Long-term forecasting of climatic parameters using parametric and non-parametric stochastic modelling
Climatic parameters fluctuate dynamically and their turbulences become more significant as the influence of the climate change increases. A robust model that is able to factor in the recent climate change for long-term climatic parameters forecasting is desired to strategically plan for future anthr...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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EDP Sciences
2022-01-01
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| Series: | E3S Web of Conferences |
| Online Access: | https://www.e3s-conferences.org/articles/e3sconf/pdf/2022/14/e3sconf_iccee2022_05013.pdf |
| _version_ | 1811332260941529088 |
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| author | Chia Min Yan Huang Yuk Feng Koo Chai Hoon Chong Zheng Rong |
| author_facet | Chia Min Yan Huang Yuk Feng Koo Chai Hoon Chong Zheng Rong |
| author_sort | Chia Min Yan |
| collection | DOAJ |
| description | Climatic parameters fluctuate dynamically and their turbulences become more significant as the influence of the climate change increases. A robust model that is able to factor in the recent climate change for long-term climatic parameters forecasting is desired to strategically plan for future anthropogenic activities. In this study, two stochastic time series model, namely the seasonal auto-regressive integrated moving average (SARIMA) model and the artificial neural network (ANN) model are used to predict monthly mean temperature (Tmean), relative humidity (RH), wind speed (u) and pan evaporation (Epan) up to 12 months ahead. This study is conducted using data collected from three meteorological stations in the northern region of the Peninsular Malaysia. The stochastic models forecasted the Tmean with the highest accuracy, followed by RH, u and Epan. Besides, despite the increasing time step (from 1 to 12 months), the accuracy of the models remain consistent. However, both of the models are susceptible to the occurrence of extreme climates. In general, the SARIMA model performs better than the ANN model, probably attributed to its ability to consider the seasonality of the climatic data rather than depending solely on black-box computation. |
| first_indexed | 2024-04-13T16:33:57Z |
| format | Article |
| id | doaj.art-4a07d88d167146f382c8553f03e4a7b9 |
| institution | Directory Open Access Journal |
| issn | 2267-1242 |
| language | English |
| last_indexed | 2024-04-13T16:33:57Z |
| publishDate | 2022-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | E3S Web of Conferences |
| spelling | doaj.art-4a07d88d167146f382c8553f03e4a7b92022-12-22T02:39:29ZengEDP SciencesE3S Web of Conferences2267-12422022-01-013470501310.1051/e3sconf/202234705013e3sconf_iccee2022_05013Long-term forecasting of climatic parameters using parametric and non-parametric stochastic modellingChia Min Yan0Huang Yuk Feng1Koo Chai Hoon2Chong Zheng Rong3Department of Civil Engineering, Lee Kong Chian Faculty of Engineering and Science, Universiti Tunku Abdul RahmanDepartment of Civil Engineering, Lee Kong Chian Faculty of Engineering and Science, Universiti Tunku Abdul RahmanDepartment of Civil Engineering, Lee Kong Chian Faculty of Engineering and Science, Universiti Tunku Abdul RahmanDepartment of Civil Engineering, Lee Kong Chian Faculty of Engineering and Science, Universiti Tunku Abdul RahmanClimatic parameters fluctuate dynamically and their turbulences become more significant as the influence of the climate change increases. A robust model that is able to factor in the recent climate change for long-term climatic parameters forecasting is desired to strategically plan for future anthropogenic activities. In this study, two stochastic time series model, namely the seasonal auto-regressive integrated moving average (SARIMA) model and the artificial neural network (ANN) model are used to predict monthly mean temperature (Tmean), relative humidity (RH), wind speed (u) and pan evaporation (Epan) up to 12 months ahead. This study is conducted using data collected from three meteorological stations in the northern region of the Peninsular Malaysia. The stochastic models forecasted the Tmean with the highest accuracy, followed by RH, u and Epan. Besides, despite the increasing time step (from 1 to 12 months), the accuracy of the models remain consistent. However, both of the models are susceptible to the occurrence of extreme climates. In general, the SARIMA model performs better than the ANN model, probably attributed to its ability to consider the seasonality of the climatic data rather than depending solely on black-box computation.https://www.e3s-conferences.org/articles/e3sconf/pdf/2022/14/e3sconf_iccee2022_05013.pdf |
| spellingShingle | Chia Min Yan Huang Yuk Feng Koo Chai Hoon Chong Zheng Rong Long-term forecasting of climatic parameters using parametric and non-parametric stochastic modelling E3S Web of Conferences |
| title | Long-term forecasting of climatic parameters using parametric and non-parametric stochastic modelling |
| title_full | Long-term forecasting of climatic parameters using parametric and non-parametric stochastic modelling |
| title_fullStr | Long-term forecasting of climatic parameters using parametric and non-parametric stochastic modelling |
| title_full_unstemmed | Long-term forecasting of climatic parameters using parametric and non-parametric stochastic modelling |
| title_short | Long-term forecasting of climatic parameters using parametric and non-parametric stochastic modelling |
| title_sort | long term forecasting of climatic parameters using parametric and non parametric stochastic modelling |
| url | https://www.e3s-conferences.org/articles/e3sconf/pdf/2022/14/e3sconf_iccee2022_05013.pdf |
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