A multi-task deep neural network reveals inflowing river impacts for predictive lake management.

ABSTRACT: Lake ecosystems, vital freshwater resources, are increasingly threatened by pollution from riverine inputs, making the management of these loads critical for preventing ecological degradation. Predicting the combined effects of multiple rivers on lake water quality is a significant challenge; traditional mechanistic models are computationally intensive and data-dependent, while conventional machine learning methods often fail to capture the system's multifaceted nature. This complexity creates a critical need for an integrated predictive tool for effective environmental management. Here we show a multi-task deep neural network (MTDNN) that can accurately and simultaneously predict four key water quality indicators-permanganate index, total phosphorus, total nitrogen, and algal density-at m

SUBMITTER: Yan H 

PROVIDER: S-EPMC12274680 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature