Code and data for probabilistic algal biomass forecasting in Lake Dianchi using Bayesian additive regression trees

Description

This dataset supports a study of 1- to 5-day-ahead chlorophyll-a forecasting in Lake Dianchi using BART. It includes processed daily water-quality observations from Huiwanzhong Station obtained from the China National Environmental Monitoring Center, together with R code and model outputs. The results show that BART provides competitive point accuracy while enabling uncertainty quantification and model explanation.

Steps to reproduce

See the README file for detailed instructions. Run the provided R scripts with the included data to reproduce the analyses and figures reported in the study.

Institutions

• Xiamen University

  Fujian, Xiamen

Categories

Funders

• National Natural Science Foundation of China

  Beijing, Beijing

  Grant ID: 52409106
• Fujian Provincial Water‑Conservancy S&T Project

  Grant ID: MSK202513

Licence