AIMC Topic: Drinking Water
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Time series-based machine learning for forecasting multivariate water quality in full-scale drinking water treatment with various reagent dosages.
Water research
Nov 9, 2024
Accurately predicting drinking water quality is critical for intelligent water supply management and for maintaining the stability and efficiency of water treatment processes. This study presents an optimized time series machine learning approach for...
Advanced Low-Cost Technology for Assessing Metal Accumulation in the Body of a Metropolitan Resident Based on a Neural Network Model.
Sensors (Basel, Switzerland)
Nov 7, 2024
This study is devoted to creating a neural network technology for assessing metal accumulation in the body of a metropolis resident with short-term and long-term intake from anthropogenic sources. Direct assessment of metal retention in the human bod...
Machine learning for environmental justice: Dissecting an algorithmic approach to predict drinking water quality in California.
The Science of the total environment
Aug 24, 2024
The potential for machine learning to answer questions of environmental science, monitoring, and regulatory enforcement is evident, but there is cause for concern regarding potential embedded bias: algorithms can codify discrimination and exacerbate ...
Machine Learning-Assisted Eu(III)-Functionalized HOF-on-HOF Composite-Based Sensor Platform for Precise and Visual Identification of Multiple Pesticides.
Analytical chemistry
Aug 21, 2024
Precise and rapid identification of pesticides is crucial to ensure a green environment, food safety, and human health. However, complex sample environments often hinder precise identification, especially for simultaneous differentiation of multiple ...
Determinants of adoption of household water treatment in Haiti using two analysis methods: logistic regression and machine learning.
Journal of water and health
Aug 21, 2024
Household water treatment (HWT) is recommended when safe drinking water is limited. To understand determinants of HWT adoption, we conducted a cross-sectional survey with 650 households across different regions in Haiti. Data were collected on 71 dem...
Exploring the potential of machine learning to understand the occurrence and health risks of haloacetic acids in a drinking water distribution system.
The Science of the total environment
Aug 15, 2024
Determining the occurrence of disinfection byproducts (DBPs) in drinking water distribution system (DWDS) remains challenging. Predicting DBPs using readily available water quality parameters can help to understand DBPs associated risks and capture t...
Classification machine learning to detect de facto reuse and cyanobacteria at a drinking water intake.
The Science of the total environment
Jul 9, 2024
Harmful algal blooms (HABs) or higher levels of de facto water reuse (DFR) can increase the levels of certain contaminants at drinking water intakes. Therefore, the goal of this study was to use multi-class supervised machine learning (SML) classific...
Groundwater suitability assessment for irrigation and drinking purposes by integrating spatial analysis, machine learning, water quality index, and health risk model.
Environmental science and pollution research international
May 29, 2024
An in-depth understanding of nitrate-contaminated surface water and groundwater quality and associated risks is important for groundwater management. Hydrochemical characteristics and driving forces of groundwater quality and non-carcinogenic risks o...
Applications of artificial intelligence (AI) in drinking water treatment processes: Possibilities.
Chemosphere
Apr 10, 2024
In water treatment processes (WTPs), artificial intelligence (AI) based techniques, particularly machine learning (ML) models have been increasingly applied in decision-making activities, process control and optimization, and cost management. At leas...
A novel method for multi-pollutant monitoring in water supply systems using chemical machine vision.
Environmental science and pollution research international
Mar 6, 2024
Drinking water is vital for human health and life, but detecting multiple contaminants in it is challenging. Traditional testing methods are both time-consuming and labor-intensive, lacking the ability to capture abrupt changes in water quality over ...
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