A survey on river water quality modelling using artificial intelligence models: 2000–2020

Journal of Hydrology, Journal Year: 2020, Volume and Issue: 585, P. 124670 - 124670

Published: Feb. 14, 2020

Language: Английский

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Applications for deep learning in ecology

Methods in Ecology and Evolution, Journal Year: 2019, Volume and Issue: 10(10), P. 1632 - 1644

Published: July 5, 2019

Abstract A lot of hype has recently been generated around deep learning, a novel group artificial intelligence approaches able to break accuracy records in pattern recognition. Over the course just few years, learning revolutionized several research fields such as bioinformatics and medicine with its flexibility ability process large complex datasets. As ecological datasets are becoming larger more complex, we believe these methods can be useful ecologists well. In this paper, review existing implementations show that used successfully identify species, classify animal behaviour estimate biodiversity like camera‐trap images, audio recordings videos. We demonstrate beneficial most disciplines, including applied contexts, management conservation. also common questions about how when use what steps required create network, which tools available help, requirements terms data computer power. provide guidelines, recommendations resources, reference flowchart help get started learning. argue at time automatic monitoring populations ecosystems generates vast amount cannot effectively processed by humans anymore, could become powerful tool for ecologists.

Language: Английский

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Drought forecasting using feed-forward recursive neural network

Ecological Modelling, Journal Year: 2006, Volume and Issue: 198(1-2), P. 127 - 138

Published: June 15, 2006

Language: Английский

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Neural network and genetic programming for modelling coastal algal blooms

International Journal of Environment and Pollution, Journal Year: 2006, Volume and Issue: 28(3/4), P. 223 - 223

Published: Jan. 1, 2006

In the recent past, machine learning (ML) techniques such as artificial neural networks (ANN) have been increasingly used to model algal bloom dynamics.In present paper, along with ANN, we select genetic programming (GP) for modelling and prediction of blooms in Tolo Harbour, Hong Kong.The study weights trained ANN also GP-evolved equations shows that they correctly identify ecologically significant variables.Analysis various GP scenarios indicates good predictions longterm trends biomass can be obtained using only chlorophyll-a input.The results indicate use biweekly data simulate long-term reasonably well, but it is not ideally suited give short-term predictions.

Language: Английский

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Deep learning as a tool for ecology and evolution

Methods in Ecology and Evolution, Journal Year: 2022, Volume and Issue: 13(8), P. 1640 - 1660

Published: May 30, 2022

Abstract Deep learning is driving recent advances behind many everyday technologies, including speech and image recognition, natural language processing autonomous driving. It also gaining popularity in biology, where it has been used for automated species identification, environmental monitoring, ecological modelling, behavioural studies, DNA sequencing population genetics phylogenetics, among other applications. relies on artificial neural networks predictive modelling excels at recognizing complex patterns. In this review we synthesize 818 studies using deep the context of ecology evolution to give a discipline‐wide perspective necessary promote rethinking inference approaches field. We provide an introduction machine contrast with mechanistic inference, followed by gentle primer learning. applications discuss its limitations efforts overcome them. practical biologists interested their toolkit identify possible future find that being rapidly adopted evolution, 589 (64%) published since beginning 2019. Most use convolutional (496 studies) supervised identification but tasks molecular data, sounds, data or video as input. More sophisticated uses biology are appear. Operating within paradigm, can be viewed alternative modelling. desirable properties good performance scaling increasing complexity, while posing unique challenges such sensitivity bias input data. expect rapid adoption will continue, especially automation biodiversity monitoring discovery from genetic Increased unsupervised visualization clusters gaps, simplification multi‐step analysis pipelines, integration into graduate postgraduate training all likely near future.

Language: Английский

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Machine-learning paradigms for selecting ecologically significant input variables

Engineering Applications of Artificial Intelligence, Journal Year: 2007, Volume and Issue: 20(6), P. 735 - 744

Published: Jan. 17, 2007

Language: Английский

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A convolutional neural network regression for quantifying cyanobacteria using hyperspectral imagery

Remote Sensing of Environment, Journal Year: 2019, Volume and Issue: 233, P. 111350 - 111350

Published: Aug. 10, 2019

Language: Английский

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Prediction of Chlorophyll-a Concentrations in the Nakdong River Using Machine Learning Methods

Water, Journal Year: 2020, Volume and Issue: 12(6), P. 1822 - 1822

Published: June 25, 2020

Many studies have attempted to predict chlorophyll-a concentrations using multiple regression models and validating them with a hold-out technique. In this study commonly used machine learning models, such as Support Vector Regression, Bagging, Random Forest, Extreme Gradient Boosting (XGBoost), Recurrent Neural Network (RNN), Long–Short-Term Memory (LSTM), are build new model in the Nakdong River, Korea. We employed 1–step ahead recursive prediction reflect characteristics of time series data. order increase accuracy, construction was based on forward variable selection. The fitted were validated by means cumulative rolling window learning, opposed hold–out best results obtained when concentration predicted combining RNN method. suggest that selection explanatory variables important processes for improving its performance.

Language: Английский

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A remote wireless system for water quality online monitoring in intensive fish culture

Computers and Electronics in Agriculture, Journal Year: 2009, Volume and Issue: 71, P. S3 - S9

Published: Nov. 13, 2009

Language: Английский

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Hydrological changes of DOM composition and biodegradability of rivers in temperate monsoon climates

Journal of Hydrology, Journal Year: 2016, Volume and Issue: 540, P. 538 - 548

Published: June 27, 2016

Language: Английский

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