پیوندهای مفید
آمار
| تعداد نشریات | 21 |
| تعداد شمارهها | 361 |
| تعداد مقالات | 3,794 |
| تعداد مشاهده مقاله | 5,029,783 |
| تعداد دریافت فایل اصل مقاله | 3,377,521 |
The Impact of Snow Cover on River Discharge Simulation: Insights from the Barandozchay River Basin | ||
| Water Harvesting Research | ||
| دوره 8، شماره 1، 2025، صفحه 93-104 اصل مقاله (697.06 K) | ||
| نوع مقاله: Research Paper | ||
| شناسه دیجیتال (DOI): 10.22077/jwhr.2025.9094.1170 | ||
| نویسندگان | ||
| Haleh Hashemi1؛ Hossein Rezaie* 2؛ Keivan Khalili3؛ Amin Amini Rakan4 | ||
| 1MSc Student, Department of Water Engineering, Urmia University, Urmia, Iran. | ||
| 2Professor, Department of Water Engineering, Urmia University, Urmia, Iran. | ||
| 3Associate Professor, Urmia Lake Research Institute, Urmia University, Urmia, Iran. | ||
| 4Ph.D. Department of Water Engineering, Urmia University, Urmia, Iran. | ||
| چکیده | ||
| This study presents a comprehensive analysis aimed at predicting the discharge of the Barandozchay River using machine learning algorithms and meteorological data from both satellite and ground sources over the period from 2002 to 2022. The research highlights the significance of incorporating snow cover data in enhancing predictive accuracy, particularly during the spring and summer seasons. Utilizing Artificial Neural Networks (ANN), Support Vector Machines (SVM), and Random Forest (RF), the study evaluates various parameters affecting river discharge, including temperature, precipitation, and solar radiation. The results indicate that the Random Forest model outperforms the others in accuracy and generalization, while SVM demonstrates improved predictive capabilities with the inclusion of snow cover data. Specifically, the integration of snow cover data significantly enhanced the simulation accuracy of river discharge. The SVM model showed notable improvements in evaluation metrics, with R2 increasing from 0.64 to 0.72, MAE decreasing from 0.4 to 0.61, and RMSE reducing from 0.81 to 0.29 in the test data. Conversely, the RF model experienced an increase in error for the test data, but the correlation coefficient R2 improved from 0.85 to 0.88. The findings underscore the necessity of employing advanced machine learning techniques for water resource management, especially in regions facing water crises due to climate change. | ||
| کلیدواژهها | ||
| Neural Networks؛ Support Vector Machines؛ Random Forest؛ Snow؛ Baranduz-chayi River | ||
| مراجع | ||
|
Alizade Govarchin Ghale, Y., Altunkaynak, A., & Unal, A. (2018). Investigation anthropogenic impacts and climate factors on drying up of Urmia Lake using water budget and drought analysis. Water Resources Management, 32, 325-337.
Awchi, T. A. (2014). River discharges forecasting in northern Iraq using different ANN techniques. Water resources management, 28, 801-814.
Banihabib, M. E., Ahmadian, A., & Jamali, F. S. (2017). Hybrid DARIMA-NARX model for forecasting long-term daily inflow to Dez reservoir using the North Atlantic Oscillation (NAO) and rainfall data. GeoResJ, 13, 9-16.
Bărbulescu, A., & Zhen, L. (2024). Forecasting the River Water Discharge by Artificial Intelligence Methods. Water, 16(9), 1248.
Behnamian, A., Millard, K., Banks, S. N., White, L., Richardson, M., & Pasher, J. (2017). A systematic approach for variable selection with random forests: achieving stable variable importance values. IEEE Geoscience and Remote Sensing Letters, 14(11), 1988-1992.
Chau, K. W., Wu, C. L., & Li, Y. S. (2005). Comparison of several flood forecasting models in Yangtze River. Journal of Hydrologic Engineering, 10(6), 485-491.
Chaudhari, S., Felfelani, F., Shin, S., & Pokhrel, Y. (2018). Climate and anthropogenic contributions to the desiccation of the second largest saline lake in the twentieth century. Journal of Hydrology, 560, 342-353.
Genuer, R., Poggi, J. M., & Tuleau-Malot, C. (2010). Variable selection using random forests. Pattern recognition letters, 31(14), 2225-2236.
Hapfelmeier, A., & Ulm, K. (2013). A new variable selection approach using random forests. Computational Statistics & Data Analysis, 60, 50-69.
Hassanzadeh, E., Zarghami, M., & Hassanzadeh, Y. (2012). Determining the main factors in declining the Urmia Lake level by using system dynamics modeling. Water Resources Management, 26, 129-145.
Immerzeel, W. W., Droogers, P., De Jong, S. M., & Bierkens, M. F. P. (2009). Large-scale monitoring of snow cover and runoff simulation in Himalayan River basins using remote sensing. Remote sensing of Environment, 113(1), 40-49.
Kim, T., Yang, T., Gao, S., Zhang, L., Ding, Z., Wen, X., ... & Hong, Y. (2021). Can artificial intelligence and data-driven machine learning models match or even replace process-driven hydrologic models for streamflow simulation?: A case study of four watersheds with different hydro-climatic regions across the CONUS. Journal of Hydrology, 598, 126423.
Nayak, P. C., Sudheer, K. P., Rangan, D. M., & Ramasastri, K. S. (2005). Short‐term flood forecasting with a neurofuzzy model. Water Resources Research, 41(4).
Nguyen, D. H., Le, X. H., Anh, D. T., Kim, S. H., & Bae, D. H. (2022). Hourly streamflow forecasting using a Bayesian additive regression tree model hybridized with a genetic algorithm. Journal of Hydrology, 606, 127445.
Nijssen, B., Lettenmaier, D. P., Liang, X., Wetzel, S. W., & Wood, E. F. (1997). Streamflow simulation for continental- scale river basins. Water Resources Research, 33(4), 711-724
Panahi, F., Ehteram, M., Ahmed, A. N., Huang, Y. F., Mosavi, A., & El-Shafie, A. (2021). Streamflow prediction with large climate indices using several hybrids multilayer perceptrons and copula Bayesian model averaging. Ecological Indicators, 133, 108285.
Radman, A., Akhoondzadeh, M., & Hosseiny, B. (2022). Monitoring and predicting temporal changes of Urmia Lake and its basin using satellite multi-sensor data and deep-learning algorithms. PFG–Journal of Photogrammetry, Remote Sensing and Geoinformation Science, 90(3), 319-335.
Schulz, S., Darehshouri, S., Hassanzadeh, E., Tajrishy, M., & Schüth, C. (2020). Climate change or irrigated agriculture-what drives the water level decline of Lake Urmia. Scientific reports, 10(1), 236.
Shadkam, S., Ludwig, F., van Oel, P., Kirmit, Ç., & Kabat, P. (2016). Impacts of climate change and water resources development on the declining inflow into Iran's Urmia Lake. Journal of Great Lakes Research, 42(5), 942-952.
Wang, J., Wang, X., hui Lei, X., Wang, H., hua Zhang, X., jun You, J., ... & lian Liu, X. (2020). Teleconnection analysis of monthly streamflow using ensemble empirical mode decomposition. Journal of Hydrology, 582, 124411.
Wei, Yaxing, Huzaifa Bin Hashim, Sai Hin Lai, Kai Lun Chong, Yuk Feng Huang, Ali Najah Ahmed, Mohsen Sherif, and Ahmed El-Shafie. "Comparative analysis of artificial intelligence methods for streamflow forecasting." IEEe Access 12 (2024): 10865-10885.
Xie, H., Li, D., & Xiong, L. (2016). Exploring the regional variance using ARMA-GARCH models. Water Resources Management, 30, 3507-3518.
Yazdani, M. R., & Zolfaghari, A. A. (2017). Monthly river forecasting using instance-based learning methods and climatic parameters. Journal of Hydrologic Engineering, 22(6), 04017002. | ||
|
آمار تعداد مشاهده مقاله: 294 تعداد دریافت فایل اصل مقاله: 280 |
||
نماد الکترونیک
سامانه مدیریت نشریات علمی. قدرت گرفته از سیناوب