Rainfall nowcasting based on neighboring rain gauge stations using learning machines

Srithagon, Somprat; Phisanbut, Nalina; Piamsa-nga, Napaporn; Piamsa-nga, Punpiti

doi:10.1109/icsec53205.2021.9684583

Cited by 1 publication

(2 citation statements)

References 7 publications

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“…The study aimed to investigate additional enhancements to the model to achieve greater accuracy in forecasting over an extended period [85]. Srithagon et al (2021) investigated the effectiveness of solely utilizing rain gauge data for prediction in Bangkok, Thailand. This study examined four distinct types of learning machines, namely classification and regression tree (CART), MLP, RF, and SVM.…”

Section: Overview Of Artificial Intelligence Techniques In Thailandmentioning

confidence: 99%

“…The findings indicated that the practice of nowcasting is a viable option. Based on the results of the experiments, it was found that the F1 performance at the crucial 90-min lead time, which plays a pivotal role in determining future activities, was observed to be average for CART, MLP, RF, and SVM, with values of 0.69, 0.59, 0.73, and 0.63, respectively [86]. Limsakul (2021) states that the daily rainfall intensity in Thailand from 1955 to 2019 has substantially altered the lower and upper distribution tails.…”

Section: Overview Of Artificial Intelligence Techniques In Thailandmentioning

confidence: 99%

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Potential of Artificial Intelligence-Based Techniques for Rainfall Forecasting in Thailand: A Comprehensive Review

Waqas,

Humphries,

Wangwongchai

et al. 2023

Water

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Rainfall forecasting is one of the most challenging factors of weather forecasting all over the planet. Due to climate change, Thailand has experienced extreme weather events, including prolonged lacks of and heavy rainfall. Accurate rainfall forecasting is crucial for Thailand’s agricultural sector. Agriculture depends on rainfall water, which is important for water resources, adversity management, and overall socio-economic development. Artificial intelligence techniques (AITs) have shown remarkable precision in rainfall forecasting in the past two decades. AITs may accurately forecast rainfall by identifying hidden patterns from past weather data features. This research investigates and reviews the most recent AITs focused on advanced machine learning (ML), artificial neural networks (ANNs), and deep learning (DL) utilized for rainfall forecasting. For this investigation, academic articles from credible online search libraries published between 2000 and 2022 are analyzed. The authors focus on Thailand and the worldwide applications of AITs for rainfall forecasting and determine the best methods for Thailand. This will assist academics in analyzing the most recent work on rainfall forecasting, with a particular emphasis on AITs, but it will also serve as a benchmark for future comparisons. The investigation concludes that hybrid models combining ANNs with wavelet transformation and bootstrapping can improve the current accuracy of rainfall forecasting in Thailand.

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Section: Overview Of Artificial Intelligence Techniques In Thailandmentioning

confidence: 99%

Section: Overview Of Artificial Intelligence Techniques In Thailandmentioning

confidence: 99%

Potential of Artificial Intelligence-Based Techniques for Rainfall Forecasting in Thailand: A Comprehensive Review

Waqas,

Humphries,

Wangwongchai

et al. 2023

Water

View full text Add to dashboard Cite

show abstract

Rainfall nowcasting based on neighboring rain gauge stations using learning machines

Cited by 1 publication

References 7 publications

Potential of Artificial Intelligence-Based Techniques for Rainfall Forecasting in Thailand: A Comprehensive Review

Potential of Artificial Intelligence-Based Techniques for Rainfall Forecasting in Thailand: A Comprehensive Review

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