An Explainable Ensemble Machine-Learning Framework for Regime-Shift Forecasting of Algal Blooms across Contrasting Lake Systems: code and data

This repository contains the relevant code and data for the paper on An Explainable Ensemble Machine-Learning Framework for Regime-Shift Forecasting of Algal Blooms across Contrasting Lake Systems.Specifically, the Research data and codes.zip file contains water quality and meteorological datasets from Lake Taihu and NEL, together with the implementation codes for three machine learning models (RF, SVM, XGBoost).本仓库收录了题为《面向不同湖泊系统水华状态转移预测的可解释集成机器学习框架》的研究论文的相关代码与数据集。具体而言，压缩包Research data and codes.zip包含取自太湖（Lake Taihu）与NEL的水质及气象数据集，同时附带随机森林(RF)、支持向量机(SVM)、极端梯度提升树(XGBoost)三种机器学习模型的实现代码。