A survey on river water quality modelling using artificial intelligence models: 2000–2020
There has been an unsettling rise in the river contamination due to the climate change and
anthropogenic activities. Last decades' research has immensely focussed on river basin …
anthropogenic activities. Last decades' research has immensely focussed on river basin …
A review of artificial neural network models for ambient air pollution prediction
Research activity in the field of air pollution forecasting using artificial neural networks
(ANNs) has increased dramatically in recent years. However, the development of ANN …
(ANNs) has increased dramatically in recent years. However, the development of ANN …
[HTML][HTML] Exploding the myths: An introduction to artificial neural networks for prediction and forecasting
Abstract Artificial Neural Networks (ANNs), sometimes also called models for deep learning,
are used extensively for the prediction of a range of environmental variables. While the …
are used extensively for the prediction of a range of environmental variables. While the …
SPlit: An optimal method for data splitting
In this article, we propose an optimal method referred to as SPlit for splitting a dataset into
training and testing sets. SPlit is based on the method of support points (SP), which was …
training and testing sets. SPlit is based on the method of support points (SP), which was …
Methods used for the development of neural networks for the prediction of water resource variables in river systems: Current status and future directions
Over the past 15 years, artificial neural networks (ANNs) have been used increasingly for
prediction and forecasting in water resources and environmental engineering. However …
prediction and forecasting in water resources and environmental engineering. However …
[HTML][HTML] Handling limited datasets with neural networks in medical applications: A small-data approach
Motivation Single-centre studies in medical domain are often characterised by limited
samples due to the complexity and high costs of patient data collection. Machine learning …
samples due to the complexity and high costs of patient data collection. Machine learning …
Data-driven modelling: some past experiences and new approaches
Physically based (process) models based on mathematical descriptions of water motion are
widely used in river basin management. During the last decade the so-called data-driven …
widely used in river basin management. During the last decade the so-called data-driven …
Review of input variable selection methods for artificial neural networks
The choice of input variables is a fundamental, and yet crucial consideration in identifying
the optimal functional form of statistical models. The task of selecting input variables is …
the optimal functional form of statistical models. The task of selecting input variables is …
Protocol for develo** ANN models and its application to the assessment of the quality of the ANN model development process in drinking water quality modelling
Abstract The application of Artificial Neural Networks (ANNs) in the field of environmental
and water resources modelling has become increasingly popular since early 1990s. Despite …
and water resources modelling has become increasingly popular since early 1990s. Despite …
Assessment of surface soil moisture using high-resolution multi-spectral imagery and artificial neural networks
Many crop production management decisions can be informed using data from high-
resolution aerial images that provide information about crop health as influenced by soil …
resolution aerial images that provide information about crop health as influenced by soil …