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About Air Pollution Modelling With Deep Learning A Review
This document reviews the use of deep learning techniques for air pollution modelling. It provides an overview of deep learning concepts and how they can be applied to develop models for predicting air pollution levels and locating pollutant sources. The review discusses how deep learning differs from traditional machine learning and artificial intelligence approaches in its ability to handle large datasets and solve problems simultaneously across multiple layers. It outlines the typical steps involved in developing a deep learning model for air pollution, including collecting and preprocessing data, selecting an algorithm, training and testing the model. Several recent studies that have applied deep learning methods like LSTM, STDL and CNNs to air quality prediction are also mentioned.
Detailed Information
| Author: | ['lilu'] |
|---|---|
| Publication Year: | 2021 |
| Pages: | 6 |
| Language: | English |
| Format: | |
| Price: | FREE |
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