5,138 research outputs found
Wind generation forecasting methods and proliferation of artificial neural network:A review of five years research trend
To sustain a clean environment by reducing fossil fuels-based energies and increasing the integration of renewable-based energy sources, i.e., wind and solar power, have become the national policy for many countries. The increasing demand for renewable energy sources, such as wind, has created interest in the economic and technical issues related to the integration into the power grids. Having an intermittent nature and wind generation forecasting is a crucial aspect of ensuring the optimum grid control and design in power plants. Accurate forecasting provides essential information to empower grid operators and system designers in generating an optimal wind power plant, and to balance the power supply and demand. In this paper, we present an extensive review of wind forecasting methods and the artificial neural network (ANN) prolific in this regard. The instrument used to measure wind assimilation is analyzed and discussed, accurately, in studies that were published from May 1st, 2014 to May 1st, 2018. The results of the review demonstrate the increased application of ANN into wind power generation forecasting. Considering the component limitation of other systems, the trend of deploying the ANN and its hybrid systems are more attractive than other individual methods. The review further revealed that high forecasting accuracy could be achieved through proper handling and calibration of the wind-forecasting instrument and method
Machine learning in drug supply chain management during disease outbreaks: a systematic review
The drug supply chain is inherently complex. The challenge is not only the number of stakeholders and the supply chain from producers to users but also production and demand gaps. Downstream, drug demand is related to the type of disease outbreak. This study identifies the correlation between drug supply chain management and the use of predictive parameters in research on the spread of disease, especially with machine learning methods in the last five years. Using the Publish or Perish 8 application, there are 71 articles that meet the inclusion criteria and keyword search requirements according to Kitchenham's systematic review methodology. The findings can be grouped into three broad groupings of disease outbreaks, each of which uses machine learning algorithms to predict the spread of disease outbreaks. The use of parameters for prediction with machine learning has a correlation with drug supply management in the coronavirus disease case. The area of drug supply risk management has not been heavily involved in the prediction of disease outbreaks
Applying autonomy to distributed satellite systems: Trends, challenges, and future prospects
While monolithic satellite missions still pose significant advantages in terms of accuracy and
operations, novel distributed architectures are promising improved flexibility, responsiveness,
and adaptability to structural and functional changes. Large satellite swarms, opportunistic satellite
networks or heterogeneous constellations hybridizing small-spacecraft nodes with highperformance
satellites are becoming feasible and advantageous alternatives requiring the adoption
of new operation paradigms that enhance their autonomy. While autonomy is a notion that
is gaining acceptance in monolithic satellite missions, it can also be deemed an integral characteristic
in Distributed Satellite Systems (DSS). In this context, this paper focuses on the motivations
for system-level autonomy in DSS and justifies its need as an enabler of system qualities. Autonomy
is also presented as a necessary feature to bring new distributed Earth observation functions
(which require coordination and collaboration mechanisms) and to allow for novel structural
functions (e.g., opportunistic coalitions, exchange of resources, or in-orbit data services). Mission
Planning and Scheduling (MPS) frameworks are then presented as a key component to implement
autonomous operations in satellite missions. An exhaustive knowledge classification explores the
design aspects of MPS for DSS, and conceptually groups them into: components and organizational
paradigms; problem modeling and representation; optimization techniques and metaheuristics;
execution and runtime characteristics and the notions of tasks, resources, and constraints.
This paper concludes by proposing future strands of work devoted to study the trade-offs of
autonomy in large-scale, highly dynamic and heterogeneous networks through frameworks that
consider some of the limitations of small spacecraft technologies.Postprint (author's final draft
Probabilistic and artificial intelligence modelling of drought and agricultural crop yield in Pakistan
Pakistan is a drought-prone, agricultural nation with hydro-meteorological imbalances that increase the scarcity of water resources, thus, constraining water availability and leading major risks to the agricultural productivity sector and food security. Rainfall and drought are imperative matters of consideration, both for hydrological and agricultural applications. The aim of this doctoral thesis is to advance new knowledge in designing hybridized probabilistic and artificial intelligence forecasts models for rainfall, drought and crop yield within the agricultural hubs in Pakistan. The choice of these study regions is a strategic decision, to focus on precision agriculture given the importance of rainfall and drought events on agricultural crops in socioeconomic activities of Pakistan. The outcomes of this PhD contribute to efficient modelling of seasonal rainfall, drought and crop yield to assist farmers and other stakeholders to promote more strategic decisions for better management of climate risk for agriculturalreliant nations
Interpretable AI-Driven Discovery of Terrain-Precipitation Relationships for Enhanced Climate Insights
Despite the remarkable strides made by AI-driven models in modern
precipitation forecasting, these black-box models cannot inherently deepen the
comprehension of underlying mechanisms. To address this limitation, we propose
an AI-driven knowledge discovery framework known as genetic
algorithm-geographic weighted regression (GA-GWR). Our approach seeks to unveil
the explicit equations that govern the intricate relationship between
precipitation patterns and terrain characteristics in regions marked by complex
terrain. Through this AI-driven knowledge discovery, we uncover previously
undisclosed explicit equations that shed light on the connection between
terrain features and precipitation patterns. These equations demonstrate
remarkable accuracy when applied to precipitation data, outperforming
conventional empirical models. Notably, our research reveals that the
parameters within these equations are dynamic, adapting to evolving climate
patterns. Ultimately, the unveiled equations have practical applications,
particularly in fine-scale downscaling for precipitation predictions using
low-resolution future climate data. This capability offers invaluable insights
into the anticipated changes in precipitation patterns across diverse terrains
under future climate scenarios, which enhances our ability to address the
challenges posed by contemporary climate science
Analysis, Characterization, Prediction and Attribution of Extreme Atmospheric Events with Machine Learning: a Review
Atmospheric Extreme Events (EEs) cause severe damages to human societies and
ecosystems. The frequency and intensity of EEs and other associated events are
increasing in the current climate change and global warming risk. The accurate
prediction, characterization, and attribution of atmospheric EEs is therefore a
key research field, in which many groups are currently working by applying
different methodologies and computational tools. Machine Learning (ML) methods
have arisen in the last years as powerful techniques to tackle many of the
problems related to atmospheric EEs. This paper reviews the ML algorithms
applied to the analysis, characterization, prediction, and attribution of the
most important atmospheric EEs. A summary of the most used ML techniques in
this area, and a comprehensive critical review of literature related to ML in
EEs, are provided. A number of examples is discussed and perspectives and
outlooks on the field are drawn.Comment: 93 pages, 18 figures, under revie
PICES Press, Vol. 18, No. 1, Winter 2010
•Major Outcomes from the 2009 PICES Annual Meeting: A Note from the Chairman (pp. 1-3, 8)
•PICES Science – 2009 (pp. 4-8)
•2009 PICES Awards (pp. 9-10)
•New Chairmen in PICES (pp. 11-15)
•PICES Interns (p. 15)
•The State of the Western North Pacific in the First Half of 2009 (pp. 16-17, 27)
•The State of the Northeast Pacific in 2009 (pp. 18-19)
•The Bering Sea: Current Status and Recent Events (pp. 20-21)
•2009 PICES Summer School on “Satellite Oceanography for the Earth Environment” (pp. 22-25)
•2009 International Conference on “Marine Bioinvasions” (pp. 26-27)
•A New PICES Working Group Holds Workshop and Meeting in Jeju Island (pp. 28-29)
•The Second Marine Ecosystem Model Inter-comparison Workshop (pp. 30-32)
•ICES/PICES/UNCOVER Symposium on “Rebuilding Depleted Fish Stocks – Biology, Ecology, Social Science and Management Strategies” (pp. 33-35)
•2009 North Pacific Synthesis Workshop (pp. 36-37)
•2009 PICES Rapid Assessment Survey (pp. 38-40
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