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Jeffrey Tim Query, Evaristo Diz
Pág. 145 - 159
AbstractIn this study we examine the robustness of fit for a multivariate and an autoregressive integrated moving average model to a data sample time series type. The sample is a recurrent actuarial data set for a 10-year horizon. We utilize ...
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Madeleine G. Barton, Hazel Parry, Paul A. Umina, Matthew R. Binns, Thomas Heddle, Ary A. Hoffmann, Joanne Holloway, Dustin Severtson, Maarten Van Helden, Samantha Ward, Rachel Wood and Sarina Macfadyen
Despite the known benefits of integrated pest management, adoption in Australian broadacre crops has been slow, in part due to the lack of understanding about how pests and natural enemies interact. We use a previously developed process-based model to pr...
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Juan Luis Pérez-Ruiz, Yu Tang, Igor Loboda and Luis Angel Miró-Zárate
In the field of aircraft engine diagnostics, many advanced algorithms have been proposed over the last few years. However, there is still wide room for improvement, especially in the development of more integrated and complete engine health management sy...
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Aikaterini Lyra, Athanasios Loukas, Pantelis Sidiropoulos and Lampros Vasiliades
This study presents the projected future evolution of water resource balance and nitrate pollution under various climate change scenarios and climatic models using a holistic approach. The study area is Almyros Basin and its aquifer system, located in Ce...
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Jhon B. Valencia, Vladimir V. Guryanov, Jeison Mesa-Diez, Nilton Diaz, Daniel Escobar-Carbonari and Artyom V. Gusarov
This paper presents a hydrological assessment of the 113,981 km2 Meta River basin in Colombia using 13 global climate models to predict water yield for 2050 under two CMIP6 scenarios, SSP 4.5 and SSP 8.5. Despite mixed performance across subbasins, the m...
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C. Tamilselvi, Md Yeasin, Ranjit Kumar Paul and Amrit Kumar Paul
Denoising is an integral part of the data pre-processing pipeline that often works in conjunction with model development for enhancing the quality of data, improving model accuracy, preventing overfitting, and contributing to the overall robustness of pr...
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José Francisco Lima, Fernanda Catarina Pereira, Arminda Manuela Gonçalves and Marco Costa
Linear models, seasonal autoregressive integrated moving average (SARIMA) models, and state-space models have been widely adopted to model and forecast economic data. While modeling using linear models and SARIMA models is well established in the literat...
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Wen Tian, Yining Zhang, Ying Zhang, Haiyan Chen and Weidong Liu
To fully leverage the spatiotemporal dynamic correlations in air traffic flow and enhance the accuracy of traffic flow prediction models, thereby providing a more precise basis for perceiving congestion situations in the air route network, a study was co...
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Lilu Zhu, Yang Wang, Yunbo Kong, Yanfeng Hu and Kai Huang
The integration of geospatial-analysis models is crucial for simulating complex geographic processes and phenomena. However, compared to non-geospatial models and traditional geospatial models, geospatial-analysis models face more challenges owing to ext...
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Feifei He, Qinjuan Wan, Yongqiang Wang, Jiang Wu, Xiaoqi Zhang and Yu Feng
Accurately predicting hydrological runoff is crucial for water resource allocation and power station scheduling. However, there is no perfect model that can accurately predict future runoff. In this paper, a daily runoff prediction method with a seasonal...
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