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Xuan Wu and Yafei Song
In recent years, the presence of malware has been growing exponentially, resulting in enormous demand for efficient malware classification methods. However, the existing machine learning-based classifiers have high false positive rates and cannot effecti...
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Jingyun Zhang, Lingyu Xu and Baogang Jin
The multi-model ensemble (MME) forecast for meteorological elements has been proved many times to be more skillful than the single model. It improves the forecast quality by integrating multiple sets of numerical forecast results with different spatial-t...
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Linus Zhang and Xiaoliu Yang
Given the substantial impacts that are expected due to climate change, it is crucial that accurate rainfall?runoff results are provided for various decision-making purposes. However, these modeling results often generate uncertainty or bias due to the im...
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Bo Qu, Xingnan Zhang, Florian Pappenberger, Tao Zhang, Yuanhao Fang
Pág. 1 - 13
Statistical post-processing for multi-model grand ensemble (GE) hydrologic predictions is necessary, in order to achieve more accurate and reliable probabilistic forecasts. This paper presents a case study which applies Bayesian model averaging (BMA) to ...
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Bastian Klein, Dennis Meissner, Hans-Ulrich Kobialka, Paolo Reggiani
Pág. 1 - 22
Predictive uncertainty (PU) is defined as the probability of occurrence of an observed variable of interest, conditional on all available information. In this context, hydrological model predictions and forecasts are considered to be accessible but yet u...
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