A review on machine learning, artificial intelligence, and smart technology in water treatment and monitoring
Artificial-intelligence methods and machine-learning models have demonstrated their ability
to optimize, model, and automate critical water-and wastewater-treatment applications …
to optimize, model, and automate critical water-and wastewater-treatment applications …
Smart water resource management using Artificial Intelligence—A review
SR Krishnan, MK Nallakaruppan, R Chengoden… - Sustainability, 2022 - mdpi.com
Water management is one of the crucial topics discussed in most of the international forums.
Water harvesting and recycling are the major requirements to meet the global upcoming …
Water harvesting and recycling are the major requirements to meet the global upcoming …
Emerging evolutionary algorithm integrated with kernel principal component analysis for modeling the performance of a water treatment plant
Providing a robust and reliable model is essential for hydro-environmental and public health
engineering perspectives, including water treatment plants (WTPs). The current research …
engineering perspectives, including water treatment plants (WTPs). The current research …
Deep learning with data preprocessing methods for water quality prediction in ultrafiltration
Ultrafiltration (UF) has been widely used to remove colloidal substances and suspended
solids in feed water. However, UF membrane breakage can lead to downstream impurities …
solids in feed water. However, UF membrane breakage can lead to downstream impurities …
[PDF][PDF] Estimation of water quality index using artificial intelligence approaches and multi-linear regression
Water quality index is a measure of water quality at a certain location and over a period of
time. High value indicates that the water is unsafe for drinking and inadequate in quality to …
time. High value indicates that the water is unsafe for drinking and inadequate in quality to …
A soft-sensor for sustainable operation of coagulation and flocculation units
M Arab, H Akbarian, M Gheibi, M Akrami… - … Applications of Artificial …, 2022 - Elsevier
Abstract Nowadays, Machine Learning (ML) techniques have become one of the most
widely used engineering tools due to their numerous advantages, including their continuous …
widely used engineering tools due to their numerous advantages, including their continuous …
Potential of hybrid data-intelligence algorithms for multi-station modelling of rainfall
One of the most challenging tasks in rainfall prediction is designing a reliable computational
methodology owing the random and stochastic characteristics of time-series. In this study …
methodology owing the random and stochastic characteristics of time-series. In this study …
Hybrid machine learning ensemble techniques for modeling dissolved oxygen concentration
The reliable prediction of dissolved oxygen concentration (DO) is significantly crucial for
protecting the health of the aquatic ecosystem. The current research employed four different …
protecting the health of the aquatic ecosystem. The current research employed four different …
A novel multi-model data-driven ensemble technique for the prediction of retention factor in HPLC method development
Reliable simulation of retention factor (k) is crucial in high-performance liquid
chromatography (HPLC) method development. In this research, three different Artificial …
chromatography (HPLC) method development. In this research, three different Artificial …
Prediction of compressive strength of concrete incorporated with jujube seed as partial replacement of coarse aggregate: a feasibility of Hammerstein–Wiener model …
The need for evaluation of compressive strength of a concrete is of utmost importance in civil
and structural engineering as one of the factors that determine quality of concrete. In this …
and structural engineering as one of the factors that determine quality of concrete. In this …