Ensemble Learning-Based Soft Computing Approach for Future Precipitation Analysis
This study integrated the strengths of ensemble learning and soft computing to develop a future regional rainfall model for evaluating the complex characteristics of island precipitation. Soft computing uses the well-developed adaptive neuro-fuzzy inference system, which has been successfully applie...
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MDPI AG
2025-06-01
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| Series: | Atmosphere |
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| Online Access: | https://www.mdpi.com/2073-4433/16/6/669 |
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| author | Shiu-Shin Lin Kai-Yang Zhu Chen-Yu Wang Chou-Ping Yang Ming-Yi Liu |
| author_facet | Shiu-Shin Lin Kai-Yang Zhu Chen-Yu Wang Chou-Ping Yang Ming-Yi Liu |
| author_sort | Shiu-Shin Lin |
| collection | DOAJ |
| description | This study integrated the strengths of ensemble learning and soft computing to develop a future regional rainfall model for evaluating the complex characteristics of island precipitation. Soft computing uses the well-developed adaptive neuro-fuzzy inference system, which has been successfully applied in atmospheric hydrology and combines the features of neural networks and fuzzy logic. This combination enables artificial intelligence (AI) to effectively represent reasoning derived from complex data and expert experience. Due to the multiple atmospheric and hydrological factors that influence rainfall, the nonlinear interrelations among them are highly intricate. Nonlinear principal component analysis can extract nonlinear features from the data, reduce dimensionality, and minimize the adverse effects of data noise and excessive input factors on soft computing, which may otherwise result in poor model performance. Ultimately, ensemble learning enhances prediction accuracy and reduces uncertainty. This study used Tamsui and Kaohsiung in Taiwan as case study locations. Historical monthly rainfall data (January 1950 to December 2005) from Tamsui Station and Kaohsiung Station of the Central Weather Administration, along with historical and varied emission scenario data (RCP 4.5 and RCP 8.5) from three AR5 GCM models (ACCESS 1.0, CSIRO-MK3.6.0, MRI-CGCM3), were used to evaluate future regional rainfall trends and uncertainties through the method proposed in this study. The research findings indicate the following: (1) Ensemble learning results demonstrate that all examined general circulation models effectively simulate historical rainfall trends. (2) The average rainfall trends under the RCP 4.5 emission scenario are generally consistent with historical rainfall trends. (3) The exceedance probabilities of future rainfall during the mid-term (2061–2080) and long-term (2081–2100) suggest that Kaohsiung may experience precipitation events with higher rainfall than historical data during dry seasons (October to April of next year), while Tamsui Station may exhibit greater variability in terms of exceedance probabilities. (4) Under both the RCP 4.5 and RCP 8.5 emission scenarios, the percentage changes in future rainfall variability at Kaohsiung Station during dry seasons are higher than those during wet seasons (May to September), indicating an increased risk of extreme precipitation events during dry seasons. |
| format | Article |
| id | doaj-art-e2c1fe511e1247568d40edb3bf5cd280 |
| institution | OA Journals |
| issn | 2073-4433 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Atmosphere |
| spelling | doaj-art-e2c1fe511e1247568d40edb3bf5cd2802025-08-20T02:24:39ZengMDPI AGAtmosphere2073-44332025-06-0116666910.3390/atmos16060669Ensemble Learning-Based Soft Computing Approach for Future Precipitation AnalysisShiu-Shin Lin0Kai-Yang Zhu1Chen-Yu Wang2Chou-Ping Yang3Ming-Yi Liu4Department of Civil Engineering, Chung Yuan Christian University, Taoyuan City 320314, TaiwanDepartment of Civil Engineering, Chung Yuan Christian University, Taoyuan City 320314, TaiwanDepartment of Civil Engineering, Chung Yuan Christian University, Taoyuan City 320314, TaiwanGraduate Institute of Landscape Architecture and Recreation Management, National Pingtung University of Science and Technology, Pingtung 912301, TaiwanDepartment of Civil Engineering, Chung Yuan Christian University, Taoyuan City 320314, TaiwanThis study integrated the strengths of ensemble learning and soft computing to develop a future regional rainfall model for evaluating the complex characteristics of island precipitation. Soft computing uses the well-developed adaptive neuro-fuzzy inference system, which has been successfully applied in atmospheric hydrology and combines the features of neural networks and fuzzy logic. This combination enables artificial intelligence (AI) to effectively represent reasoning derived from complex data and expert experience. Due to the multiple atmospheric and hydrological factors that influence rainfall, the nonlinear interrelations among them are highly intricate. Nonlinear principal component analysis can extract nonlinear features from the data, reduce dimensionality, and minimize the adverse effects of data noise and excessive input factors on soft computing, which may otherwise result in poor model performance. Ultimately, ensemble learning enhances prediction accuracy and reduces uncertainty. This study used Tamsui and Kaohsiung in Taiwan as case study locations. Historical monthly rainfall data (January 1950 to December 2005) from Tamsui Station and Kaohsiung Station of the Central Weather Administration, along with historical and varied emission scenario data (RCP 4.5 and RCP 8.5) from three AR5 GCM models (ACCESS 1.0, CSIRO-MK3.6.0, MRI-CGCM3), were used to evaluate future regional rainfall trends and uncertainties through the method proposed in this study. The research findings indicate the following: (1) Ensemble learning results demonstrate that all examined general circulation models effectively simulate historical rainfall trends. (2) The average rainfall trends under the RCP 4.5 emission scenario are generally consistent with historical rainfall trends. (3) The exceedance probabilities of future rainfall during the mid-term (2061–2080) and long-term (2081–2100) suggest that Kaohsiung may experience precipitation events with higher rainfall than historical data during dry seasons (October to April of next year), while Tamsui Station may exhibit greater variability in terms of exceedance probabilities. (4) Under both the RCP 4.5 and RCP 8.5 emission scenarios, the percentage changes in future rainfall variability at Kaohsiung Station during dry seasons are higher than those during wet seasons (May to September), indicating an increased risk of extreme precipitation events during dry seasons.https://www.mdpi.com/2073-4433/16/6/669climate changesoft computingnonlinear principal component analysisensemble learningneural-fuzzy systemuncertainty |
| spellingShingle | Shiu-Shin Lin Kai-Yang Zhu Chen-Yu Wang Chou-Ping Yang Ming-Yi Liu Ensemble Learning-Based Soft Computing Approach for Future Precipitation Analysis Atmosphere climate change soft computing nonlinear principal component analysis ensemble learning neural-fuzzy system uncertainty |
| title | Ensemble Learning-Based Soft Computing Approach for Future Precipitation Analysis |
| title_full | Ensemble Learning-Based Soft Computing Approach for Future Precipitation Analysis |
| title_fullStr | Ensemble Learning-Based Soft Computing Approach for Future Precipitation Analysis |
| title_full_unstemmed | Ensemble Learning-Based Soft Computing Approach for Future Precipitation Analysis |
| title_short | Ensemble Learning-Based Soft Computing Approach for Future Precipitation Analysis |
| title_sort | ensemble learning based soft computing approach for future precipitation analysis |
| topic | climate change soft computing nonlinear principal component analysis ensemble learning neural-fuzzy system uncertainty |
| url | https://www.mdpi.com/2073-4433/16/6/669 |
| work_keys_str_mv | AT shiushinlin ensemblelearningbasedsoftcomputingapproachforfutureprecipitationanalysis AT kaiyangzhu ensemblelearningbasedsoftcomputingapproachforfutureprecipitationanalysis AT chenyuwang ensemblelearningbasedsoftcomputingapproachforfutureprecipitationanalysis AT choupingyang ensemblelearningbasedsoftcomputingapproachforfutureprecipitationanalysis AT mingyiliu ensemblelearningbasedsoftcomputingapproachforfutureprecipitationanalysis |