An Explainable Ensemble Machine-Learning Framework for Regime-Shift Forecasting of Algal Blooms across Contrasting Lake Systems: code and data
Abstract
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).
Fields of Study & Subjects
Aquatic Ecosystems and Phytoplankton Dynamics
Hydrological Forecasting Using AI
Ecosystem dynamics and resilience
Publication details
- Journal / source
- Zenodo (CERN European Organization for Nuclear Research)
- Publication date
- 12 January 2026
- Document type
- Dataset