Publications
A stochastic data-driven ensemble forecasting framework for water resources: A case study using ensemble members derived from a database of deterministic wavelet-based models. Water Resources Research, 55, 175–202.
. (2019). Coupling the maximum overlap discrete wavelet transform and long short-term memory networks for irrigation flow forecasting. Agricultural Water Management, 219, 72–85. Elsevier.
. (2019). On the applicability of maximum overlap discrete wavelet transform integrated with MARS and M5 model tree for monthly pan evaporation prediction. Agricultural and Forest Meteorology, 278, 107647. Elsevier.
. (2019). Addressing the incorrect usage of wavelet-based hydrological and water resources forecasting models for real-world applications with best practices and a new forecasting framework. Journal of hydrology, 563, 336–353. Elsevier.
. (2018). Very short-term reactive forecasting of the solar ultraviolet index using an extreme learning machine integrated with the solar zenith angle. Environmental research, 155, 141–166. Academic Press.
. (2017). Forecasting effective drought index using a wavelet extreme learning machine (W-ELM) model. Stochastic environmental research and risk assessment, 31, 1211–1240. Springer Berlin Heidelberg.
. (2017). Stream-flow forecasting using extreme learning machines: A case study in a semi-arid region in Iraq. Journal of Hydrology, 542, 603–614. Elsevier.
. (2016). Coupling machine learning methods with wavelet transforms and the bootstrap and boosting ensemble approaches for drought prediction. Atmospheric research, 172, 37–47. Elsevier.
. (2016). Bootstrap rank-ordered conditional mutual information (broCMI): A nonlinear input variable selection method for water resources modeling. Water Resources Research, 52, 2299–2326.
. (2016). The application of dynamic linear bayesian models in hydrological forecasting: varying coefficient regression and discount weighted regression. Journal of Hydrology, 530, 762–784. Elsevier.
. (2015). Modeling of daily pan evaporation in sub tropical climates using ANN, LS-SVR, Fuzzy Logic, and ANFIS. Expert systems with applications, 41, 5267–5276. Elsevier.
. (2014). A stochastic data-driven forecasting framework using wavelets for forecasting uncertain hydrological and water resources processes. In EGU General Assembly Conference Abstracts (Vol. 20, p. 2380).
. (2018). Information-theoretic-based input variable selection for hydrology and water resources. In EGU General Assembly Conference Abstracts (Vol. 20, p. 2379).
. (2018). Hydrological data assimilation using Extreme Learning Machines. In EGU General Assembly Conference Abstracts (Vol. 19, p. 5722).
. (2017). Stochastic weather inputs for improved urban water demand forecasting: application of nonlinear input variable selection and machine learning methods. In AGU Fall Meeting Abstracts.
. (2015). Forecasting Urban Water Demand via Machine Learning Methods Coupled with a Bootstrap Rank-Ordered Conditional Mutual Information Input Variable Selection Method. In AGU Fall Meeting Abstracts.
. (2014). Forecasting drought via bootstrap and machine learning methods. In CSCE 3rd Specialty Conference on Disaster Prevention and Mitigation.
. (2013).