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Evaluation of Hybrid Metaheuristic Models in Estimating Electrical Conductivity (Case Study: Kakarza River, Lorestan Province) | ||
| مجله پژوهش های خشکسالی و تغییراقلیم | ||
| مقاله 6، دوره 3، شماره 2 - شماره پیاپی 10، شهریور 1404، صفحه 69-86 اصل مقاله (1.35 M) | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22077/jdcr.2025.9196.1135 | ||
| نویسندگان | ||
| ابراهیم نوحانی1؛ حمیدرضا باباعلی* 2؛ رضا دهقانی3 | ||
| 1استادیارگروه عمران، مرکز تحقیقات مواد و انرژی، واحد دزفول، دانشگاه آزاد اسلامی، دزفول، ایران | ||
| 2دانشیار، گروه مهندسی عمران، دانشگاه آزاد اسلامی واحد خرم آباد، خرم آباد، ایران | ||
| 3بخش تحقیقات حفاظت خاک و آبخیزداری، مرکز تحقیقات و آموزش کشاورزی و منابع طبیعی استان لرستان، خرم آباد، ایران. | ||
| چکیده | ||
| Electrical conductivity (EC) is an important indicator for monitoring water quality in rivers. Electrical conductivity is inherently related to the concentration of dissolved ionic compounds present in aquatic environments, including various salts and minerals. Estimating electrical conductivity is crucial for environmental monitoring and assessing the overall health of aquatic ecosystems. In this research, a hybrid intelligent model based on the Support Vector Regression (SVR) approach was developed to estimate the electrical conductivity of river water. For this purpose, three optimization algorithms, including Wavelet, Whale, and Particle Swarm Optimization (PSO), were used to model the electrical conductivity of the river flow. For modeling, statistics and information from the Kakarza River hydrometric station, located in Lorestan province, were used as a case study in 7 combined scenarios of input parameters for the years 2003-2023. To evaluate the performance of the models, the evaluation criteria of correlation coefficient, root mean square error, mean absolute error, and Nash-Sutcliffe coefficient were used. The results showed that combined scenarios in the models under investigation improve the model performance. Furthermore, the results obtained from the evaluation criteria showed that the Support Vector Regression-Wavelet model has a correlation coefficient of 0.980, a root mean square error of 0.344 (ppm), a mean absolute error of 0.172 (ppm), and a Nash-Sutcliffe coefficient of 0.985 in the validation phase. Overall, the results showed that the use of intelligent models based on the support vector regression approach can be an effective approach in river engineering sustainability. | ||
| کلیدواژهها | ||
| Kakarza River؛ Water Quality؛ Electrical Conductivity؛ Wavelet | ||
| مراجع | ||
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