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Implementation of a Machine-Learning-Based Approach for Forecasting Watershed Stream Flow (Case Study: Chehel Chai Watershed, Iran) | ||
| Water Harvesting Research | ||
| دوره 6، شماره 2، آذر 2023، صفحه 213-225 اصل مقاله (1.81 M) | ||
| نوع مقاله: Case Study | ||
| شناسه دیجیتال (DOI): 10.22077/jwhr.2024.6969.1117 | ||
| نویسندگان | ||
| Hamed Sahranavard1؛ Mahdi Naseri* 2؛ Abolfazl Akbarpour3؛ Farshad Ahmadi4 | ||
| 1MSc Student of Water Resources Management, Department of Civil Engineering, University of Birjand, Birjand, Iran. | ||
| 2Assistant Professor, Department of Civil Engineering, University of Birjand, Birjand, Iran. | ||
| 3Professor, Department of Civil Engineering, University of Birjand, Birjand, Iran. | ||
| 4Assistant Professor, Department of Hydrology & Water Resources Engineering, Shahid Chamran University of Ahvaz, Ahvaz, Iran. | ||
| چکیده | ||
| The importance of the optimal and efficient use of all available water resources becomes noticeable when today due to successive droughts and a decrease in rainfall, the surface water resources are running out. Runoff and surface water resources are some of the primary and vital available water resources, and hence, modeling and predicting their behavior are especially critical. In the current research, the aim was to model the stream flow of the Chehel Chai watershed in Golestan province, Iran, using the data of the stream flow and precipitation for a period of 45 years. For this reason, 4 machine learning algorithms namely, Extreme Learning Machine (ELM), Random Forest (RF), Gaussian Process Regression (GPR), and Gene Expression Programming (GEP) were used. The data were entered into the modeling in the form of different scenarios consisting of the stream flow and precipitation with varying lags of time. The results showed that scenario M2 (using only stream flow data with two time lags) in the ELM (extreme learning machine) model with the values of RMSE (root mean square error) =0.984 (m3/s) and R2=0.613 had the most accurate performance and predictions among all the models and scenarios. | ||
| کلیدواژهها | ||
| Forecasting؛ Machine learning؛ Modeling؛ Stream flow | ||
| مراجع | ||
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Abudu, S., Cui, C. L., King, J. P., & Abudukadeer, K. (2010). Comparison of performance of statistical models in forecasting monthly stream flow of Kizil River, China. Water Science and Engineering, 3(3), 269-281.
Adnan, R. M., Dai, H. L., Mostafa, R. R., Islam, A. R. M. T., Kisi, O., Elbeltagi, A., & Zounemat-Kermani, M. (2023). Application of novel binary optimized machine learning models for monthly streamflow prediction. Applied Water Science, 13(5), 110.
Adnan, R. M., Liang, Z., Kuriqi, A., Kisi, O., Malik, A., & Li, B. (2020, October). Stream flow forecasting using heuristic machine learning methods. In 2020 2nd International Conference on Computer and Information Sciences (ICCIS) (pp. 1-6). IEEE.
Ahmadi, F., Mehdizadeh, S., & Nourani, V. (2022). Improving the performance of random forest for estimating monthly reservoir inflow via complete ensemble empirical mode decomposition and wavelet analysis. Stochastic Environmental Research and Risk Assessment, 36(9), 2753-2768.
Asefa, T., Kemblowski, M., McKee, M., & Khalil, A. (2006). Multi-time scale stream flow predictions: The support vector machines approach. Journal of hydrology, 318(1-4), 7-16.
Breiman, L. (2001). Random forests. Machine learning, 45, 5-32.
Cheng, M., Fang, F., Kinouchi, T., Navon, I. M., & Pain, C. C. (2020). Long lead-time daily and monthly stream flow forecasting using machine learning methods. Journal of Hydrology, 590, 125376.
Essam, Y., Huang, Y. F., Ng, J. L., Birima, A. H., Ahmed, A. N., & El-Shafie, A. (2022). Predicting streamflow in Peninsular Malaysia using support vector machine and deep learning algorithms. Scientific Reports, 12(1), 3883.
Ferreira, C. (2001). Gene expression programming: a new adaptive algorithm for solving problems. arXiv preprint cs/0102027.
Garsole, P., & Rajurkar, M. (2015). Stream flow forecasting by using support vector regression. In Proc., 20th Int. Conf. of Hydraulics, Water Resources and River Engineering.
Guo, J., Zhou, J., Qin, H., Zou, Q., & Li, Q. (2011). Monthly stream flow forecasting based on improved support vector machine model. Expert Systems with Applications, 38(10), 13073-13081.
Hong, W. C. (2008). Rainfall forecasting by technological machine learning models. Applied Mathematics and Computation, 200(1), 41-57.
Huang, G. B., Zhu, Q. Y., & Siew, C. K. (2006). Extreme learning machine: theory and applications. Neurocomputing, 70(1-3), 489-501.
Islam, K. I., Elias, E., Carroll, K. C., & Brown, C. (2023). Exploring random forest machine learning and remote sensing data for streamflow prediction: An alternative approach to a process-based hydrologic modeling in a snowmelt-driven watershed. Remote Sensing, 15(16), 3999.
Kalra, A., & Ahmad, S. (2009). Using oceanic‐atmospheric oscillations for long lead time stream flow forecasting. Water Resources Research, 45(3), 233-242.
Khan, M. A., Farooq, F., Javed, M. F., Zafar, A., Ostrowski, K. A., Aslam, F., ... & Maślak, M. (2021). Simulation of depth of wear of eco-friendly concrete using machine learning based computational approaches. Materials, 15(1), 58-70.
Kişi, Ö. (2007). Stream flow forecasting using different artificial neural network algorithms. Journal of Hydrologic Engineering, 12(5), 532-539.
Kumar, V., Kedam, N., Sharma, K. V., Mehta, D. J., & Caloiero, T. (2023). Advanced machine learning techniques to improve hydrological prediction: A comparative analysis of streamflow prediction models. Water, 15(14), 2572.
Li, P. H., Kwon, H. H., Sun, L., Lall, U., & Kao, J. J. (2010). A modified support vector machine based prediction model on stream flow at the Shihmen Reservoir, Taiwan. International Journal of Climatology, 30(8), 1256-1268.
Luna, I., Soares, S., Magalhaes, M. H., & Ballini, R. (2005, July). Stream flow forecasting using neural networks and fuzzy clustering techniques. In Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005. (Vol. 4, pp. 2631-2636). IEEE.
Miche, Y., Sorjamaa, A., & Lendasse, A. (2008, September). OP-ELM: theory, experiments and a toolbox. In International conference on Artificial Neural networks (pp. 145-154). Berlin, Heidelberg: Springer Berlin Heidelberg.
Orellana-Alvear, J., Célleri, R., Rollenbeck, R., Muñoz, P., Contreras, P., & Bendix, J. (2020). Assessment of native radar reflectivity and radar rainfall estimates for discharge forecasting in mountain catchments with a random forest model. Remote Sensing, 12(12), 1986.
Pustokhina, I., Seraj, A., Hafsan, H., Mostafavi, S. M., & Alizadeh, S. M. (2021). Developing a robust model based on the gaussian process regression approach to predict biodiesel properties. International Journal of Chemical Engineering, 2021, 1-12.
Rasouli, K., Hsieh, W. W., & Cannon, A. J. (2010). Short lead-time stream flow forecasting by machine learning methods, with climate variability incorporated. In World Environmental and Water Resources Congress 2010: Challenges of Change (pp. 4608-4619).
Rasouli, K., Hsieh, W. W., & Cannon, A. J. (2012). Daily stream flow forecasting by machine learning methods with weather and climate inputs. Journal of Hydrology, 414, 284-293.
Sahranavard, H., & Naseri, M. (2022). Investigating the effect of observational data length on ARIMA modeling and forecasting accuracy: a case study of Kortian Stream watershed, Iran. Water Supply, 22(11), 8253-8265.
Schulz, E., Speekenbrink, M., & Krause, A. (2018). A tutorial on Gaussian process regression: Modelling, exploring, and exploiting functions. Journal of Mathematical Psychology, 85, 1-16.
She, N., & Basketfield, D. (2005). Long range forecast of stream flow using support vector machine. In Impacts of global climate change, 1-9.
Shiri, J., & Kisi, O. (2010). Short-term and long-term stream flow forecasting using a wavelet and neuro-fuzzy conjunction model. Journal of Hydrology, 394(3-4), 486-493.
Singh, A. K., Kumar, P., Ali, R., Al-Ansari, N., Vishwakarma, D. K., Kushwaha, K. S., ... & Heddam, S. (2022). An integrated statistical-machine learning approach for runoff prediction. Sustainability, 14(13), 8209.
Sun, A. Y., Wang, D., & Xu, X. (2014). Monthly stream flow forecasting using Gaussian process regression. Journal of Hydrology, 511, 72-81.
Tongal, H., & Booij, M. J. (2018). Simulation and forecasting of streamflows using machine learning models coupled with base flow separation. Journal of hydrology, 564, 266-282.
Tosunoğlu, F., HANAY, S., Çintaş, E., & Özyer, B. (2020). Monthly stream flow forecasting using machine learning. Erzincan University Journal of Science and Technology, 13(3), 1242-1251.
Tyralis, H., Papacharalampous, G., & Langousis, A. (2021). Super ensemble learning for daily stream flow forecasting: Large-scale demonstration and comparison with multiple machine learning algorithms. Neural Computing and Applications, 33(8), 3053-3068.
Were, K., Bui, D. T., Dick, Ø. B., & Singh, B. R. (2015). A comparative assessment of support vector regression, artificial neural networks, and random forests for predicting and mapping soil organic carbon stocks across an Afromontane landscape. Ecological Indicators, 52, 394-403.
Zakaria, Z. A., & Shabri, A. (2012). Stream flow forecasting at ungaged sites using support vector machines. Applied Mathematical Sciences, 6(60), 3003-3014. | ||
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