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Saeed Samadianfard, Salar Jarhan, Ely Salwana, Amir Mosavi, Shahaboddin Shamshirband and Shatirah Akib
Advancement in river flow prediction systems can greatly empower the operational river management to make better decisions, practices, and policies. Machine learning methods recently have shown promising results in building accurate models for river flow...
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Pablo de Llano, Carlos Piñeiro, Manuel Rodríguez
Pág. pp. 163 - 198
This paper offers a comparative analysis of the effectiveness of eight popular forecasting methods: univariate, linear, discriminate and logit regression; recursive partitioning, rough sets, artificial neural networks, and DEA. Our goals are: clarify the...
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Junting Wang, Tianhe Xu, Wei Huang, Liping Zhang, Jianxu Shu, Yangfan Liu and Linyang Li
Underwater sound speed is one of the most significant factors that affects high-accuracy underwater acoustic positioning and navigation. Due to its complex temporal variation, the forecasting of the underwater sound speed field (SSF) becomes a challengin...
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Efrain Noa-Yarasca, Javier M. Osorio Leyton and Jay P. Angerer
Timely forecasting of aboveground vegetation biomass is crucial for effective management and ensuring food security. However, research on predicting aboveground biomass remains scarce. Artificial intelligence (AI) methods could bridge this research gap a...
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Xiaoou Li
This paper tackles the challenge of time series forecasting in the presence of missing data. Traditional methods often struggle with such data, which leads to inaccurate predictions. We propose a novel framework that combines the strengths of Generative ...
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Konstantinos P. Fourkiotis and Athanasios Tsadiras
In today?s evolving global world, the pharmaceutical sector faces an emerging challenge, which is the rapid surge of the global population and the consequent growth in drug production demands. Recognizing this, our study explores the urgent need to stren...
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Ru Wang, Qingyu Zheng, Wei Li, Guijun Han, Xuan Wang and Song Hu
The uncertainty in the initial condition seriously affects the forecasting skill of numerical models. Targeted observations play an important role in reducing uncertainty in numerical prediction. The conditional nonlinear optimal perturbation (CNOP) meth...
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Shih-Lun Fang, Yi-Shan Lin, Sheng-Chih Chang, Yi-Lung Chang, Bing-Yun Tsai and Bo-Jein Kuo
The reference evapotranspiration (ET0) information is crucial for irrigation planning and water resource management. While the Penman-Monteith (PM) equation is widely recognized for ET0 calculation, its reliance on numerous meteorological parameters cons...
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Han Lin Shang
A key summary statistic in a stationary functional time series is the long-run covariance function that measures serial dependence. It can be consistently estimated via a kernel sandwich estimator, which is the core of dynamic functional principal compon...
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Wei Zhuang, Zhiheng Li, Ying Wang, Qingyu Xi and Min Xia
Predicting photovoltaic (PV) power generation is a crucial task in the field of clean energy. Achieving high-accuracy PV power prediction requires addressing two challenges in current deep learning methods: (1) In photovoltaic power generation prediction...
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