The prediction of mid-winter and spring breakups of ice cover on Canadian rivers using a hybrid ontology-based and machine learning model

Coste, Michael De; Li, Zhong; Khédri, Ridha

doi:10.1016/j.envsoft.2022.105577

Cited by 6 publications

(1 citation statement)

References 45 publications

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“…Some papers try to bring ontology into the requirements analysis phase. De Coste et al [20] proposes a hybrid modeling framework combining Mid-winter breakups(MWBs) ontology with image recognition, which allows an ontology to define and analyze key data, events, and relations in the ice season to reduce the prediction error of time-split events. Asudeh et al [21] provides efficient techniques for traversing all value combinations to evaluate the coverage of multiple classification attributes in the given dataset based on the ontology of diamond.…”

Section: Related Workmentioning

confidence: 99%

Dataset Constrution through Ontology-Based Data Requirements Analysis

Jiang,

Wang

2024

Applied Sciences

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Machine learning (ML) technology is rapidly evolving, and the quality of ML systems is becoming an increasingly focal point of attention. Since the ML system is shaped by the dataset it learns from, its quality largely depends on the quality of the dataset. However, the dataset is often collected in a non-standardized process and few requirements and analysis methods are given to assist in identifying the needed dataset. This leads to no guarantee for the quality of dataset, affecting the generalization ability of model and resulting in low training efficiency. To address these issues, this paper proposes an ontology-based requirement analysis method where ontology integrates domain knowledge into the process of data requirements analysis and the coverage criteria on ontology are given for specifying data requirements which can later be used to guide the high-quality construction of the dataset. We held an experiment on an image recognition system in the field of autonomous driving to validate our approach. The result shows that the ML system trained by the dataset constructed through our data requirements analysis method has a better performance.

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Section: Related Workmentioning

confidence: 99%

Dataset Constrution through Ontology-Based Data Requirements Analysis

Jiang,

Wang

2024

Applied Sciences

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Assessing the impact of climate change and reservoir operation on the thermal and ice regime of mountain rivers using the XGBoost model and wavelet analysis

Fukś,

Kędra,

Wiejaczka

2024

Stoch Environ Res Risk Assess

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This study presents an analysis of the influence of climatic conditions and the operation of a dam reservoir on the occurrence of ice cover and water temperature in two rivers (natural and transformed by reservoir operations) located in the Carpathian Mountains (central Europe). The analyses are based on data obtained from four hydrological and two climatological stations. The Extreme Gradient Boosting (XGBoost) machine learning model was used to quantitatively separate the effects of climate change from the effects arising from the operation of the dam reservoir. An analysis of the effects of reservoir operation on the phase synchronization between air and river water temperatures based on a continuous wavelet transform was also conducted. The analyses showed that there has been an increase in the average air temperature of the study area in November by 1.2 °C per decade (over the period 1984–2016), accompanied by an increase in winter water temperature of 0.3 °C per decade over the same period. As water and air temperatures associated with the river not influenced by the reservoir increased, there was a simultaneous reduction in the duration of ice cover, reaching nine days per decade. The river influenced by the dam reservoir showed a 1.05 °C increase in winter water temperature from the period 1994–2007 to the period 1981–1994, for which the operation of the reservoir was 65% responsible and climatic conditions were 35% responsible. As a result of the reservoir operation, the synchronization of air and water temperatures was disrupted. Increasing water temperatures resulted in a reduction in the average annual number of days with ice cover (by 27.3 days), for which the operation of the dam reservoir was 77.5% responsible, while climatic conditions were 22.5% responsible.

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