Robust prediction of chlorophyll-A from nitrogen and phosphorus content in Philippine and global lakes using fine-tuned, explainable machine learning

Chlorophyll-a (Chl-a) content in waterbodies is a primary indicator of algal biomass and is used to detect impending harmful algal blooms. This paper presents a methodology using 8 popular machine learning (ML) models for estimating Chl-a concentration from nutrient content in lakes. Different from...

ver descrição completa

Detalhes bibliográficos

Principais autores:	Karl Ezra Pilario, Eric Jan Escober, Aurelio de los Reyes V, Maria Pythias Espino
Formato:	Artigo
Idioma:	Inglês
Publicado em:	Elsevier 2024-12-01
Colecção:	Environmental Challenges
Assuntos:	Machine learning Chlorophyll-a Robustness analysis Explainability Bayesian optimization Ion chromatography
Acesso em linha:	http://www.sciencedirect.com/science/article/pii/S2667010024002221
Tags:	Adicionar Tag Sem tags, seja o primeiro a adicionar uma tag!