Zobrazeno 1 - 10
of 2 648
pro vyhledávání: '"Hybrid models"'
Autor:
Wulfran Fendzi Mbasso, Reagan Jean Jacques Molu, Ambe Harrison, Mukesh Pushkarna, Fritz Nguemo Kemdoum, Emmanuel Fendzi Donfack, Pradeep Jangir, Pierre Tiako, Milkias Berhanu Tuka
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-22 (2024)
Abstract This study introduces an advanced mathematical methodology for predicting energy generation and consumption based on temperature variations in regions with diverse climatic conditions and increasing energy demands. Using a comprehensive data
Externí odkaz:
https://doaj.org/article/fbe4290568a04443aa031c603b099480
Publikováno v:
Journal of Big Data, Vol 11, Iss 1, Pp 1-39 (2024)
Abstract In late 2023, the United Nations conference on climate change (COP28), which was held in Dubai, encouraged a quick move from fossil fuels to renewable energy. Solar energy is one of the most promising forms of energy that is both sustainable
Externí odkaz:
https://doaj.org/article/454ecca6808540f196021c9c73e422c2
Publikováno v:
Journal of Materials Research and Technology, Vol 32, Iss , Pp 2767-2779 (2024)
To address the challenges posed by inadequate data and data utilization in multiple scenarios of fatigue loading, a Physics-informed Transfer Learning (PITL) model has been developed to predict the fatigue life of IN718 superalloy. Strain-controlled
Externí odkaz:
https://doaj.org/article/ec9819efaf08439fb0dcd08d28121e4a
Publikováno v:
Proceedings of the Estonian Academy of Sciences, Vol 73, Iss 3, Pp 264-278 (2024)
Mathematical modelling of physical phenomena is based on the laws of physics, but for complicated processes, phenomenological models could enhance the descriptive and prescriptive power of the analysis. This paper describes some hybrid models, where
Externí odkaz:
https://doaj.org/article/8f50221d41bf4386bd3b6d91d462e309
Autor:
Negin Ahmadi, Sina Fard Moradinia
Publikováno v:
Hydrology Research, Vol 55, Iss 5, Pp 560-575 (2024)
Using machine learning methods is efficient in predicting floods in areas where complete data is not available. Therefore, this study considers the Adaptive Neuro-Fuzzy Inference System (ANFIS) model combined with evolutionary algorithms, namely Harr
Externí odkaz:
https://doaj.org/article/af4955f991bf40e68b74a30e57ecbba5
Publikováno v:
Frontiers in Marine Science, Vol 11 (2024)
Predicting the nearshore sediment transport and shifts in coastlines in view of climate change is important for planning and management of coastal infrastructure and requires an accurate prediction of the regional wave climate as well as an in-depth
Externí odkaz:
https://doaj.org/article/c3308ac3e0b5460cbe8b00998c31151e
Publikováno v:
Ecological Indicators, Vol 167, Iss , Pp 112654- (2024)
Mapping the spatial distribution of soil organic carbon (SOC) is crucial for monitoring soil health, understanding ecosystem functions, and contributing to global carbon cycling. However, few studies have directly compared the influence of hybrid mod
Externí odkaz:
https://doaj.org/article/2ce88b1465f842ba9a572457355e0e8a
Publikováno v:
Frontiers in Applied Mathematics and Statistics, Vol 10 (2024)
Given the numerous factors that can influence stock prices such as a company's financial health, economic conditions, and the political climate, predicting stock prices can be quite difficult. However, the advent of the newer learning algorithm such
Externí odkaz:
https://doaj.org/article/9f21259911404300ac3769d3314fd80d
Autor:
J. Pérez-Aracil, D. Fister, C.M. Marina, C. Peláez-Rodríguez, L. Cornejo-Bueno, P.A. Gutiérrez, M. Giuliani, A. Castelleti, S. Salcedo-Sanz
Publikováno v:
Applied Computing and Geosciences, Vol 23, Iss , Pp 100185- (2024)
This paper proposes two hybrid approaches based on Autoencoders (AEs) for long-term temperature prediction. The first algorithm comprises an AE trained to learn temperature patterns, which is then linked to a second AE, used to detect possible anomal
Externí odkaz:
https://doaj.org/article/e45536aa007d458391e2e43ee37b08a9