AI Medical Compendium Journal:
Water research

Showing 111 to 120 of 130 articles

Machine learning methods for imbalanced data set for prediction of faecal contamination in beach waters.

Water research
Predicting water contamination by statistical models is a useful tool to manage health risk in recreational beaches. Extreme contamination events, i.e. those exceeding normative are generally rare with respect to bathing conditions and thus the data ...

Predicting the performance of anaerobic digestion using machine learning algorithms and genomic data.

Water research
Modeling of anaerobic digestion (AD) is crucial to better understand the process dynamics and to improve the digester performance. This is an essential yet difficult task due to the complex and unknown interactions within the system. The application ...

Deep learning model for simulating influence of natural organic matter in nanofiltration.

Water research
Controlling membrane fouling in a membrane filtration system is critical to ensure high filtration performance. A forecast of membrane fouling could enable preliminary actions to relieve the development of membrane fouling. Therefore, we established ...

Prediction of antibiotic-resistance genes occurrence at a recreational beach with deep learning models.

Water research
Antibiotic resistance genes (ARGs) have been reported to threaten the public health of beachgoers worldwide. Although ARG monitoring and beach guidelines are necessary, substantial efforts are required for ARG sampling and analysis. Accordingly, in t...

Replacing the internal standard to estimate micropollutants using deep and machine learning.

Water research
Similar to the worldwide proliferation of urbanization, micropollutants have been involved in aquatic and ecological environmental systems. These pollutants have the propensity to wreak havoc on human health and the ecological system; hence, it is im...

Exploring the multiscale hydrologic regulation of multipond systems in a humid agricultural catchment.

Water research
Assessing the hydrologic processes over scales ranging from single wetland to regional is critical to understand the hydrologically-driven ecosystem services especially nutrient buffering of wetlands. Here, we present a novel approach to quantify the...

An integrated approach based on virtual data augmentation and deep neural networks modeling for VFA production prediction in anaerobic fermentation process.

Water research
Data-driven models are suitable for simulating biological wastewater treatment processes with complex intrinsic mechanisms. However, raw data collected in the early stage of biological experiments are normally not enough to train data-driven models. ...

A predictive model of recreational water quality based on adaptive synthetic sampling algorithms and machine learning.

Water research
Predicting recreational water quality is one of the most difficult tasks in water management with major implications for humans and society. Many data-driven models have been used to predict water quality indicators to allow a real time assessment of...

Soft detection of 5-day BOD with sparse matrix in city harbor water using deep learning techniques.

Water research
To better control and manage harbor water quality is an important mission for coastal cities such as New York City (NYC). To achieve this, managers and governors need keep track of key quality indicators, such as temperature, pH, and dissolved oxygen...

Deep learning identifies accurate burst locations in water distribution networks.

Water research
Pipe bursts in water distribution networks lead to considerable water loss and pose risks of bacteria and pollutant contamination. Pipe burst localisation methods help water service providers repair the burst pipes and restore water supply timely and...