From fully physical to virtual sensing for water quality assessment: A comprehensive review of the relevant state-of-the-art
Rapid urbanization, industrial development, and climate change have resulted in water
pollution and in the quality deterioration of surface and groundwater at an alarming rate …
pollution and in the quality deterioration of surface and groundwater at an alarming rate …
Application of artificial intelligence methods for monsoonal river classification in Selangor river basin, Malaysia
YJ Wong, Y Shimizu, A Kamiya, L Maneechot… - Environmental …, 2021 - Springer
Rivers in Malaysia are classified based on water quality index (WQI) that comprises of six
parameters, namely, ammoniacal nitrogen (AN), biochemical oxygen demand (BOD) …
parameters, namely, ammoniacal nitrogen (AN), biochemical oxygen demand (BOD) …
Application of classification machine learning algorithms for characterizing nutrient transport in a clay plain agricultural watershed
Excess nutrients in surface water and groundwater can lead to water quality deterioration in
available water resources. Thus, the classification of nutrient concentrations in water …
available water resources. Thus, the classification of nutrient concentrations in water …
[HTML][HTML] Machine learning models for prediction of nutrient concentrations in surface water in an agricultural watershed
Prediction and quantification of nutrient concentrations in surface water has gained
substantial attention during recent decades because excess nutrients released from …
substantial attention during recent decades because excess nutrients released from …
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 …
Determination of Optimal Predictors and Sampling Frequency to Develop Nutrient Soft Sensors Using Random Forest
M Arhab, J Huang - Sensors, 2023 - mdpi.com
Despite advancements in sensor technology, monitoring nutrients in situ and in real-time is
still challenging and expensive. Soft sensors, based on data-driven models, offer an …
still challenging and expensive. Soft sensors, based on data-driven models, offer an …
A virtual sensing concept for Nitrogen and Phosphorus monitoring using machine learning techniques
Harmful cyanobacterial bloom (HCB) is problematic for drinking water treatment, and some
of its strains can produce toxins that significantly affect human health. To better control …
of its strains can produce toxins that significantly affect human health. To better control …
Machine learning models to predict nitrate concentration in a river basin
DY Dorado-Guerra, G Corzo-Pérez… - Environmental …, 2023 - iopscience.iop.org
Aquifer-stream interactions affect the water quality in Mediterranean areas; therefore, the
coupling of surface water and groundwater models is generally used to solve water …
coupling of surface water and groundwater models is generally used to solve water …
Machine learning-based forecasting of potability of drinking water through adaptive boosting model
Water is an indispensable requirement for life for health and many other purposes, but not
all water is safe for consumption. Thus, various metrics, such as biological, chemical, and …
all water is safe for consumption. Thus, various metrics, such as biological, chemical, and …
Health risk assessment related to cyanotoxins exposure of a community living near Tri An Reservoir, Vietnam
TAD Nguyen, LT Nguyen, A Enright, LT Pham… - … Science and Pollution …, 2021 - Springer
Cyanotoxins released by cyanobacteria are currently a concern due to potential impacts on
plants, animals, and human health. Many instances of cyanotoxin poisoning have been …
plants, animals, and human health. Many instances of cyanotoxin poisoning have been …