Logo Lanfrica
  • Home
  • Atlas
  • Insights
  • Docs
  • Sign in

© 2026 Lanfrica. All rights reserved. All copyrights of the resources shown on the Lanfrica website belong to the original copyright holders, unless explicitly stated otherwise.

Harnessing Deep Learning for Real-Time Water Quality Assessment: A Sustainable Solution

Domain:

environment and energyagriculture

Record type:

paper
Creator:
TouLekTouMou
Editor:
YahUniUniUni
Publisher:
CCSDMDPI
Host:avatar
International audience This study presents an innovative approach utilizing artificial intelligence (AI) for the prediction and classification of water quality parameters based on physico-chemical measurements. The primary objective was to enhance the accuracy, speed, and accessibility of water quality monitoring. Data collected from various water samples in Algeria were analyzed to determine key parameters such as conductivity, turbidity, pH, and total dissolved solids (TDS). These measurements were integrated into deep neural networks (DNNs) to predict indices such as the sodium adsorption ratio (SAR), magnesium hazard (MH), sodium percentage (SP), Kelley’s ratio (KR), potential salinity (PS), exchangeable sodium percentage (ESP), as well as Water Quality Index (WQI) and Irrigation Water Quality Index (IWQI). The DNNs model, optimized through the selection of various activation functions and hidden layers, demonstrated high precision, with a correlation coefficient (R) of 0.9994 and a low root mean square error (RMSE) of 0.0020. This AI-driven methodology significantly reduces the reliance on traditional laboratory analyses, offering real-time water quality assessments that are adaptable to local conditions and environmentally sustainable. This approach provides a practical solution for water resource managers, particularly in resource-limited regions, to efficiently monitor water quality and make informed decisions for public health and agricultural applications.

Visit

hal.science

Tags

water qualityartificial intelligencedeep neural networksphysico-chemical measurementsreal-time assessmentsustainable water management[CHIM.GENI]Chemical Sciences/Chemical engineering

Licenses

https://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/OpenAccess

Similar

Internet of Things-Enabled Deep Learning Model for Real-Time Air Quality AssessmentPoint-of-Care Real-Time Signal Quality for Fetal Doppler Ultrasound Using a Deep Learning ApproachIoT-enabled Real-time Water Quality Monitoring System for Sustainable Small-scale Aquaculture in Sub-Saharan AfricaA Deep Learning Approach for Real-Time Detection and Classification of Crop Leaf Diseases to Support Sustainable Farming PracticesA systematic review of integrating remote sensing and machine learning for near real-time water quality monitoring applicationsExplainable deep learning approach for recognizing “Egyptian Cobra” bite in real-time

Internet of Things-Enabled Deep Learning Model for Real-Time Air Quality Assessment

Air quality has become a concern in urban cities, particularly in areas where pollution levels pose

Point-of-Care Real-Time Signal Quality for Fetal Doppler Ultrasound Using a Deep Learning Approach

In this study, we present a deep learning framework designed to integrate with our previously develo

IoT-enabled Real-time Water Quality Monitoring System for Sustainable Small-scale Aquaculture in Sub-Saharan Africa

Small-scale aquaculture in sub-Saharan Africa is significantly impacted by poor water quality, with

A Deep Learning Approach for Real-Time Detection and Classification of Crop Leaf Diseases to Support Sustainable Farming Practices

Early and correct diagnosis of the crop leaf diseases is essential to guarantee agricultural output,

A systematic review of integrating remote sensing and machine learning for near real-time water quality monitoring applications

ABSTRACT Conceptual diagram of three input streams: satellite remote sensing, Io

Explainable deep learning approach for recognizing “Egyptian Cobra” bite in real-time

Abstract The Egyptian cobra is among the deadliest snake species, capable of cau