[HTML][HTML] Smarter eco-cities and their leading-edge artificial intelligence of things solutions for environmental sustainability: A comprehensive systematic review
The recent advancements made in the realms of Artificial Intelligence (AI) and Artificial
Intelligence of Things (AIoT) have unveiled transformative prospects and opportunities to …
Intelligence of Things (AIoT) have unveiled transformative prospects and opportunities to …
Artificial intelligence for sustainability: Challenges, opportunities, and a research agenda
Artificial intelligence (AI) will transform business practices and industries and has the
potential to address major societal problems, including sustainability. Degradation of the …
potential to address major societal problems, including sustainability. Degradation of the …
[HTML][HTML] Performance analysis of the water quality index model for predicting water state using machine learning techniques
Existing water quality index (WQI) models assess water quality using a range of
classification schemes. Consequently, different methods provide a number of interpretations …
classification schemes. Consequently, different methods provide a number of interpretations …
A review of the artificial neural network models for water quality prediction
Water quality prediction plays an important role in environmental monitoring, ecosystem
sustainability, and aquaculture. Traditional prediction methods cannot capture the nonlinear …
sustainability, and aquaculture. Traditional prediction methods cannot capture the nonlinear …
Short-term water quality variable prediction using a hybrid CNN–LSTM deep learning model
Water quality monitoring is an important component of water resources management. In
order to predict two water quality variables, namely dissolved oxygen (DO; mg/L) and …
order to predict two water quality variables, namely dissolved oxygen (DO; mg/L) and …
Artificial neural networks for water quality soft-sensing in wastewater treatment: a review
This paper aims to present a comprehensive survey on water quality soft-sensing of a
wastewater treatment process (WWTP) based on artificial neural networks (ANNs). We …
wastewater treatment process (WWTP) based on artificial neural networks (ANNs). We …
Advances in machine learning modeling reviewing hybrid and ensemble methods
The conventional machine learning (ML) algorithms are continuously advancing and
evolving at a fast-paced by introducing the novel learning algorithms. ML models are …
evolving at a fast-paced by introducing the novel learning algorithms. ML models are …
Data-driven soft computing modeling of groundwater quality parameters in southeast Nigeria: comparing the performances of different algorithms
JC Egbueri, JC Agbasi - Environmental Science and Pollution Research, 2022 - Springer
In recent decades, the simulation and modeling of water quality parameters have been
useful for monitoring and assessment of the quality of water resources. Moreover, the use of …
useful for monitoring and assessment of the quality of water resources. Moreover, the use of …
Reliability assessment of water quality index based on guidelines of national sanitation foundation in natural streams: Integration of remote sensing and data-driven …
Rivers, as one of the freshwater resources, are generally put in the state of jeopardy in terms
of quantity and quality due to the development in industry, agriculture, and urbanization …
of quantity and quality due to the development in industry, agriculture, and urbanization …
Forecasting effluent and performance of wastewater treatment plant using different machine learning techniques
M El-Rawy, MK Abd-Ellah, H Fathi… - Journal of Water Process …, 2021 - Elsevier
Expectation of wastewater quality in wastewater treatment plants (WWTPs) is significant and
can decrease the sampling number, cost, decision time, and energy. This paper presents …
can decrease the sampling number, cost, decision time, and energy. This paper presents …