پژوهش های کاربردی مهندسی آب

پژوهش های کاربردی مهندسی آب

کاربرد روش برنامه ریزی بیان ژن در تخمین شاخص کیفیت آب آنتروپی شانون

نوع مقاله : مقاله پژوهشی

نویسندگان
گروه مهندسی محیط زیست، دانشکده مهندسی آب و محیط‌زیست، دانشگاه شهید چمران اهواز، ایران.
چکیده
کیفیت آب به دلیل اهمیت حیاتی آن در حفظ زندگی و پشتیبانی از جنبه‌های مختلف، یکی از موضوعات کلیدی در مدیریت منابع آبی محسوب می‌شود. با پیشرفت صنعت و افزایش نیاز به آب، آلودگی منابع آبی به یک نگرانی جدی تبدیل شده است. این تحقیق به بررسی کیفیت آب رودخانه زهره با استفاده از شاخص کیفیت آب آنتروپی شانون و مدل برنامه‌ریزی بیان ژن (GEP) پرداخته است. داده‌های مربوط به پارامترهای کیفیت آب، شامل pH، هدایت الکتریکی، فسفات، نیترات، آمونیوم، سختی کل، کلیفرم مدفوعی، اکسیژن محلول، BOD5،COD و کدورت، طی دوره ده ‌ساله (1390-1400) از ایستگاه‌های فیروزآباد و 720 متری سویره تهیه شدند. ابتدا، آنتروپی هر پارامتر محاسبه و وزن‌های آن-ها تعیین شد. سپس، با استفاده از وزن‌های محاسبه‌شده، شاخص کیفیت آب آنتروپی شانون به دست آمد. نتایج حاصل از مدل GEP نشان داد که این روش با ضریب تعیین (R²) برابر 803/0 و ریشه میانگین مربعات خطا (RMSE) برابر 07/0، دقت بالایی در تخمین شاخص کیفیت آب دارد. نتایج این مطالعه به استفاده از GEP به عنوان ابزاری مؤثر برای ارزیابی و مدیریت کیفیت آب در رودخانه زهره اشاره دارد و می‌تواند به بهبود سیاست‌های مدیریتی منابع آبی و پیشگیری از خطرات برای سلامت عمومی و اکوسیستم‌ها کمک کند.
کلیدواژه‌ها

موضوعات


عنوان مقاله English

Application of Gene Expression Programming Method in Estimation of Shannon Entropy Water Quality Index

نویسندگان English

Amir hosein Shakarami
Laleh Divband Hafshejani
Parvaneh Tishehzan
Hamid Abdolabadi
Department of Environmental Engineering, Faculty of Water and Environmental Engineering, Shahid Chamran University of Ahvaz, Ahvaz, Iran.
چکیده English

Water quality, due to its vital role in sustaining life and supporting various aspects of human life, is a key issue in water resource management. With industrial advancements and increasing water demands, water pollution has become a serious concern. This research investigates the water quality of the Zohreh River using the Shannon Entropy Water Quality Index (SEWQI) and Gene Expression Programming (GEP) model. Data on water quality parameters, including pH, electrical conductivity, phosphate, nitrate, ammonium, total hardness, fecal coliform, dissolved oxygen, BOD5, COD, and turbidity, were collected from the Firoozabad and 720-meter Suire stations over a ten-year period (2011-2020). Firstly, the entropy of each parameter was calculated, and their weights were determined. Then, using the calculated weights, the Shannon Entropy Water Quality Index was obtained. The results obtained from the GEP model showed that this method has a high accuracy in predicting the water quality index with a coefficient of determination (R²) of 0.803 and root mean square error (RMSE) of 0.07. The results of this study highlight the use of GEP as an effective tool for assessing and managing water quality in the Zohreh River and can contribute to improving water resource management policies and preventing risks to public health and ecosystems.
Firstly, the entropy of each parameter was calculated, and their weights were determined. Then, using the calculated weights, the Shannon Entropy Water Quality Index was obtained. The results obtained from the GEP model showed that this method has a high accuracy in predicting the water quality index with a coefficient of determination (R²) of 0.803 and root mean square error (RMSE) of 0.07. The results of this study highlight the use of GEP as an effective tool for assessing and managing water quality in the Zohreh River and can contribute to improving water resource management policies and preventing risks to public health and ecosystems.

کلیدواژه‌ها English

Artificial intelligence
Firozabad
Optimization
Pearson correlation coefficient
Aldrees et al., 2022)., L. N. T., Minh, D. N., Quang, T. P., Mong, T. V. T., Le Xuan, T., Son, H. P., & Van, P. N. (2020). Assessment of nitrogen nutrient sources in aquatic environment of Tuyen Lam sub-catchment based on it’s stable isotopes ratio (δ¹⁵N-NO3) combined with geochemical parameters. Nuclear Science and Technology10(2), 32-43.
Aldrees, A., Khan, M. A., Tariq, M. A. U. R., Mustafa Mohamed, A., Ng, A. W. M., & Bakheit Taha, A. T. (2022). Multi-expression programming (MEP): water quality assessment using water quality indices. Water14(6), 947.
Cruz, F. M., & Silva, T. F. D. G. (2024). Water quality emergency monitoring networks: A method for identifying non-critical variables based on Shannon's entropy. Journal of Hydroinformatics, 26(3), 658-682.
Divband Hafshejani, L., Naseri, A. A., Moradzadeh, M., Daneshvar, E., & Bhatnagar, A. (2022). Applications of soft computing techniques for prediction of pollutant removal by environmentally friendly adsorbents (case study: the nitrate adsorption on modified hydrochar). Water Science & Technology, 86(5), 1066-1082.
Ferreira, C. (2001). Gene expression programming: a new adaptive algorithm for solving problems. arXiv preprint cs/0102027.
Goodarzi, M. R., Niknam, A. R. R., Barzkar, A., Niazkar, M., Zare Mehrjerdi, Y., Abedi, M. J., & Heydari Pour, M. (2023). Water quality index estimations using machine learning algorithms: a case study of Yazd-Ardakan Plain, Iran. Water, 15(10), 1876.
Hojjati-Najafabadi, A., Mansoorianfar, M., Liang, T., Shahin, K., & Karimi-Maleh, H. (2022). A review on magnetic sensors for monitoring of hazardous pollutants in water resources. Science of the Total Environment, 824, 153844.
Khaire, U. M., & Dhanalakshmi, R. (2022). Stability of feature selection algorithm: A review. Journal of King Saud University-Computer and Information Sciences, 34(4), 1060-1073.
Maddah, H. A. (2022). Predicting optimum dilution factors for BOD sampling and desired dissolved oxygen for controlling organic contamination in various wastewaters. International Journal of Chemical Engineering2022(1), 8637064.
Mohammadpour, R., Shaharuddin, S., Zakaria, N. A., Ghani, A. A., Vakili, M., & Chan, N. W. (2016). Prediction of water quality index in free surface constructed wetlands. Environmental Earth Sciences, 75, 1-12.
Nordin, N. F. C., Mohd, N. S., Koting, S., Ismail, Z., Sherif, M., & El-Shafie, A. (2021). Groundwater quality forecasting modelling using artificial intelligence: A review. Groundwater for Sustainable Development, 14, 100643.
Pang, T., Jiang, J., Alfonso, L., Yang, R., Zheng, Y., Wang, P., & Zheng, T. (2023). Deriving analytical expressions of the spatial information entropy index on riverine water quality dynamics. Journal of Hydrology, 623, 129806.
Sarma, A. (2023, February). An Analysis on the Techniques for Water Quality Prediction from Remotely Sensed data. In 2023 International Conference on Recent Trends in Electronics and Communication (ICRTEC) (pp. 1-6). IEEE.
Sarma, A. (2023, February). An Analysis on the Techniques for Water Quality Prediction from Remotely Sensed data. In 2023 International Conference on Recent Trends in Electronics and Communication (ICRTEC) (pp. 1-6). IEEE.
Shah, M. I., Javed, M. F., & Abunama, T. (2021). Proposed formulation of surface water quality and modelling using gene expression, machine learning, and regression techniques. Environmental Science and Pollution Research28, 13202-13220.
Shahinejad, B., & Dehgani, R. (2018). Comparison of wavelet neural network models, support vector machine and gene expression programming in estimating the amount of oxygen dissolved in rivers. Iran Water Resources Research, 14(3), 226-238.
Singh, K. R., Dutta, R., Kalamdhad, A. S., & Kumar, B. (2022). Study of physicochemical parameters and wetland water quality assessment by using Shannon’s entropy. Applied Water Science, 12(11), 247.
Zheng, K. G. Q. L. J. Z. J. X. W. M. (2017). Applications of support vector machine and improved k-Nearest Neighbor algorithm in fault diagnosis and fault degree evaluation of gas insulated switchgear. 1st International Conference on Electrical Materials and Power Equipment, Xian, Peppler R China.
 
 
 
دوره 2، شماره 1
تیر 1403
صفحه 149-158

  • تاریخ دریافت 30 مهر 1403
  • تاریخ بازنگری 22 آبان 1403
  • تاریخ پذیرش 24 آبان 1403