پیوندهای مفید
آمار
| تعداد نشریات | 21 |
| تعداد شمارهها | 370 |
| تعداد مقالات | 3,873 |
| تعداد مشاهده مقاله | 5,218,975 |
| تعداد دریافت فایل اصل مقاله | 3,499,748 |
Estimating Total Sediment Load Using Water Quality Parameters in the Sufi Chay River, Iran: A Comparative Analysis of Soft Computing and Dimensionless Approaches | ||
| Water Harvesting Research | ||
| دوره 8، شماره 1، 2025، صفحه 1-15 اصل مقاله (842.64 K) | ||
| نوع مقاله: Research Paper | ||
| شناسه دیجیتال (DOI): 10.22077/jwhr.2025.8174.1154 | ||
| نویسنده | ||
| Jafar Chabokpour* | ||
| Associate Professor, Department of Civil Engineering, University of Maragheh, Maragheh, Iran. | ||
| چکیده | ||
| The sediment transport and the relation to water quality parameters and hydrological characteristics in the Sufi Chay River in Iran were investigated in this study using long-term monitoring data. Traditional statistical methods, dimensionless parameter analysis, and advanced soft computing techniques are combined within the scope of the presented comprehensive analysis. Total sediment load is the dependent variable, while the independent variables include flow rate, total dissolved solids (TDS), electrical conductivity (EC), pH, total anions, total cations, anion hardness, and cation hardness. Strong correlations were observed between total sediment load and flow rate (r = 0.82), total dissolved solids (r = 0.68), and electrical conductivity (r = 0.65). The dimensionless equation developed related sediment concentration to Reynolds number, Froude number, and normalized water quality parameters. The performance was quite good as revealed by the R2 value of 0.82. Comparison of performances using three soft computing methods, namely Artificial Neural Networks, Adaptive Neuro-Fuzzy Inference System, and Support Vector Regression, are performed. The highest R2 value of 0.91 and RMSE of 53.2 tons/ day were obtained with ANFIS model. Sensitivity analyses indicated that flow rate and TDS were the most sensitive parameters to predict total sediment load. Generally, a seasonal variability in sediment transport, showing that the maximum discharges happened in the spring season with the mean of 187.3 tons/day, while the minimum discharges happened in the summer season with the mean of 42.8 tons/day. Besides, a nonlinear relationship between flow rate and both sediment concentration and discharge in this catchment reflects a complex erosion and transport process. The investigation also resulted in some important ion-parameter relationships, which are indicative of the geochemical factors operating on the water quality and sediment activity. | ||
| کلیدواژهها | ||
| Total sediment load؛ Water quality؛ Soft computing؛ Dimensionless analysis؛ River management | ||
| مراجع | ||
Ashraf, M. A., Maah, M. J., Yusoff, I., Wajid, A., & Mahmood, K. (2011). Sand mining effects, causes and concerns: A case study from Bestari Jaya, Selangor, Peninsular Malaysia. Scientific Research and Essays, 6(6), 1216-1231.
Aspray, K. L., Holden, J., Ledger, M. E., Mainstone, C. P., & Brown, L. E. (2017). Organic sediment pulses impact rivers across multiple levels of ecological organization. Ecohydrology, 10(6), e1855.
Bartley, R., Speirs, W. J., Ellis, T. W., & Waters, D. K. (2012). A review of sediment and nutrient concentration data from Australia for use in catchment water quality models. Marine pollution bulletin, 65(4-9), 101-116.
Bayram, A., Kankal, M., Tayfur, G., & Önsoy, H. (2014). Prediction of suspended sediment concentration from water quality variables. Neural Computing and Applications, 24, 1079-1087.
Beeson, P. C., Sadeghi, A. M., Lang, M. W., Tomer, M. D., & Daughtry, C. S. (2014). Sediment delivery estimates in water quality models altered by resolution and source of topographic data. Journal of Environmental Quality, 43(1), 26-36.
Chabokpour, J. (2024a). Comparative Analysis of Transfer Function Method with Advanced Flood Prediction Techniques. Water Harvesting Research. 7(2), 194-209
Chabokpour, J. (2024b). Experimental study of contaminant mixing through the buried river junctions. Journal of Hydraulic Structures, 10(3), 18-33.
Chabokpour, J., & Azamathulla, H. M. (2022). Numerical simulation of pollution transport and hydrodynamic characteristics through the river confluence using FLOW 3D. Water Supply, 22(10), 7821-7832.
Chabokpour, J., & Raji, M. (2024). Predicting Morphological Changes in Rivers Using Image Processing (Case Study: Qizil Ouzan River). Journal of Hydraulic Structures, 10(2).
Chahinian, N., Tournoud, M.-G., Perrin, J.-L., & Picot, B. (2011). Flow and nutrient transport in intermittent rivers: a modelling case-study on the Vène River using SWAT 2005. Hydrological Sciences Journal–Journal Des Sciences Hydrologiques, 56(2), 268-287.
Chalov, S., Golosov, V., Tsyplenkov, A., Theuring, P., Zakerinejad, R., Märker, M., & Samokhin, M. (2017). A toolbox for sediment budget research in small catchments. Geography, Environment, Sustainability, 10(4), 43-68.
Cheung, K., Poon, B., Lan, C., & Wong, M. H. (2003). Assessment of metal and nutrient concentrations in river water and sediment collected from the cities in the Pearl River Delta, South China. Chemosphere, 52(9), 1431-1440.
CIĞIZOĞLU, H. K. (2002). Suspended sediment estimation for rivers using artificial neural networks and sediment rating curves. Turkish Journal of Engineering and Environmental Sciences, 26(1), 27-36.
Fagundes, H. d. O., Fan, F. M., & Paiva, R. C. D. d. (2019). Automatic calibration of a large-scale sediment model using suspended sediment concentration, water quality, and remote sensing data. Rbrh, 24, e26.
Gazzaz, N. M., Yusoff, M. K., Aris, A. Z., Juahir, H., & Ramli, M. F. (2012). Artificial neural network modeling of the water quality index for Kinta River (Malaysia) using water quality variables as predictors. Marine pollution bulletin, 64(11), 2409-2420.
Gholizadeh, M. H., Melesse, A. M., & Reddi, L. (2016). A comprehensive review on water quality parameters estimation using remote sensing techniques. Sensors, 16(8), 1298.
Guojiao, L., Baohui, M., & Lehao, W. (2023). Water Quality Assessment of Wenyu River with Variable Weight Cloud Model. Nature Environment & Pollution Technology, 22(1).
Horowitz, A. J. (1984). A primer on trace metal-sediment chemistry (2331-1258).
Hosseini, N., Johnston, J., & Lindenschmidt, K. (2017). Impacts of climate change on the water quality of a regulated prairie river. Water, 9 (3), 199. In.
Lindenschmidt, K.-E., Poser, K., & Rode, M. (2005). Impact of morphological parameters on water quality variables of a regulated lowland river. Water Science and Technology, 52(6), 187-193.
Lou, H., Zhang, Y., Yang, S., Wang, X., Pan, Z., & Luo, Y. (2022). A new method for long-term river discharge estimation of small-and medium-scale Rivers by using multisource remote sensing and RSHS: Application and validation. Remote Sensing, 14(8), 1798.
Lundy, L., Alves, L., Revitt, M., & Wildeboer, D. (2017). Metal water-sediment interactions and impacts on an urban ecosystem. International journal of environmental research and public health, 14(7), 722.
Mohsen, A., Kovács, F., & Kiss, T. (2022). Remote Sensing of Sediment load in Rivers Using Sentinel-2 Images and Machine-Learning Algorithms. Hydrology, 9, 88. In.
Monjardin, C. E. F., Gomez, R. A., Cruz, M. N. G. D., Capili, D. L. R., Tan, F. J., & Uy, F. A. A. (2021). Sediment transport and water quality analyses of Naic River, Cavite, Philippines. 2021 IEEE Conference on Technologies for Sustainability (SusTech),
Munyai, L. F., Malungani, A., Ikudayisi, A., & Mutoti, M. I. (2024). The drivers of benthic macroinvertebrates communities along a subtropical river system: Sediments chemistry or water quality? Ecohydrology, e2649.
Othman, F., Alaaeldin, M., Seyam, M., Ahmed, A. N., Teo, F. Y., Ming Fai, C., Afan, H. A., Sherif, M., Sefelnasr, A., & El-Shafie, A. (2020). Efficient river water quality index prediction considering minimal number of inputs variables. Engineering Applications of Computational Fluid Mechanics, 14(1), 751-763.
Park, Y. S., & Engel, B. A. (2016). Identifying the correlation between water quality data and LOADEST model behavior in annual sediment load estimations. Water, 8(9), 368.
Saranga, B., Premarathne, U., & Atapattu, B. (2021). Stream Sediment Rate Estimation using Optical Sensing. 2021 IEEE Industrial Electronics and Applications Conference (IEACon),
Saraswati, S. P., Ardion, M. V., Widodo, Y. H., & Hadisusanto, S. (2019). Water quality index performance for river pollution control based on better ecological point of view (a case study in Code, Winongo, Gadjah Wong streams). Journal of the Civil Engineering Forum,
Smith, I. M., Hall, K. J., Lavkulich, L. M., & Schreier, H. (2007). Trace Metal Concentrations in an Intensive Agricultural Watershed in British Columbia, Canada 1. JAWRA Journal of the American Water Resources Association, 43(6), 1455-1467.
Tyler, A., Svab, E., Preston, T., Présing, M., & Kovács, W. (2006). Remote sensing of the water quality of shallow lakes: A mixture modelling approach to quantifying phytoplankton in water characterized by high‐suspended sediment. International Journal of Remote Sensing, 27(8), 1521-1537.
Warrick, J. A. (2015). Trend analyses with river sediment rating curves. Hydrological Processes, 29(6), 936-949. | ||
|
آمار تعداد مشاهده مقاله: 430 تعداد دریافت فایل اصل مقاله: 498 |
||
نماد الکترونیک
سامانه مدیریت نشریات علمی. قدرت گرفته از سیناوب