Xin Ma

TL;DR: Two novel hybrid decision tree-based machine learning models are proposed to obtain more accurate short-term water quality prediction results and shows that the prediction stability of CEEMDAN-RF and CEEMdAN-XGBoost is higher than other benchmark models.

TL;DR: In this article, the authors evaluated the potential of a new machine learning algorithm using gradient boosting on decision trees with categorical features support (i.e., CatBoost) for accurately estimating daily ET0 with limited meteorological data in humid regions of China.

TL;DR: A novel fractional grey model called the fractional time delayed grey model, which significantly outperforms the other 8 existing grey models is proposed and applied to forecast the coal and natural gas consumption of Chongqing China.

TL;DR: A novel nonlinear grey Bernoulli model with fractional order accumulation, abbreviated as FANGBM(1,1) model, is proposed to forecast short-term renewable energy consumption of China during the 13th Five-Year Plan (2016–2020).

TL;DR: In this article, the authors assess the historical carbon mitigation and simulate the energy and emission peaks of China's residential building sector using a dynamic emission scenario, and the sensitivity analysis reveals that the impacts of floor space per capita and energy intensity of urban residential buildings are the most significant for the uncertainty of emission peaks.