Publication:
Development of In-situ Sensing System and Classification of Water Quality using Machine Learning Approach
Development of In-situ Sensing System and Classification of Water Quality using Machine Learning Approach
| cris.author.scopus-author-id | 57209073616 | |
| cris.author.scopus-author-id | 57763318600 | |
| cris.author.scopus-author-id | 36471236100 | |
| cris.virtual.department | Universiti Malaysia Perlis | |
| cris.virtualsource.department | 54e269b4-1687-4fb3-b89b-45540df8b38e | |
| dc.contributor.author | Abdul Syafiq Abdull Sukor | |
| dc.contributor.author | Mohamad Naim Muhamad | |
| dc.contributor.author | Ab Wahab M.N. | |
| dc.date.accessioned | 2024-09-27T01:09:51Z | |
| dc.date.available | 2024-09-27T01:09:51Z | |
| dc.date.issued | 2022-01-01 | |
| dc.description.abstract | Quality of water applied to the agriculture sector is one of the factors for agriculture farming to be successful. The use of bad quality irrigation water can cause soil problems. In general, determining water quality model is one of the many interests as it can be used to classify the conditions of water. This project focuses on developing the in-situ sensing system of water quality sensors that can detect parameters of water quality such as pH level, electric conductivity, temperature and total dissolved solid. To validate the approach, there are three types of water samples in a dataset that was collected which include water pipes, soap water and drain water. The types of machine learning models used for classification process are Artificial Neural Network (ANN), Support Vector Machine (SVM), and Decision Tree. The performance showed that SVM model was the highest, ANN was intermediate, and Decision Tree was the lowest. This shows that the SVM model of machine learning approach is the most suitable to be used as the classification model to classify the status of water quality. | |
| dc.identifier.doi | 10.1109/CSPA55076.2022.9781984 | |
| dc.identifier.isbn | [9781665485296] | |
| dc.identifier.scopus | 2-s2.0-85132762865 | |
| dc.identifier.uri | https://hdl.handle.net/20.500.14170/3919 | |
| dc.relation.funding | Ministry of Higher Education, Malaysia | |
| dc.relation.grantno | FRGS/1/2020/STG06/USM/02/4 | |
| dc.relation.ispartof | 2022 IEEE 18th International Colloquium on Signal Processing and Applications, CSPA 2022 - Proceeding | |
| dc.relation.ispartofseries | 2022 IEEE 18th International Colloquium on Signal Processing and Applications, CSPA 2022 - Proceeding | |
| dc.subject | artificial neural network | decision tree | sensors | support vector machine | water quality | |
| dc.title | Development of In-situ Sensing System and Classification of Water Quality using Machine Learning Approach | |
| dc.type | Conference Proceeding | |
| dspace.entity.type | Publication | |
| oaire.citation.endPage | 385 | |
| oaire.citation.startPage | 382 | |
| oairecerif.affiliation.orgunit | Universiti Malaysia Perlis | |
| oairecerif.affiliation.orgunit | Universiti Malaysia Perlis | |
| oairecerif.affiliation.orgunit | Universiti Sains Malaysia | |
| oairecerif.author.affiliation | Universiti Malaysia Perlis | |
| oairecerif.author.affiliation | Universiti Malaysia Perlis | |
| oairecerif.author.affiliation | #PLACEHOLDER_PARENT_METADATA_VALUE# | |
| person.identifier.orcid | #PLACEHOLDER_PARENT_METADATA_VALUE# | |
| person.identifier.orcid | #PLACEHOLDER_PARENT_METADATA_VALUE# | |
| person.identifier.orcid | #PLACEHOLDER_PARENT_METADATA_VALUE# | |
| person.identifier.scopus-author-id | 57209073616 | |
| person.identifier.scopus-author-id | 57763318600 | |
| person.identifier.scopus-author-id | 36471236100 |