Identifying high influence parameters using Genetic Algorithm (GA) chromosomes for water consumption
Severe uncertainties climate changes course flood and droughts disaster have made clean water precious for domestic consumption. Thus, securing clean water is important. Wastage of water comes from water consumption such as from household usage. However, monitoring water cons...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | en |
| Published: |
Penerbit UTHM
2021
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| Subjects: | |
| Online Access: | https://umpir.ump.edu.my/id/eprint/46390/1/Identifying%20high%20influence%20parameters%20using%20genetic%20algorithm.pdf https://doi.org/10.30880/ijie.2021.13.05.018 https://umpir.ump.edu.my/id/eprint/46390/ |
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| Summary: | Severe uncertainties climate changes course flood and droughts disaster have made clean water precious for domestic consumption. Thus, securing clean water is important. Wastage of water comes from water consumption such as from household usage. However, monitoring water consumption from household usage is tedious and time consuming. This work utilized Genetic Algorithm (GA) to optimize the coefficient of micro-components of water consumption (CMWC) values to determine high influential household routine parameters. Nine household parameters have been investigated namely, bath/shower, personal hygiene, flush toilet, wash cloth by hand, wash cloth by washing machine, food preparation, water plant, washing car and miscellaneous.These parameters are encoded as a chromosome data in GA to incorporate the CMWC values. The aim is to minimize the residential water consumption estimation error rates and subsequently enabling increased accuracy towards estimating and classifying the amount of residential water consumption. Data average monthly water consumption were collected from 80 households in Seremban. Water consumption has been categorized into three groups of low (L-PDWC), medium (M-PDWC) and high (H-PDWC). Comparison was made betweenper capita water consumption (PCC) and Domestic Water Consumption via Genetic Algorithm (DWC-GA) error rate’s values. The results are as follows; PCC method’s error rates of 9.49 and DWC-GA error rate is 1.05. |
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