Modeling Spatial Data with Heteroscedasticity Using PLVCSAR Model: A Bayesian Quantile Regression Approach by Rongshang Chen, Zhiyong Chen

“…Spatial data not only enables smart cities to visualize, analyze, and interpret data related to location and space, but also helps departments make more informed decisions. We apply a Bayesian quantile regression (BQR) of the partially linear varying coefficient spatial autoregressive (PLVCSAR) model for spatial data to improve the prediction of performance. …”
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Growing-season NDVI responses to climate change in China’s three major marsh wetland regions by Yue Deng, Shujing Gao, Lulu Liu, Weiguo Jiang

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Phenology-Based Maize and Soybean Yield Potential Prediction Using Machine Learning and Sentinel-2 Imagery Time-Series by Dorijan Radočaj, Ivan Plaščak, Mladen Jurišić

“…Four machine learning algorithms were tested: random forest (RF), support vector machine regression (SVM), multivariate adaptive regression splines (MARS), and Bayesian regularized neural networks (BRNNs). …”
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Incorporating Risk in Operational Water Resources Management: Probabilistic Forecasting, Scenario Generation, and Optimal Control by Ties van derHeijden, Miguel Angel Mendoza‐Lugo, Peter Palensky, Nick van deGiesen, Edo Abraham

“…We utilize Combined Quantile Regression Deep Neural Networks and Non‐parametric Bayesian Networks to generate probabilistic scenarios that capture realistic temporal dependencies. …”
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