Estimation of missing streamflow data using various artificial intelligence methods in peninsular Malaysia
Missing streamflow data is a common issue in Peninsular Malaysia, as the technologies used in hydrological studies often fail to collect data accurately. Additionally, conventional methods are still widely used in the region, which are less accurate compared to artificial intelligence (AI) methods i...
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my.uniten.dspace-362202025-03-03T15:41:37Z Estimation of missing streamflow data using various artificial intelligence methods in peninsular Malaysia Ng J.L. Huang Y.F. Chong A.H. Ahmed A.N. Syamsunurc D. 57192698412 55807263900 58739268700 57214837520 59465959500 Fuzzy neural networks Mean square error Artificial intelligence methods Artificial neural network Artificial neuro-fuzzy inference system Auto regressive integrated moving average Autoregressive integrated moving average(ARIMA) Data estimation Malaysia Missing streamflow data estimation Neural-networks Neuro-fuzzy inference systems Missing streamflow data is a common issue in Peninsular Malaysia, as the technologies used in hydrological studies often fail to collect data accurately. Additionally, conventional methods are still widely used in the region, which are less accurate compared to artificial intelligence (AI) methods in estimating missing streamflow data. Therefore, this study aims to estimate the missing streamflow data from 11 stations in Peninsular Malaysia by using different AI methods and determine the most appropriate method. Four homogeneity tests were applied to check the quality of data, and the results of the tests indicated that the streamflow data in most stations were homogenous. Two AI methods were applied in this study, which were artificial neural network and artificial neuro-fuzzy inference systems (ANFIS). The proposed AI methods were compared with five different conventional methods. All streamflow missing data, constituting 30% of data from each year were estimated on a daily time scale, and evaluated using root mean square error, mean absolute error and correlation coefficient values. The results indicated that ANFIS was the best due to its learning abilities and the fuzzy inference systems, which enable it to handle complicated input? output patterns and provide highly accurate estimation results. ? 2024 The Authors. Final 2025-03-03T07:41:37Z 2025-03-03T07:41:37Z 2024 Article 10.2166/wpt.2024.265 2-s2.0-85211481243 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85211481243&doi=10.2166%2fwpt.2024.265&partnerID=40&md5=bd2f334a415c93741f0c8a3e15ba9ee4 https://irepository.uniten.edu.my/handle/123456789/36220 19 11 4338 4354 All Open Access; Gold Open Access IWA Publishing Scopus |
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Fuzzy neural networks Mean square error Artificial intelligence methods Artificial neural network Artificial neuro-fuzzy inference system Auto regressive integrated moving average Autoregressive integrated moving average(ARIMA) Data estimation Malaysia Missing streamflow data estimation Neural-networks Neuro-fuzzy inference systems |
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Fuzzy neural networks Mean square error Artificial intelligence methods Artificial neural network Artificial neuro-fuzzy inference system Auto regressive integrated moving average Autoregressive integrated moving average(ARIMA) Data estimation Malaysia Missing streamflow data estimation Neural-networks Neuro-fuzzy inference systems Ng J.L. Huang Y.F. Chong A.H. Ahmed A.N. Syamsunurc D. Estimation of missing streamflow data using various artificial intelligence methods in peninsular Malaysia |
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Missing streamflow data is a common issue in Peninsular Malaysia, as the technologies used in hydrological studies often fail to collect data accurately. Additionally, conventional methods are still widely used in the region, which are less accurate compared to artificial intelligence (AI) methods in estimating missing streamflow data. Therefore, this study aims to estimate the missing streamflow data from 11 stations in Peninsular Malaysia by using different AI methods and determine the most appropriate method. Four homogeneity tests were applied to check the quality of data, and the results of the tests indicated that the streamflow data in most stations were homogenous. Two AI methods were applied in this study, which were artificial neural network and artificial neuro-fuzzy inference systems (ANFIS). The proposed AI methods were compared with five different conventional methods. All streamflow missing data, constituting 30% of data from each year were estimated on a daily time scale, and evaluated using root mean square error, mean absolute error and correlation coefficient values. The results indicated that ANFIS was the best due to its learning abilities and the fuzzy inference systems, which enable it to handle complicated input? output patterns and provide highly accurate estimation results. ? 2024 The Authors. |
| author2 |
57192698412 |
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57192698412 Ng J.L. Huang Y.F. Chong A.H. Ahmed A.N. Syamsunurc D. |
| format |
Article |
| author |
Ng J.L. Huang Y.F. Chong A.H. Ahmed A.N. Syamsunurc D. |
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Ng J.L. |
| title |
Estimation of missing streamflow data using various artificial intelligence methods in peninsular Malaysia |
| title_short |
Estimation of missing streamflow data using various artificial intelligence methods in peninsular Malaysia |
| title_full |
Estimation of missing streamflow data using various artificial intelligence methods in peninsular Malaysia |
| title_fullStr |
Estimation of missing streamflow data using various artificial intelligence methods in peninsular Malaysia |
| title_full_unstemmed |
Estimation of missing streamflow data using various artificial intelligence methods in peninsular Malaysia |
| title_sort |
estimation of missing streamflow data using various artificial intelligence methods in peninsular malaysia |
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IWA Publishing |
| publishDate |
2025 |
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1825816099802841088 |
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13.244109 |