Time series trend analysis and forecasting of climate variability using deep learning in Thailand
Climate variability, trend analysis, and accurate forecasting are vital in a country's effective water resource management and strategic planning. Precipitation and temperature are critical indicators for assessing the effects of climate change (CC) variability. Thailand is sensitive to climati...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2024-12-01
|
| Series: | Results in Engineering |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2590123024012520 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1846115875483025408 |
|---|---|
| author | Muhammad Waqas Usa Wannasingha Humphries Phyo Thandar Hlaing |
| author_facet | Muhammad Waqas Usa Wannasingha Humphries Phyo Thandar Hlaing |
| author_sort | Muhammad Waqas |
| collection | DOAJ |
| description | Climate variability, trend analysis, and accurate forecasting are vital in a country's effective water resource management and strategic planning. Precipitation and temperature are critical indicators for assessing the effects of climate change (CC) variability. Thailand is sensitive to climatic variations, affecting the socio-economic conditions. This study quantifies climate variability and trends analysis based on precipitation, mean temperature (Tmean), and daily temperature range (DTR) across five climatic regions of Thailand. The results indicate regional variations: in the Central and Southern regions, there are increases in precipitation and warming temperatures, with substantial upward trends in annual precipitation (0.093 mm/year and 0.148 mm/year) and Tmean (0.002 °C/year). The Eastern and Northeastern regions display complex patterns with increased precipitation and temperatures. Also, DTR trends across regions show a decrease in temperature variability. The study offers new insights into forecasting climate variables for the different regions of Thailand between 2023 and 2028 b y utilizing two deep learning (DL) algorithms: Wavelet-CNN-LSTM and Wavelet-LSTM, which reveals high predictive accuracy. For precipitation forecasting, Wavelet-CNN-LSTM showed higher performance in the eastern region (R2 = 0.83) and comparative efficiency in other regions. Both models faced challenges in precipitation forecasting in the northeastern and southern regions. These models performed efficiently for the DTR forecast, especially in the northern region (R2 = 0.87 and 0.86). For Tmean, both models perform similarly with high R2 (0.57–0.87) across all regions, suggesting a substantial model accuracy. Wavelet-CNN-LSTM provides consistent performance for DTR and Tmean forecasting. These findings underscore the importance of climate analysis and refined forecasting models. |
| format | Article |
| id | doaj-art-452677448be54d28aaa132dd666f00a6 |
| institution | Kabale University |
| issn | 2590-1230 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Results in Engineering |
| spelling | doaj-art-452677448be54d28aaa132dd666f00a62024-12-19T10:57:44ZengElsevierResults in Engineering2590-12302024-12-0124102997Time series trend analysis and forecasting of climate variability using deep learning in ThailandMuhammad Waqas0Usa Wannasingha Humphries1Phyo Thandar Hlaing2The Joint Graduate School of Energy and Environment (JGSEE), King Mongkut's University of Technology Thonburi (KMUTT), Bangkok, 10140, Thailand; Center of Excellence on Energy Technology and Environment (CEE), Ministry of Higher Education, Science, Research and Innovation, Bangkok, ThailandDepartment of Mathematics, Faculty of Science, King Mongkut's University of Technology Thonburi (KMUTT), Bangkok, 10140, Thailand; Corresponding author.The Joint Graduate School of Energy and Environment (JGSEE), King Mongkut's University of Technology Thonburi (KMUTT), Bangkok, 10140, Thailand; Center of Excellence on Energy Technology and Environment (CEE), Ministry of Higher Education, Science, Research and Innovation, Bangkok, ThailandClimate variability, trend analysis, and accurate forecasting are vital in a country's effective water resource management and strategic planning. Precipitation and temperature are critical indicators for assessing the effects of climate change (CC) variability. Thailand is sensitive to climatic variations, affecting the socio-economic conditions. This study quantifies climate variability and trends analysis based on precipitation, mean temperature (Tmean), and daily temperature range (DTR) across five climatic regions of Thailand. The results indicate regional variations: in the Central and Southern regions, there are increases in precipitation and warming temperatures, with substantial upward trends in annual precipitation (0.093 mm/year and 0.148 mm/year) and Tmean (0.002 °C/year). The Eastern and Northeastern regions display complex patterns with increased precipitation and temperatures. Also, DTR trends across regions show a decrease in temperature variability. The study offers new insights into forecasting climate variables for the different regions of Thailand between 2023 and 2028 b y utilizing two deep learning (DL) algorithms: Wavelet-CNN-LSTM and Wavelet-LSTM, which reveals high predictive accuracy. For precipitation forecasting, Wavelet-CNN-LSTM showed higher performance in the eastern region (R2 = 0.83) and comparative efficiency in other regions. Both models faced challenges in precipitation forecasting in the northeastern and southern regions. These models performed efficiently for the DTR forecast, especially in the northern region (R2 = 0.87 and 0.86). For Tmean, both models perform similarly with high R2 (0.57–0.87) across all regions, suggesting a substantial model accuracy. Wavelet-CNN-LSTM provides consistent performance for DTR and Tmean forecasting. These findings underscore the importance of climate analysis and refined forecasting models.http://www.sciencedirect.com/science/article/pii/S2590123024012520Climate changeTrend analysisClimate variabilityDeep learningPrecipitation forecasting |
| spellingShingle | Muhammad Waqas Usa Wannasingha Humphries Phyo Thandar Hlaing Time series trend analysis and forecasting of climate variability using deep learning in Thailand Results in Engineering Climate change Trend analysis Climate variability Deep learning Precipitation forecasting |
| title | Time series trend analysis and forecasting of climate variability using deep learning in Thailand |
| title_full | Time series trend analysis and forecasting of climate variability using deep learning in Thailand |
| title_fullStr | Time series trend analysis and forecasting of climate variability using deep learning in Thailand |
| title_full_unstemmed | Time series trend analysis and forecasting of climate variability using deep learning in Thailand |
| title_short | Time series trend analysis and forecasting of climate variability using deep learning in Thailand |
| title_sort | time series trend analysis and forecasting of climate variability using deep learning in thailand |
| topic | Climate change Trend analysis Climate variability Deep learning Precipitation forecasting |
| url | http://www.sciencedirect.com/science/article/pii/S2590123024012520 |
| work_keys_str_mv | AT muhammadwaqas timeseriestrendanalysisandforecastingofclimatevariabilityusingdeeplearninginthailand AT usawannasinghahumphries timeseriestrendanalysisandforecastingofclimatevariabilityusingdeeplearninginthailand AT phyothandarhlaing timeseriestrendanalysisandforecastingofclimatevariabilityusingdeeplearninginthailand |