A bootstrap regional model for assessing the long-term impacts of climate change on river discharge

Li, Chi Yu; Lin, Shiu Shin; Lin, Yi Fong; Kan, Ping Shiuan

doi:10.1504/ijhst.2019.096802

Cited by 2 publications

(1 citation statement)

References 22 publications

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“…With advances in machine learning, recent developments in statistical downscaling methods have shifted from using traditional statistical methods to machine learning (or deep learning)-based methods in the past two decades [7][8][9][10]. In addition, in recent years, with improved hardware computing speed, machine learning has come to be widely applied in various fields [11,12].…”

Section: Introductionmentioning

confidence: 99%

A Deep Learning-Based Downscaling Method Considering the Impact on Typhoons to Future Precipitation in Taiwan

Lin,

Zhu,

Wang

2024

Atmosphere

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This study proposes a deep neural network (DNN)-based downscaling model incorporating kernel principal component analysis (KPCA) to investigate the precipitation uncertainty influenced by typhoons in Taiwan, which has a complex island topography. The best tracking data of tropical cyclones from the Joint Typhoon Warning Center (JTWC) are utilized to calculate typhoon and non-typhoon precipitation. KPCA is applied to extract nonlinear features of the BCC-CSM1-1 (Beijing Climate Center Climate System Model version 1.1) and CanESM2 (second-generation Canadian Earth System Model) GCM models. The length of the data used in the two GCM models span from January 1950 to December 2005 (historical data) and from January 2006 to December 2099 (scenario out data). The rainfall data are collected from the weather stations in Taichung and Hualien (cities of Taiwan) operated by the Central Weather Administration (CWA), Taiwan. The period of rainfall data in Taichung and in Hualien spans from January 1950 to December 2005. The proposed model is constructed with features extracted from the GCMs and historical monthly precipitation from Taichung and Hualien. The model we have built is used to estimate monthly precipitation and uncertainty in both Taichung and Hualien for future scenarios (rcp 4.5 and 8.5) of the GCMs. The results suggest that, in Taichung and Hualien, the summer precipitation is mostly within the normal range. The rainfall in the long term (January 2071 to December 2080) for both Taichung and Hualien typically fall between 100 mm and 200 mm. In the long term, the dry season (January to April, November, and December) precipitation for Taichung and that in the wet season (May to October) for Hualien are less and more affected by typhoons, respectively. The dry season precipitation is more affected by typhoons in Taichung than Hualien. In both Taichung and Hualien, the long-term probability of rainfall exceeding the historical average in the dry season is higher than that in the wet season.

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Section: Introductionmentioning

confidence: 99%

A Deep Learning-Based Downscaling Method Considering the Impact on Typhoons to Future Precipitation in Taiwan

Lin,

Zhu,

Wang

2024

Atmosphere

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Applying Adaptive Neuro-Fuzzy Inference System to Improve Typhoon Intensity Forecast in the Northwest Pacific

Lin

Song

Zhu

et al. 2023

Water

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Typhoon intensity forecast is an important issue. The objective of this study is to construct a 5-day 12-hourly typhoon intensity forecast model based on the adaptive neuro-fuzzy inference systems (ANFIS) to improve the typhoon intensity forecast in the Northwest Pacific. It analyzed the improvement of the ANFIS typhoon intensity forecast model by comparing it with the MLR model when only the atmospheric factor or both atmospheric and oceanic factors are considered. This study collected the SHIPS (Statistical Hurricane Intensity Prediction Scheme) developmental data of typhoons in the Northwest Pacific before landing from 2000 to 2012. The input factors of the ANFIS model were simplified by the stepwise regression procedure (SRP). Subtractive clustering (SC) was used to determine the number of ANFIS rules and to reduce model complexity. Model Index (MI) was taken as the clustering standard of SC to determine the network architecture of the ANFIS typhoon intensity forecast model. The simulated results show that the MI could effectively determine the radius of influence of SC. The typhoon intensity forecast was significantly improved after oceanic environmental factors were added. The improvement of RMSE of ANFIS was the highest at 84 h; the improvement of ANFIS on the underestimated ratio was primarily positive. The Typhoon Songda case study shows that the maximum bias of ANFIS is greatly improved, at 60 h of the lead time, and the improvement percentage of maximum bias is the highest (39%). Overall, the ANFIS model could effectively improve the MLR model in typhoon intensity forecast.

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A bootstrap regional model for assessing the long-term impacts of climate change on river discharge

Cited by 2 publications

References 22 publications

A Deep Learning-Based Downscaling Method Considering the Impact on Typhoons to Future Precipitation in Taiwan

A Deep Learning-Based Downscaling Method Considering the Impact on Typhoons to Future Precipitation in Taiwan

Applying Adaptive Neuro-Fuzzy Inference System to Improve Typhoon Intensity Forecast in the Northwest Pacific

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