مدل هیبریدی SNIP-ANN : رویکردی نوین جهت پیش‌بینی دقیق دبی جریان رودخانه

References

Abdoulaye, O. O., & Lu, H. (2021). Future irrigation water requirements of the main crops cultivated in the Niger River Basin. Atmosphere. doi: 10.3390/atmos12040439.

Adnan, R., Yuan, X., et al. (2017). Improving Accuracy of River Flow Forecasting Using LSSVR with Gravitational Search Algorithm, Advances in Meteorology, 2017, 1-23. doi: 10.1155/2017/2391621

Aichouri, I., Hani, A., Bougherira, N., Djabri, L., Chaffai, H., & Lallahem, S. (2015). River flow model using artificial neural networks. Energy Procedia, 74, 1007–1014. doi: 10.1016/j.egypro.2015.07.785

Albared, M., Beise, H. P., & Stuber, M. (2024). Deep learning models for small rivers stream-flow forecasting and flood prediction. Research Square (Preprint, Version 1).

Armstrong, J. S. (2001). Evaluating forecasting methods. In Principles of forecasting: A handbook for researchers and practitioners (pp. 443–472).

Araujo, P., Astray, G., Ferrerio-Lage, J. A., Mejuto, J. C., Rodríguez-Suárez, J. A., & Soto, B. (2011). Multilayer perceptron neural network for flow prediction. Journal of Environmental Monitoring, 13(1), 35–41. doi: 10.1039/C0EM00478B

Arbic II, P. R., & Bukshtynov, V. (2024). Efficient gradient-based optimization for reconstructing binary images in applications to electrical impedance tomography. Computational Optimization and Applications, 88(1), 379–403. doi: 10.1007/s10589-024-00553-z

Bak, G., Bae, Y. (2023). Deep learning algorithm development for river flow prediction: PNP algorithm, Soft Computing, 27, 13487-13515. doi: 10.1007/s00500-023-08254-1

Bolshakov, V. (2013). Regression-based Daugava River flood forecasting and monitoring. Information Technology and Management Science, 16(1), 59–63. doi: 10.2478/itms-2013-0021

Elbeltagi, A., Di Nunno, F., Kushwaha, N. L., et al. (2022). River flow rate prediction in the Des Moines watershed (Iowa, USA): A machine learning approach. Stochastic Environmental Research and Risk Assessment, 36(11), 3835–3855. doi: 10.1007/s00477-022-02240-3

Hamlet, A. F., & Lettenmaier, D. P. (1999). Columbia River streamflow forecasting based on ENSO and PDO climate signals. Journal of Water Resources Planning and Management, 125(6), 333–341.

Hayder, G., Solihin, M. I. (2020). Modelling of River Flow Using Particle Swarm Optimized Cascade-Forward Neural Networks: A Case Study of Kelantan River in Malaysia, Applied Sciences. doi: 10.3390/app10238670

Hsieh, B., & Bartos, C. L. (2000). Riverflow/River stage prediction for military applications using artificial neural network modeling. doi: 10.21236/ADA382991

Stanford, J. A., Thompson, A., Asher, E., Gregory, S. V., Reeves, G., Ratliff, D., Bouwes, N., Frissell, C., & Williams, R. N. (2023). Columbia River Basin. In Rivers of North America (2nd ed., pp. 558–615). Academic Press. doi: 10.1016/B978-0-12-818847-7.00020-3

Kedam, N., Tiwari, D., Kumar, V., Kh M., & Salem, M. A. (2024). River stream flow prediction through advanced machine learning models for enhanced accuracy. Results in Engineering, 22, 102215. doi: 10.1016/j.rineng.2024.102215

Mahaki, B., Jafari, S., Rezaei, M., & Solooki, L. (2024). Types of correlation coefficients in medical research using SPSS and R software. Isfahan Medical School Journal, 42(773), 560–573.

Morháč, M., Matoušek, V. (2008). Peak clipping algorithms for background estimation in spectroscopic data. Applied spectroscopy, 62(1), 91-106.

Ogunjo, S., Olusola, A., & Olusegun, C. (2024). Predicting river discharge in the Niger River Basin: A deep learning approach. Applied Sciences, 14(1), 12. doi: 10.3390/app14010012

Omer, M. (2013). Analysis of SNIP algorithm for background estimation in spectra measured with LaBr3:Ce detectors. In Proceedings of the XYZ Conference (pp. 123-130). Springer. doi: 10.1007/978-4-431-54264-3 27

Porporato, A., & Ridolfi, L. (2001). Multivariate nonlinear prediction of river flows. Journal of Hydrology, 248, 109–122.

Roshni, T., Jha, M. K., & Drisya, J. (2020). Neural network modeling for groundwater-level forecasting in coastal aquifers. Neural Computing and Applications, 32, 12737–12754. doi: 10.1007/s00521-020-04722-z

Wang, J., Pan, X., Li, W., & Zhang, M. (2020, December). PDSR: Optimization of SNIP pre-pruning algorithm based on dynamic sparsity rate. In 2020 2nd International Conference on Information Technology and Computer Application (ITCA) (pp. 119–124). doi: 10.1109/ITCA52113.2020.00032

Yadav, A., Prasad, B. B. V. S., Mojjada, R. K., Kothamasu, K. K., & Joshi, D. (2020). Application of artificial neural network and genetic algorithm based artificial neural network models for river flow prediction. Rev. d'Intelligence Artif. , 34(6), 745–751. doi: 10.18280/ria.340608

Zanial, W. N. C., Abdul Malek, M. B., Md Reba, M. N., Zaini, N., Ahmed, A. N., Sherif, M., & Elshafie, A. (2023).River flow prediction based on improved machine learning method: Cuckoo Search-Artificial Neural Network. Applied Water Science, 13 (28), 1–10. doi: 10.1007/s13201-022-01830-0

Zhang, Y. (2024). Application of speech recognition technology based on multimodal information in human-computer interaction. International Journal of Advanced Computer Science and Applications, 15(9). doi: 10.14569/IJACSA.2024.0150911