Applications for deep learning in ecology
S Christin, É Hervet, N Lecomte - Methods in Ecology and …, 2019 - Wiley Online Library
A lot of hype has recently been generated around deep learning, a novel group of artificial
intelligence approaches able to break accuracy records in pattern recognition. Over the …
intelligence approaches able to break accuracy records in pattern recognition. Over the …
Deep learning as a tool for ecology and evolution
Deep learning is driving recent advances behind many everyday technologies, including
speech and image recognition, natural language processing and autonomous driving. It is …
speech and image recognition, natural language processing and autonomous driving. It is …
Protocol for developing 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 …
A hybrid ARIMA and artificial neural networks model to forecast particulate matter in urban areas: The case of Temuco, Chile
Air quality time series consists of complex linear and non-linear patterns and are difficult to
forecast. Box–Jenkins Time Series (ARIMA) and multilinear regression (MLR) models have …
forecast. Box–Jenkins Time Series (ARIMA) and multilinear regression (MLR) models have …
A translucent box: interpretable machine learning in ecology
TCD Lucas - Ecological Monographs, 2020 - Wiley Online Library
Abstract Machine learning has become popular in ecology but its use has remained
restricted to predicting, rather than understanding, the natural world. Many researchers …
restricted to predicting, rather than understanding, the natural world. Many researchers …
Application of deep learning in ecological resource research: Theories, methods, and challenges
Ecological resources are an important material foundation for the survival, development, and
self-realization of human beings. In-depth and comprehensive research and understanding …
self-realization of human beings. In-depth and comprehensive research and understanding …
Improved predictive performance of cyanobacterial blooms using a hybrid statistical and deep-learning method
H Li, C Qin, W He, F Sun, P Du - Environmental Research Letters, 2021 - iopscience.iop.org
Cyanobacterial harmful algal blooms (CyanoHABs) threaten ecosystem functioning and
human health at both regional and global levels, and this threat is likely to become more …
human health at both regional and global levels, and this threat is likely to become more …
Stable forecasting of environmental time series via long short term memory recurrent neural network
In a recent decade, deep neural networks have been applied for many research areas after
achieving dramatic improvements of accuracy in solving complex problems in vision and …
achieving dramatic improvements of accuracy in solving complex problems in vision and …
[HTML][HTML] Performance evaluation of artificial neural network approaches in forecasting reservoir inflow
This study investigates the potential of Time Lag Recurrent Neural Networks (TLRN) for
modeling the daily inflow into Eleviyan reservoir, Iran. TLRN are extended with short term …
modeling the daily inflow into Eleviyan reservoir, Iran. TLRN are extended with short term …
Neural hierarchical models of ecological populations
MB Joseph - Ecology Letters, 2020 - Wiley Online Library
Neural networks are increasingly being used in science to infer hidden dynamics of natural
systems from noisy observations, a task typically handled by hierarchical models in ecology …
systems from noisy observations, a task typically handled by hierarchical models in ecology …