Artificial intelligence and big data application for drinking water quality monitoring

An IoT real-time potable water quality monitoring and prediction model based on cloud computing architecture

10.3390/s24041180 · doi-reference

Performance analysis of the water quality index model for predicting water state using machine learning techniques

10.1016/j.psep.2022.11.073 · doi-reference

Robust machine learning algorithms for predicting coastal water quality index

10.1016/j.jenvman.2022.115923 · doi-reference

Application of digital PCR for public health-related water quality monitoring

10.1016/j.scitotenv.2022.155663 · doi-reference

Remote sensing retrieval of inland water quality parameters using Sentinel-2 and multiple machine learning algorithms

10.1007/s11356-022-23431-9 · doi-reference

Data-driven models for predicting microbial water quality in the drinking water source using E. coli monitoring and hydrometeorological data

10.1016/j.scitotenv.2021.149798 · doi-reference

Smart water quality monitoring with iot wireless sensor networks

10.3390/s24092871 · doi-reference

IoT-enabled effective real-time water quality monitoring method for aquaculture

10.1016/j.mex.2024.102906 · doi-reference

Water quality prediction using machine learning models based on grid search method

10.1007/s11042-023-16737-4 · doi-reference

Real-time monitoring and prediction of water quality parameters and algae concentrations using microbial potentiometric sensor signals and machine learning tools

10.1016/j.scitotenv.2020.142876 · doi-reference

Artificial intelligence for surface water quality evaluation, monitoring and assessment

10.3390/w15223919 · doi-reference

Perspectives of heavy metal pollution indices for soil, sediment, and water pollution evaluation: An insight

10.1016/j.totert.2023.100039 · doi-reference

Determination of key parameters in water quality monitoring of the most sediment-laden Yellow River based on water quality index

10.1016/j.psep.2022.05.067 · doi-reference

Water quality classification using machine learning algorithms

10.1016/j.jwpe.2022.102920 · doi-reference

Predicting optical water quality indicators from remote sensing using machine learning algorithms in tropical highlands of Ethiopia

10.3390/hydrology10050110 · doi-reference

10.3390/electronics11131927

10.3390/electronics11131927 · doi-reference

Using machine learning models for predicting the water quality index in the La Buong River, Vietnam

10.3390/w14101552 · doi-reference

The latest innovative avenues for the utilization of artificial Intelligence and big data analytics in water resource management

10.1016/j.rineng.2023.101566 · doi-reference

IoT-based solutions to monitor water level, leakage, and motor control for smart water tanks

10.3390/w14030309 · doi-reference

Development of LoRaWAN-based IoT system for water quality monitoring in rural areas

10.1016/j.eswa.2023.122862 · doi-reference

A hybrid machine learning and embedded IoT-based water quality monitoring system

10.1016/j.iot.2023.100774 · doi-reference

Recent advancements in electrochemical biosensors for monitoring the water quality

10.3390/bios12070551 · doi-reference

Modelling and prediction of water quality by using artificial intelligence

10.3390/su13084259 · doi-reference

Development of a proof-of-concept microfluidic portable pathogen analysis system for water quality monitoring

10.1016/j.scitotenv.2021.152556 · doi-reference

Real-time anomaly detection for water quality sensor monitoring based on multivariate deep learning technique

10.3390/s23208613 · doi-reference

Chemical assessment of drinking water quality and associated human health risk of heavy metals in Gutai Mountains, Romania

10.3390/toxics12030168 · doi-reference

A novel approach for water quality classification based on the integration of deep learning and feature extraction techniques

10.1016/j.chemolab.2021.104329 · doi-reference

Applications of artificial intelligence (AI) in drinking water treatment processes: Possibilities

10.1016/j.chemosphere.2024.141958 · doi-reference

Urban river water quality monitoring based on self-optimizing machine learning method using multi-source remote sensing data

10.1016/j.ecolind.2022.109750 · doi-reference

Low-cost internet-of-things water-quality monitoring system for rural areas

10.3390/s23083919 · doi-reference

Drinking water resources suitability assessment based on pollution index of groundwater using improved explainable artificial intelligence

10.3390/su152115655 · doi-reference