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Water

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Advanced monitoring and numerical modelling of the stability, safety and reliability indicators of the earthen dam of Songloulou (Cameroon).

PloS one
For the determination of global stability after long term advanced monitoring, artificial intelligence have been used for the data analysis of water level and displacements of Songloulou earth dam at Cameroon. Measurements of safety and reliability i...

Individualized hemodialysis: Is similar hemodialysis adequacy possible using less water?

Turkish journal of medical sciences
BACKGROUND AND AIM: There are over 60,000 hemodialysis (HD) patients in Türkiye, and the number of patients is increasing yearly. Dialysate flow rate (Qd) is a factor in HD adequacy. Approximately 150 L of water are consumed per session to prepare th...

An interval water demand prediction method to reduce uncertainty: A case study of Sichuan Province, China.

Environmental research
Effective prediction of water demand is a prerequisite for decision makers to achieve reliable management of water supply. Currently, the research on water demand prediction focuses on point prediction method. In this study, we constructed a GA-BP-KD...

Use of machine learning and deep learning to predict particulate Cs concentrations in a nuclearized river.

Journal of environmental radioactivity
Cesium-137, discharged by nuclear installations under normal operations and deposited in watersheds following atmospheric testing and accidents (i.e. Chernobyl, Fukushima …), has been studied for decades. Thus, modelling of Cs concentration in rivers...

Explainable Supervised Machine Learning Model To Predict Solvation Gibbs Energy.

Journal of chemical information and modeling
Many challenges persist in developing accurate computational models for predicting solvation free energy (Δ). Despite recent developments in Machine Learning (ML) methodologies that outperformed traditional quantum mechanical models, several issues r...

Rainfall-Runoff modelling using SWAT and eight artificial intelligence models in the Murredu Watershed, India.

Environmental monitoring and assessment
The growing concerns surrounding water supply, driven by factors such as population growth and industrialization, have highlighted the need for accurate estimation of streamflow at the river basin level. To achieve this, rainfall-runoff models are wi...

Soil Moisture and Heat Level Prediction for Plant Health Monitoring Using Deep Learning with Gannet Namib Beetle Optimization in IoT.

Applied biochemistry and biotechnology
Plant health monitoring is crucial in ensuring a constant food supply to satisfy the growing demand for food. Hence, it is essential to monitor plant health to maximize the yield and minimize the risk of various diseases. Soil moisture and temperatur...

Deep learning proton beam range estimation model for quality assurance based on two-dimensional scintillated light distributions in simulations.

Medical physics
BACKGROUND: Many studies have utilized optical camera systems with volumetric scintillators for quality assurances (QA) to estimate the proton beam range. However, previous analytically driven range estimation methods have the difficulty to derive th...

Elucidating the Role of Hydrogen Bonding in the Optical Spectroscopy of the Solvated Green Fluorescent Protein Chromophore: Using Machine Learning to Establish the Importance of High-Level Electronic Structure.

The journal of physical chemistry letters
Hydrogen bonding interactions with chromophores in chemical and biological environments play a key role in determining their electronic absorption and relaxation processes, which are manifested in their linear and multidimensional optical spectra. Fo...

Count-Based Morgan Fingerprint: A More Efficient and Interpretable Molecular Representation in Developing Machine Learning-Based Predictive Regression Models for Water Contaminants' Activities and Properties.

Environmental science & technology
In this study, we introduce the count-based Morgan fingerprint (C-MF) to represent chemical structures of contaminants and develop machine learning (ML)-based predictive models for their activities and properties. Compared with the binary Morgan fing...