Measurement of total dissolved solids and total suspended solids in water systems: A review of the issues, conventional, and remote sensing techniques
This study provides a comprehensive review of the efforts utilized in the measurement of
water quality parameters (WQPs) with a focus on total dissolved solids (TDS) and total …
water quality parameters (WQPs) with a focus on total dissolved solids (TDS) and total …
Comparative analysis of surface water quality prediction performance and identification of key water parameters using different machine learning models based on big …
The water quality prediction performance of machine learning models may be not only
dependent on the models, but also dependent on the parameters in data set chosen for …
dependent on the models, but also dependent on the parameters in data set chosen for …
Water quality assessment of a river using deep learning Bi-LSTM methodology: forecasting and validation
Water is a prime necessity for the survival and sustenance of all living beings. Over the past
few years, the water quality of rivers is adversely affected due to harmful wastes and …
few years, the water quality of rivers is adversely affected due to harmful wastes and …
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 …
Hybrid WT–CNN–GRU-based model for the estimation of reservoir water quality variables considering spatio-temporal features
Water quality indicators (WQIs), such as chlorophyll-a (Chl-a) and dissolved oxygen (DO),
are crucial for understanding and assessing the health of aquatic ecosystems. Precise …
are crucial for understanding and assessing the health of aquatic ecosystems. Precise …
A multi-model data fusion methodology for reservoir water quality based on machine learning algorithms and bayesian maximum entropy
A major concern in the management of reservoirs is water quality because of the negative
consequences it has on both environment and human life. Artificial Intelligence (AI) concept …
consequences it has on both environment and human life. Artificial Intelligence (AI) concept …
Improving the performance of machine learning models for early warning of harmful algal blooms using an adaptive synthetic sampling method
Many countries have attempted to monitor and predict harmful algal blooms to mitigate
related problems and establish management practices. The current alert system-based …
related problems and establish management practices. The current alert system-based …
Predicting stream water quality under different urban development pattern scenarios with an interpretable machine learning approach
Urban development pattern significantly impacts stream water quality by influencing
pollutant generation, build-up, and wash-off processes. It is thus necessary to understand …
pollutant generation, build-up, and wash-off processes. It is thus necessary to understand …
[HTML][HTML] Machine learning models for water quality prediction: a comprehensive analysis and uncertainty assessment in Mirpurkhas, Sindh, Pakistan
Groundwater represents a pivotal asset in conserving natural water reservoirs for potable
consumption, irrigation, and diverse industrial uses. Nevertheless, human activities …
consumption, irrigation, and diverse industrial uses. Nevertheless, human activities …
A unified deep learning framework for water quality prediction based on time-frequency feature extraction and data feature enhancement
R Xu, S Hu, H Wan, Y ** highly nonlinear
relationships with acceptable computational speed, and have been widely used to predict …
relationships with acceptable computational speed, and have been widely used to predict …