JWSS - Journal of Water and Soil Science

Nowadays, human interferences in the natural resources cause the loss of these resources and lead to destructive floods, soil erosion and other various environmental, economic and social damages. Furthermore, increasing growth of population and climate change intensify the destructions. Thus management and planning through land use optimization is essential for the proper utilization, protection and revival of these resources. The purpose of this study is to couple the fuzzy goal programming and multi objective land allocation optimization approaches to develop a model for watershed management and planning in Chelgerd watershed. The proposed model is based on optimum area determination in various land uses and also their optimum local situation. In this research, a fuzzy model has been proposed. In this model, minimizing the amount of soil erosion and maximizing the amount of profit are priorities, respectively. Moreover, production resources including water and land as well as economic and social problems are limitations of the mentioned model. Results obtained show that the proposed model is an efficient model in land use optimization and sustainable area development and can increase profit to 37% and decrease sedimentation to 2.4%, respectively.

The river flow prediction is a key aspect of hydrology that plays a significant role in water resources management, flood risk reduction, and agricultural planning. This study simulates the monthly flow of the Razavar River, located in western Iran, using an extreme learning machine (ELM) model enhanced by the Whale (WOA) Optimization Algorithm and Grasshopper Optimization Algorithm (GOA) metaheuristic optimization algorithms. The data used include river flow, precipitation, evaporation, and temperature, which were collected for 10 years with a monthly time step and normalized in the numerical range of zero to one. 80% of the data is used for training, and the remaining 20% for model evaluation. The performance of the models is measured with the statistical indices RMSE, NSE, and R². First, the basic ELM model is developed using the trial-and-error method to adjust the weights between the hidden and output layers. Then, the WOA and GOA algorithms are used to optimize the weights. The results show that the basic ELM model performs worse than the optimized models (Train: RMSE=0.1427, NSE=0.7795, R²=0.7911, Test: RMSE=0.1406, NSE=0.7811, R2=0.7916). While the WOA-ELM and GOA-ELM models provide similar results, the WOA-ELM model shows better performance in complex conditions (Train: RMSE=0.1215, NSE=0.7869, R2=0.7932, Test: RMSE=0.1165, NSE=0.7872, R2=0.7933). The results of this research show that meta-heuristic optimization algorithms play an important role in improving the performance of river flow prediction models due to their ability to search comprehensively and avoid getting stuck in local optima. The findings of this study emphasize the importance of applying these techniques in water resources management and sustainable planning and will pave the way for future research in this area.