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: | , , , , |
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| Format: | Article |
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Elsevier B.V.
2023
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| Subjects: | |
| Online Access: | http://eprints.utm.my/107505/ http://dx.doi.org/10.1016/j.uclim.2023.101486 |
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| Summary: | 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%. |
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