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Qian Liu, Xiaofeng Zhao, Jing Zou, Yunzhou Li, Zhijin Qiu, Tong Hu, Bo Wang and Zhiqian Li
The Coupled Ocean?Atmosphere?Wave?Sediment Transport (COAWST) model serves as the foundation for creating a forecast model to detect lower atmospheric ducts in this study. A set of prediction tests with different forecasting times focusing on the South C...
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Huang Feng and Yu Zhang
Extensive research in predicting annual passenger throughput has been conducted, aiming at providing decision support for airport construction, aircraft procurement, resource management, flight scheduling, etc. However, how airport operational throughput...
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Dwaipayan Chakraborty and Subhashis Mallick
Ocean-water temperature and salinity are two vital properties that are required for weather-, climate-, and marine biology-related research. These properties are usually measured using disposable instruments at sparse locations, typically from tens to hu...
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Mykhailo Lohachov, Ryoji Korei, Kazuo Oki, Koshi Yoshida, Issaku Azechi, Salem Ibrahim Salem and Nobuyuki Utsumi
This article investigates approaches for broccoli harvest time prediction through the application of various machine learning models. This study?s experiment is conducted on a commercial farm in Ecuador, and it integrates in situ weather and broccoli gro...
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Adriano Mancini, Francesco Solfanelli, Luca Coviello, Francesco Maria Martini, Serena Mandolesi and Raffaele Zanoli
Yield prediction is a crucial activity in scheduling agronomic operations and in informing the management and financial decisions of a wide range of stakeholders of the organic durum wheat supply chain. This research aims to develop a yield forecasting s...
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Varsha S. Lalapura, Veerender Reddy Bhimavarapu, J. Amudha and Hariram Selvamurugan Satheesh
The Recurrent Neural Networks (RNNs) are an essential class of supervised learning algorithms. Complex tasks like speech recognition, machine translation, sentiment classification, weather prediction, etc., are now performed by well-trained RNNs. Local o...
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Yuxiu Liu, Xing Yuan, Yang Jiao, Peng Ji, Chaoqun Li and Xindai An
Integrating numerical weather forecasts that provide ensemble precipitation forecasts, land surface hydrological modeling that resolves surface and subsurface hydrological processes, and artificial intelligence techniques that correct the forecast bias, ...
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Ahmed Skhiri, Ali Ferhi, Anis Bousselmi, Slaheddine Khlifi and Mohamed A. Mattar
A correct determination of irrigation water requirements necessitates an adequate estimation of reference evapotranspiration (ETo). In this study, monthly ETo is estimated using artificial neural network (ANN) models. Eleven combinations of long-term ave...
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Liuyi Chen, Bocheng Han, Xuesong Wang, Jiazhen Zhao, Wenke Yang and Zhengyi Yang
With the rapid development of artificial intelligence, machine learning is gradually becoming popular for predictions in all walks of life. In meteorology, it is gradually competing with traditional climate predictions dominated by physical models. This ...
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Neil S. Grigg
A comprehensive assessment of flood hazards will necessitate a step-by-step analysis, starting with hydrometeorological examinations of runoff and flow, followed by an assessment of the vulnerability of those at risk. Although bodies of knowledge about t...
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