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Probabilistic water demand forecasting using quantile regression algorithms
The results mostly favour the practical systems designed using the linear boosting algorithm, probably due to the presence of trends in the urban water flow time series.
A Gradient Free Neural Network Framework Based on Universal Approximation Theorem
The proposed algorithm adheres to the underlying theory, is highly fast, and results in remarkably low errors when applied for regression and classification of complex data-sets, such as the Griewank function of multiple variables $\mathbf{x} \in \mathbb{R}^{100}$ with random noise addition, and MNIST database for handwritten digits recognition, with $7\times10^4$ images.
Super ensemble learning for daily streamflow forecasting: Large-scale demonstration and comparison with multiple machine learning algorithms
Based on the obtained large-scale results, we propose super learning for daily streamflow forecasting.
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