Author:
Ehteram Mohammad,Seifi Akram,Banadkooki Fatemeh Barzegari
Publisher
Springer Nature Singapore
Reference22 articles.
1. Abedini, M., Ziai, A. N., Shafiei, M., Ghahraman, B., Ansari, H., & Meshkini, J. (2017). Uncertainty assessment of groundwater flow modeling by using generalized likelihood uncertainty estimation method (case study: Bojnourd Plain). Iranian Journal of Irrigation & Drainage, 10(6), 755–769.
2. Angelaki, A., Singh Nain, S., Singh, V., & Sihag, P. (2021). Estimation of models for cumulative infiltration of soil using machine learning methods. ISH Journal of Hydraulic Engineering. https://doi.org/10.1080/09715010.2018.1531274
3. Ehteram, M., Graf, R., Ahmed, A. N., & El-Shafie, A. (2022a). Improved prediction of daily pan evaporation using Bayesian Model averaging and optimized kernel extreme machine models in different climates. Stochastic Environmental Research and Risk Assessment, 1–36.
4. Ehteram, M., Panahi, F., Ahmed, A. N., Huang, Y. F., Kumar, P., & Elshafie, A. (2022a). Predicting evaporation with optimized artificial neural network using multi-objective salp swarm algorithm. Environmental Science and Pollution Research. https://doi.org/10.1007/s11356-021-16301-3
5. Ehteram, M., Yenn Teo, F., Najah Ahmed, A., Dashti Latif, S., Feng Huang, Y., Abozweita, O., Al-Ansari, N., & El-Shafie, A. (2021). Performance improvement for infiltration rate prediction using hybridized adaptive neuro-fuzzy inferences system (ANFIS) with optimization algorithms. Ain Shams Engineering Journal. https://doi.org/10.1016/j.asej.2020.08.019