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Single-Objective and Multi-Objective Flood Interval Forecasting Considering Interval Fitting Coefficients
Human activities and climate change have exacerbated the frequency of extreme weather events such as rainstorms and floods, which makes it difficult to accurately quantify the uncertainty characteristics in runoff prediction. Therefore, the lower and upper boundary estimation method (LUBE) has become an important means to quantify uncertainty and has been widely used. However, the traditional interval prediction evaluation system only relies on coverage and width indicators, and performs poorly in single-objective optimization methods, which limits the large-scale application of the LUBE method. Based on this, this study innovatively proposes the prediction interval fitting coefficient (PIFC), and combines the prediction interval coverage probability (PICP) and normalized average width index (PINAW) to construct the coverage width fitting-based criterion (CWFC) for the first time, which broadens and improves the interval prediction evaluation dimension system. Further, the single-objective and multi-objective LUBE interval forecasting models based on the randomized weighted particle swarm algorithm (RWPSO) and the non-dominated sorting genetic algorithms III (NSGA-III) are constructed in this study. The verification results of cascade hydropower stations in the Yalong river basin show that the calculation efficiency and prediction effect of the single target interval prediction model are both improved after the introduction of PIFC. Under the CWFC objective function, the PINAW and PIFC indexes in the prediction interval are significantly better, and the PICP gap is smaller. Under multi-objective conditions (PICP, PINAW and PIFC), the Pareto non-inferior solution set can provide more choices for decision makers. During the flood season, PICP can reach more than 93%, PINAW is controlled below 10%, and PIFC can reach more than 0.95. This fully proves that the performance of interval prediction has been significantly improved after the introduction of PIFC, and the research results can provide a new way for basin interval prediction.
Journal Article
Wild Jack
Clive Anderson is falsely accused of questioning the status quo and must escape from a twenty-third century \"retraining school.\"
Book
Disaster Prediction Knowledge Graph Based on Multi-Source Spatio-Temporal Information
Natural disasters have frequently occurred and caused great harm. Although the remote sensing technology can effectively provide disaster data, it still needs to consider the relevant information from multiple aspects for disaster analysis. It is hard to build an analysis model that can integrate the remote sensing and the large-scale relevant information, particularly at the sematic level. This paper proposes a disaster prediction knowledge graph for disaster prediction by integrating remote sensing information, relevant geographic information, with the expert knowledge in the field of disaster analysis. This paper constructs the conceptual layer and instance layer of the knowledge graph by building a common semantic ontology of disasters and a unified spatio-temporal framework benchmark. Moreover, this paper represents the disaster prediction model in the forms of knowledge of disaster prediction. This paper demonstrates experiments and cases studies regarding the forest fire and geological landslide risk. These investigations show that the proposed method is beneficial to multi-source spatio-temporal information integration and disaster prediction.
Journal Article
Eerie earthquakes
\"Discusses the science behind earthquakes and what to do to stay safe from them\"-- Provided by publisher.
Book
Integration of artificial intelligence performance prediction and learning analytics to improve student learning in online engineering course
As a cutting-edge field of artificial intelligence in education (AIEd) that depends on advanced computing technologies, AI performance prediction model is widely used to identify at-risk students that tend to fail, establish student-centered learning pathways, and optimize instructional design and development. A majority of the existing AI prediction models focus on the development and optimization of the accuracy of AI algorithms rather than applying AI models to provide student with in-time and continuous feedback and improve the students’ learning quality. To fill this gap, this research integrated an AI performance prediction model with learning analytics approaches with a goal to improve student learning effects in a collaborative learning context. Quasi-experimental research was conducted in an online engineering course to examine the differences of students’ collaborative learning effect with and without the support of the integrated approach. Results showed that the integrated approach increased student engagement, improved collaborative learning performances, and strengthen student satisfactions about learning. This research made contributions to proposing an integrated approach of AI models and learning analytics (LA) feedback and providing paradigmatic implications for future development of AI-driven learning analytics.HighlightsIntegrated approach was used to combine AI with learning analytics (LA) feedbackQuasi-experiment research was conducted to investigate student learning effectsIntegrated approach to foster student engagement, performances and satisfactionsParadigmatic implication was proposed for develop AI-driven learning analyticsClosed loop was established for both AI model development and educational application.
Journal Article
The Will to Predict
In The Will to
Predict , Eglė Rindzevičiūtė demonstrates how
the logic of scientific expertise cannot be properly understood
without knowing the conceptual and institutional history of
scientific prediction. She notes that predictions of
future population, economic growth, environmental change, and
scientific and technological innovation have shaped much of
twentieth and twenty-first-century politics and social life, as
well as government policies. Today, such predictions are more
necessary than ever as the world undergoes dramatic environmental,
political, and technological change. But, she asks, what does it
mean to predict scientifically? What are the limits of scientific
prediction and what are its effects on governance, institutions,
and society?
Her intellectual and political history of scientific prediction
takes as its example twentieth-century USSR. By outlining the role
of prediction in a range of governmental contexts, from economic
and social planning to military strategy, she shows that the
history of scientific prediction is a transnational one, part of
the history of modern science and technology as well as governance.
Going beyond the Soviet case, Rindzevičiūtė argues that scientific
predictions are central for organizing uncertainty through the
orchestration of knowledge and action. Bridging the fields of
political sociology, organization studies, and history, The
Will to Predict considers what makes knowledge scientific and
how such knowledge has impacted late modern governance.
eBook