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Deep learning method for minimizing water pollution and air pollution in urban environment

Rapid urbanization impacts water quality because contaminants from the urban environment accumulate in the water and pollute it and because there is more rivalry for water among municipalities, businesses, and other sectors such as farming. A change in the microclimate, fluid mechanics, geomorphic,...

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Main Authors:	Zhu, Lingling, Mohamad Husny, Zuhra Junaida, Samsudin, Noor Aimran, Xu, HaiPeng, Han, Chongyong
Format:	Article
Published:	Elsevier B.V. 2023
Subjects:	H Social Sciences (General) HT101-395 Sociology, Urban TH434-437 Quantity surveying
Online Access:	http://eprints.utm.my/107505/ http://dx.doi.org/10.1016/j.uclim.2023.101486
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id	my.utm.107505
record_format	eprints
spelling	my.utm.1075052024-09-23T03:19:42Z http://eprints.utm.my/107505/ Deep learning method for minimizing water pollution and air pollution in urban environment Zhu, Lingling Mohamad Husny, Zuhra Junaida Samsudin, Noor Aimran Xu, HaiPeng Han, Chongyong H Social Sciences (General) HT101-395 Sociology, Urban TH434-437 Quantity surveying Rapid urbanization impacts water quality because contaminants from the urban environment accumulate in the water and pollute it and because there is more rivalry for water among municipalities, businesses, and other sectors such as farming. A change in the microclimate, fluid mechanics, geomorphic, ecological, or biogeochemical conditions will impact the water's quantity and quality. There is a reduction in the groundwater because of the difficulty that water has soaked into the earth as more roads are built. When the rain washes over impervious buildings like roadways and roofs, it leaves excessive pollution in water bodies. Both people and aquatic life may be at risk from the increased water pollution. This paper uses deep learning methods such as Convolutional Neural Networks (CNN) and Long-Short Term Memory (LSTM) to classify water quality. Next, it identifies the air quality in Urban Development (Conv. LSTM). The convolutional LSTMs use convolutional layers and the recurrent connections found in LSTMs. This allows the model to capture spatial dependencies in the input data in addition to the temporal dependencies captured by the recurrent connections. We also use thorough exploratory analysis to investigate the various beach habitats and the kinds of trash discovered in multiple places. Lowering water pollution and raising air quality are both strategies that can be employed to ensure sustainable urban development. The performance metrics such as accuracy, recall, precision, and F1-score are evaluated and classify the water pollution efficiently. In the water pollution dataset, the algorithms of RNN 65%, DBN 78%, LSTM 82%, and the proposed work of Conv.LSTM 92%. Similarly, for the air pollution dataset, the algorithms of RNN 60%, DBN 75%, LSTM 80%, and the proposed work of Conv.LSTM 91%. Elsevier B.V. 2023-05 Article PeerReviewed Zhu, Lingling and Mohamad Husny, Zuhra Junaida and Samsudin, Noor Aimran and Xu, HaiPeng and Han, Chongyong (2023) Deep learning method for minimizing water pollution and air pollution in urban environment. Urban Climate, 49 (NA). NA. ISSN 2212-0955 http://dx.doi.org/10.1016/j.uclim.2023.101486 DOI:10.1016/j.uclim.2023.101486
institution	Universiti Teknologi Malaysia
building	UTM Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Teknologi Malaysia
content_source	UTM Institutional Repository
url_provider	http://eprints.utm.my/
topic	H Social Sciences (General) HT101-395 Sociology, Urban TH434-437 Quantity surveying
spellingShingle	H Social Sciences (General) HT101-395 Sociology, Urban TH434-437 Quantity surveying Zhu, Lingling Mohamad Husny, Zuhra Junaida Samsudin, Noor Aimran Xu, HaiPeng Han, Chongyong Deep learning method for minimizing water pollution and air pollution in urban environment
description	Rapid urbanization impacts water quality because contaminants from the urban environment accumulate in the water and pollute it and because there is more rivalry for water among municipalities, businesses, and other sectors such as farming. A change in the microclimate, fluid mechanics, geomorphic, ecological, or biogeochemical conditions will impact the water's quantity and quality. There is a reduction in the groundwater because of the difficulty that water has soaked into the earth as more roads are built. When the rain washes over impervious buildings like roadways and roofs, it leaves excessive pollution in water bodies. Both people and aquatic life may be at risk from the increased water pollution. This paper uses deep learning methods such as Convolutional Neural Networks (CNN) and Long-Short Term Memory (LSTM) to classify water quality. Next, it identifies the air quality in Urban Development (Conv. LSTM). The convolutional LSTMs use convolutional layers and the recurrent connections found in LSTMs. This allows the model to capture spatial dependencies in the input data in addition to the temporal dependencies captured by the recurrent connections. We also use thorough exploratory analysis to investigate the various beach habitats and the kinds of trash discovered in multiple places. Lowering water pollution and raising air quality are both strategies that can be employed to ensure sustainable urban development. The performance metrics such as accuracy, recall, precision, and F1-score are evaluated and classify the water pollution efficiently. In the water pollution dataset, the algorithms of RNN 65%, DBN 78%, LSTM 82%, and the proposed work of Conv.LSTM 92%. Similarly, for the air pollution dataset, the algorithms of RNN 60%, DBN 75%, LSTM 80%, and the proposed work of Conv.LSTM 91%.
format	Article
author	Zhu, Lingling Mohamad Husny, Zuhra Junaida Samsudin, Noor Aimran Xu, HaiPeng Han, Chongyong
author_facet	Zhu, Lingling Mohamad Husny, Zuhra Junaida Samsudin, Noor Aimran Xu, HaiPeng Han, Chongyong
author_sort	Zhu, Lingling
title	Deep learning method for minimizing water pollution and air pollution in urban environment
title_short	Deep learning method for minimizing water pollution and air pollution in urban environment
title_full	Deep learning method for minimizing water pollution and air pollution in urban environment
title_fullStr	Deep learning method for minimizing water pollution and air pollution in urban environment
title_full_unstemmed	Deep learning method for minimizing water pollution and air pollution in urban environment
title_sort	deep learning method for minimizing water pollution and air pollution in urban environment
publisher	Elsevier B.V.
publishDate	2023
url	http://eprints.utm.my/107505/ http://dx.doi.org/10.1016/j.uclim.2023.101486
_version_	1811681209603850240
score	13.211869

Deep learning method for minimizing water pollution and air pollution in urban environment

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