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Water Quality

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Efficacy of GIS-based AHP and data-driven intelligent machine learning algorithms for irrigation water quality prediction in an agricultural-mine district within the Lower Benue Trough, Nigeria.

Environmental science and pollution research international
Agricultural productivity can be impaired by poor irrigation water quality. Therefore, adequate vulnerability assessment and identification of the most influential water quality parameters for accurate prediction becomes crucial for enhanced water re...

Large-scale prediction of stream water quality using an interpretable deep learning approach.

Journal of environmental management
Deep learning methods, which have strong capabilities for mapping highly nonlinear relationships with acceptable calculation speed, have been increasingly applied for water quality prediction in recent studies. However, it is argued that the practica...

Evaluation and prediction of irrigation water quality of an agricultural district, SE Nigeria: an integrated heuristic GIS-based and machine learning approach.

Environmental science and pollution research international
Poor irrigation water quality can mar agricultural productivity. Traditional assessment of irrigation water quality usually requires the computation of various conventional quality parameters, which is often time-consuming and associated with errors ...

Remote Sensing and Nonlinear Auto-regressive Neural Network (NARNET) Based Surface Water Chemical Quality Study: A Spatio-Temporal Hybrid Novel Technique (STHNT).

Bulletin of environmental contamination and toxicology
In recent days, the quality of water in inland water bodies has been threatened by various natural and anthropogenic activities. Henceforth, the continuous monitoring of water quality is mandatory to control the pollution level in surface water bodie...

Pattern recognition describing spatio-temporal drivers of catchment classification for water quality.

The Science of the total environment
Classification using spatial data is foundational for hydrological modelling, particularly for ungauged areas. However, models developed from classified land use drivers deliver inconsistent water quality results for the same land uses and hinder dec...

A parallel integrated learning technique of improved particle swarm optimization and BP neural network and its application.

Scientific reports
Swarm intelligence algorithm has attracted a lot of interest since its development, which has been proven to be effective in many application areas. In this study, an enhanced integrated learning technique of improved particle swarm optimization and ...

A decision fusion method based on classification models for water quality monitoring.

Environmental science and pollution research international
Monitoring of water quality is one of the world's main intentions for countries. Classification techniques based on support vector machines (SVMs) and artificial neural network (ANN) has been widely used in several applications of water research. Wat...

Artificial ecosystem optimization with Deep Learning Enabled Water Quality Prediction and Classification model.

Chemosphere
The majority of what is needed to maintain life is found in the approximately 70 percent of the earth's surface that is composed of water. Water quality has been deteriorating at an alarming rate as a direct result of rapid industrialization and urba...

Multi-input multi-output temporal convolutional network for predicting the long-term water quality of ocean ranches.

Environmental science and pollution research international
The prediction of water quality parameters is of great significance to the control of marine environments and provides a scientific decision-making basis for maintaining the stability of water environments and ensuring the normal survival and growth ...

Groundwater Quality: The Application of Artificial Intelligence.

Journal of environmental and public health
Humans and all other living things depend on having access to clean water, as it is an indispensable essential resource. Therefore, the development of a model that can predict water quality conditions in the future will have substantial societal and ...