Machine learning based marine water quality prediction for coastal hydro-environment management
During the past three decades, harmful algal blooms (HAB) events have been frequently
observed in marine waters around many coastal cities in the world including Hong Kong …
observed in marine waters around many coastal cities in the world including Hong Kong …
A review of data-driven modelling in drinking water treatment
A Aliashrafi, Y Zhang, H Groenewegen… - … in Environmental Science …, 2021 - Springer
There are significant opportunities to optimize drinking water treatment and water resource
management using data-driven models. Modelling can help define complex system …
management using data-driven models. Modelling can help define complex system …
Application of hybrid machine learning models and data pre-processing to predict water level of watersheds: Recent trends and future perspective
SJ Mohammed, SL Zubaidi, S Ortega-Martorell… - Cogent …, 2022 - Taylor & Francis
The community's well-being and economic livelihoods are heavily influenced by the water
level of watersheds. The changes in water levels directly affect the circulation processes of …
level of watersheds. The changes in water levels directly affect the circulation processes of …
A hybrid decomposition and Machine learning model for forecasting Chlorophyll-a and total nitrogen concentration in coastal waters
Abstract Information regarding Chlorophyll-a (ChlA) and total nitrogen (TN) is critical for
early warning of algal blooms. However, reliable models for accurate forecasting of the ChlA …
early warning of algal blooms. However, reliable models for accurate forecasting of the ChlA …
Machine learning approaches to coastal water quality monitoring using GOCI satellite data
Since coastal waters are one of the most vulnerable marine systems to environmental
pollution, it is very important to operationally monitor coastal water quality. This study …
pollution, it is very important to operationally monitor coastal water quality. This study …
基于动态滑动窗口BP 神经网络的水质时间序列预测
张梦迪， 徐庆， 刘振鸿， 马春燕， 高品 - 环境工程技术学报, 2022 - hjgcjsxb.org.cn
为提高BP 神经网络(BPNN) 模型对具有时间序列特征水质的预测精准度, 采用主成分分析法对
原始样本数据进行特征提取和降维, 选取溶解性有机碳(DOC) 浓度, 总氮(TN) …
原始样本数据进行特征提取和降维, 选取溶解性有机碳(DOC) 浓度, 总氮(TN) …
A data-driven model for real-time water quality prediction and early warning by an integration method
T Jin, S Cai, D Jiang, J Liu - Environmental Science and Pollution …, 2019 - Springer
Due to increasingly serious deterioration of surface water quality, effective water quality
prediction technique for real-time early warning is essential to guarantee the emergency …
prediction technique for real-time early warning is essential to guarantee the emergency …
Artificial Neural Network ensemble modeling with conjunctive data clustering for water quality prediction in rivers
Abstract The Artificial Neural Network (ANN) is a powerful data-driven model that can
capture and represent both linear and non-linear relationships between input and output …
capture and represent both linear and non-linear relationships between input and output …
Prediction of water turbidity in a marine environment using machine learning: A case study of Hong Kong
The water quality measurement of marine water is a key research topic for environmental
and ocean modelers in the past several decades. Marine water quality is mainly described …
and ocean modelers in the past several decades. Marine water quality is mainly described …
Prediction model of undisturbed ground temperature using artificial neural network (ANN) and multiple regressions approach
M King, BJ Kim, CY Yune - Geothermics, 2024 - Elsevier
The prediction of ground temperature is of utmost importance for evaluating temperature
fluctuations in both the surface and subsurface of the ground and is essential for making …
fluctuations in both the surface and subsurface of the ground and is essential for making …