2016
DOI: 10.5194/hess-2016-412
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Real-time forecasting of typhoon inundation extent in a partially-gauged area through the integration of ARX-based models and a geographic information system

Abstract: Abstract. This study presents a methodology for forecasting the extent of inundation and depth of distribution during typhoons in real-time. The proposed approach involves the construction of ARX and ARMAX models capable of predicting water-levels at the locations of on-site gauging stations and 10 representative points located at the outlets of the sub-areas obtained by terrain analysis using a geographic information system. The models are constructed based on historical typhoon data and the results of numeri… Show more

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Cited by 20 publications
(23 citation statements)
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“…Despite these strides, a persistent challenge remains in bridging the gap between AI's interpretation of context and the nuanced understanding exhibited by humans. Studies have pointed out the limitations in current models' ability to integrate and apply external, cultural, or realworld knowledge dynamically, raising a critical area for further exploration [17,18,19,20]. The exploration of mechanisms for incorporating broader contextual cues and external information sources has been identified as a promising direction to enhance AI systems' depth of understanding and adaptability [21,22,14].…”
Section: Contextual Understanding In Aimentioning
confidence: 99%
“…Despite these strides, a persistent challenge remains in bridging the gap between AI's interpretation of context and the nuanced understanding exhibited by humans. Studies have pointed out the limitations in current models' ability to integrate and apply external, cultural, or realworld knowledge dynamically, raising a critical area for further exploration [17,18,19,20]. The exploration of mechanisms for incorporating broader contextual cues and external information sources has been identified as a promising direction to enhance AI systems' depth of understanding and adaptability [21,22,14].…”
Section: Contextual Understanding In Aimentioning
confidence: 99%
“…Dynamic knowledge management systems have been identified as crucial for enhancing the adaptability and efficiency of information retrieval and integration processes in real-time applications [10,11,12,13]. LLMs can leverage sophisticated algorithms to continuously update their databases with minimal human intervention, ensuring that the information remains current and relevant [7,14,15,16,17].…”
Section: Dynamic Knowledge Management Systemsmentioning
confidence: 99%
“…Real-time data integration techniques have been recognized for their ability to enhance the responsiveness and accuracy of systems relying on timely data, as they utilize event-driven architectures to process and integrate data streams as they arrive, minimizing latency and enabling immediate decision-making [35,11,36,37]. Efficient handling of high-velocity data streams has been achieved through the use of scalable and distributed processing frameworks, addressing the challenges of volume and velocity inherent in real-time data [22,38,39].…”
Section: Real-time Data Integration Techniquesmentioning
confidence: 99%
“…Furthermore, the exploration of sparse and adaptive attention mechanisms demonstrates potential in reducing computational demands while maintaining or even improving model performance [6,14,8,18,19,20]. The development of models that can dynamically adjust their architecture based on the task at hand represents a significant stride towards more versatile and efficient NLP systems [21,22,23,24,25].…”
Section: Advancements In Model Architecturesmentioning
confidence: 99%