to each gully can be modeled based on environmental conditions. According to the high ability of machine learning models based on artificial intelligence to analyze environmental information, in addition to determining soil loss due to gully erosion, modeling has been carried out using two random forest models, and artificial neural networks and evaluating their efficiency in the Mahurmilati watershed located in the southwest of Fars province in this study. The dimensional parameters of 70 gullies were measured over four years (2021-2024), and the volume and weight of soil lost were calculated. 15 environmental factors were selected as predictive variables, and modeling was performed with a cross-validation approach using these two models, and the accuracy of the models was evaluated using quantitative criteria. The amount of soil loss in gullies during the study period was 15300.94 tons. The accuracy evaluation of the models showed that the random forest model had better performance based on the coefficient of determination (R2=0.66-0.73). Also, this model had the lowest value in terms of the RSR error index evaluation criterion (RSR=0.66-1.03) and the highest accuracy. In terms of the fit evaluation index (D), the random forest model also had the highest fit between the observational and forecast data and had the highest value of this index (D=0.83), and therefore, it was introduced as the superior model for predicting soil loss due to gully erosion in this watershed.