水质
多元统计
质量(理念)
河口
环境科学
深水
深度学习
环境质量
空气质量指数
计算机科学
机器学习
气象学
工程类
海洋学
地理
生态学
地质学
海洋工程
哲学
认识论
生物
作者
Amina Saeed,Areej Alsini,Dawood Amin
标识
DOI:10.1016/j.envsoft.2023.105884
摘要
Accurately predicting estuaries water quality is essential to support immediate intervention to water quality problem management. Deep learning is used to improve forecasting of water quality parameters in many aquatic systems. However, it is frequently constrained by low data frequency and quality. High-frequency, continuous monitoring using integrated in-situ water quality and environmental sensors can be an input to deep learning models resulting in highly accurate water quality and environmental predictions. This paper proposes a novel approach to improve forecasting of water quality and environmental variables. The results of the real-world data from the Swan Canning Estuary sites show how well the suggested model works. With different sizes of training and testing sets, the model can still predict the increased number of hours in high scores. Eliminating highly correlated variables impact the model's performance, emphasising the usefulness of strongly correlated variables in scarce data scenarios.
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