پیوندهای مفید
اخبار و اعلانات
آمار
| تعداد نشریات | 161 |
| تعداد شمارهها | 6,532 |
| تعداد مقالات | 70,504 |
| تعداد مشاهده مقاله | 124,123,816 |
| تعداد دریافت فایل اصل مقاله | 97,231,992 |
Hybrid Algorithm for early Detection of Water Pollution Impact on Environmental Indicators using Wavelet Techniques and RBF Neural Network Learning | ||
| Pollution | ||
| دوره 10، شماره 4، آذر 2024، صفحه 1074-1091 اصل مقاله (1.92 M) | ||
| نوع مقاله: Original Research Paper | ||
| شناسه دیجیتال (DOI): 10.22059/poll.2024.372064.2246 | ||
| نویسندگان | ||
| Monireh Khayat* 1؛ Rassoul Noorossana2؛ Paria Soleimani1؛ Sadigh Raissi1 | ||
| 1Industrial Engineering Department, South Tehran Branch, Islamic Azad University, P.O. Box: 15847-43311, Tehran, Iran | ||
| 2Industrial Engineering Department, Iran University of Science and Technology, P.O.Box: 13114-16846, Tehran, Iran | ||
| چکیده | ||
| The present study examines the impact of water pollution on the environment with the aim of detecting early abnormalities or significant changes in the water pollution indicators. A hybrid algorithm based on wavelet techniques and radial basis function neural network learning using high-frequency surrogate relation is introduced. Important qualitative indicators such as phosphate, nitrate, and chemical oxygen demand (COD) in the water bodies have uncertainties with variations such as dependence, and effectiveness of physical and chemical factors. In the first step, the high-frequency time series of the main TP index is obtained through the surrogate model and compared with GARCH techniques. By using the wavelet transform, the noise components of the time series are removed and pre-processed. In the next step, it is created by using the neural network to identify the main characteristics of water quality. In the last step, the contamination threshold is calculated based on the estimated base pattern for analyzing statistical patterns. The results show that the proposed algorithm has high stability and accuracy because using the surrogate technique has extracted a more accurate model of the behavior of the required water variables. It can be used to manage surface runoff in watersheds to preserve the environment and improve water quality. | ||
| کلیدواژهها | ||
| Water Pollution؛ Water Quality؛ Environment؛ Anomaly؛ Neural Network؛ Wavelet؛ Time-Frequency Series | ||
| مراجع | ||
|
Byrand, K. (2010). Nature and History in the Potomac Country: From Hunter–Gatherers to the Age of Jefferson.Journal of Historical Geography, 2(36), 233-234. Chen, H., Li, Q., & Zhu, F. (2022). A new class of integer-valued GARCH models for time series of bounded counts with extra-binomial variation. AStA Advances in Statistical Analysis, 106(2), 243-270. Chen, N., Tu, H., Duan, X., Hu, L., & Guo, C. (2023). Semisupervised anomaly detection of multivariate time series based on a variational autoencoder. Applied Intelligence, 53(5), 6074-6098. Christensen, V. G. (2001). Characterization of surface-water quality based on real-time monitoring and regression analysis, Quivira National Wildlife Refuge, south-central Kansas, December 1998 through June 2001 (No. 1-4248). US Department of the Interior, US Geological Survey. Donoho, D. L., & Johnstone, I. M. (1994, November). Threshold selection for wavelet shrinkage of noisy data. In Proceedings of 16th annual international conference of the IEEE engineering in medicine and biology society (Vol. 1, pp. A24-A25). IEEE. El-Shafeiy, E., Alsabaan, M., Ibrahem, M. I., & Elwahsh, H. (2023). Real-Time Anomaly Detection for Water Quality Sensor Monitoring Based on Multivariate Deep Learning Technique. Sensors, 23(20), 8613. Erkyihun, S. T., Rajagopalan, B., Zagona, E., Lall, U., & Nowak, K. (2016). Wavelet‐based time series bootstrap model for multidecadal streamflow simulation using climate indicators. Water Resources Research, 52(5), 4061-4077. Gokhale, M. Y., & Khanduja, D. K. (2010). Time domain signal analysis using wavelet packet decomposition approach. Int’l J. of Communications, Network and System Sciences, 3(03), 321. Jastram, J. D. (2014). Streamflow, water quality, and aquatic macroinvertebrates of selected streams in Fairfax County, Virginia, 2007-12 (No. 2014-5073). US Geological Survey. Jin, X. B., Gong, W. T., Kong, J. L., Bai, Y. T., & Su, T. L. (2022). PFVAE: a planar flow-based variational auto-encoder prediction model for time series data. Mathematics, 10(4), 610. Karasu, S., & Altan, A. (2022). Crude oil time series prediction model based on LSTM network with chaotic Henry gas solubility optimization. Energy, 242, 122964. Kirchgässner, G., Wolters, J., & Hassler, U. (2012). Introduction to modern time series analysis. Springer Science & Business Media. Kunz, J. V., Hensley, R., Brase, L., Borchardt, D., & Rode, M. (2017). High frequency measurements of reach scale nitrogen uptake in a fourth order river with contrasting hydromorphology and variable water chemistry (Weiße E lster, G ermany). Water Resources Research, 53(1), 328-343. Kuo, J. T., Wang, Y. Y., & Lung, W. S. (2006). A hybrid neural–genetic algorithm for reservoir water quality management. Water research, 40(7), 1367-1376. Lundgren, A., & Jung, D. (2022). Data-driven fault diagnosis analysis and open-set classification of time-series data. Control Engineering Practice, 121, 105006. Makridakis, S., & Hibon, M. (1997). ARMA models and the Box–Jenkins methodology. Journal of forecasting, 16(3),147-163. Naderian, D., Noori, R., Heggy, E., Bateni, S. M., Bhattarai, R., Nohegar, A., & Sharma, S. (2024). A water quality database for global lakes. Resources, Conservation and Recycling, 202, 107401. Noori, R., Ghiasi, B., Sheikhian, H., & Adamowski, J. F. (2017). Estimation of the dispersion coefficient in natural rivers using a granular computing model. Journal of Hydraulic Engineering, 143(5), 04017001. Noori, R., Mirchi, A., Hooshyaripor, F., Bhattarai, R., Haghighi, A. T., & Kløve, B. (2021). Reliability of functional forms for calculation of longitudinal dispersion coefficient in rivers. Science of The Total Environment, 791, 148394. Saghafi, B., Hassaniz, A., Noori, R., & Bustos, M. G. (2009). Artificial neural networks and regression analysis for predicting faulting in jointed concrete pavements considering base condition. International Journal of Pavement Research and Technology, 2(1), 20-25. Shi, B., Wang, P., Jiang, J., & Liu, R. (2018). Applying high-frequency surrogate measurements and a wavelet-ANN model to provide early warnings of rapid surface water quality anomalies. Science of the Total Environment, 610, 1390-1399. Shupe, S. M. (2017). High resolution stream water quality assessment in the Vancouver, British Columbia region: a citizen science study. Science of the Total Environment, 603, 745-759. Song, C., & Yao, L. (2022). Application of artificial intelligence based on synchrosqueezed wavelet transform and improved deep extreme learning machine in water quality prediction. Environmental Science and Pollution Research, 29(25), 38066-38082. Sun, Y., Babovic, V., & Chan, E. S. (2010). Multi-step-ahead model error prediction using time-delay neural networks combined with chaos theory. Journal of Hydrology, 395(1-2), 109-116. Talebi, M. (2023). Water Crisis in Iran and Its Security Consequences. Journal of Hydraulic Structures, 8(4), 17-28. Tan, W. Y., Lai, S. H., Teo, F. Y., & El-Shafie, A. (2022). State-of-the-Art Development of Two-Waves Artificial Intelligence Modeling Techniques for River Streamflow Forecasting. Archives of Computational Methods in Engineering, 29(7), 5185-5211. US EPA, (2017). National hydrography dataset high-resolution flowline data. The national map. https://www.data.gov/, Accessed date: 20 May 2017. USGS Surface-Water Data for the Nation, https://waterdata.usgs.gov/nwis/sw. Zhang, Y. F., & Thorburn, P. J. (2022). A deep surrogate model with spatio-temporal awareness for water quality sensor measurement. Expert Systems with Applications, 200, 116914. | ||
|
آمار تعداد مشاهده مقاله: 187 تعداد دریافت فایل اصل مقاله: 252 |
||