Teses / dissertações sobre o tema "Classification and spatiotemporal forecasting"
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Veja os 50 melhores trabalhos (teses / dissertações) para estudos sobre o assunto "Classification and spatiotemporal forecasting".
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Kirchmeyer, Matthieu. "Out-of-distribution Generalization in Deep Learning : Classification and Spatiotemporal Forecasting". Electronic Thesis or Diss., Sorbonne université, 2023. http://www.theses.fr/2023SORUS080.
Texto completo da fonteDeep learning has emerged as a powerful approach for modelling static data like images and more recently for modelling dynamical systems like those underlying times series, videos or physical phenomena. Yet, neural networks were observed to not generalize well outside the training distribution, in other words out-of-distribution. This lack of generalization limits the deployment of deep learning in autonomous systems or online production pipelines, which are faced with constantly evolving data. In this thesis, we design new strategies for out-of-distribution generalization. These strategies handle the specific challenges posed by two main application tasks, classification of static data and spatiotemporal dynamics forecasting. The first two parts of this thesis consider the classification problem. We first investigate how we can efficiently leverage some observed training data from a target domain for adaptation. We then explore how to generalize to unobserved domains without access to such data. The last part of this thesis handles various generalization problems specific to spatiotemporal forecasting
Fu, Kaiqun. "Spatiotemporal Event Forecasting and Analysis with Ubiquitous Urban Sensors". Diss., Virginia Tech, 2021. http://hdl.handle.net/10919/104165.
Texto completo da fonteDoctor of Philosophy
The ubiquitously deployed urban sensors such as traffic speed meters, street-view cameras, and even smartphones in everybody's pockets are generating terabytes of data every hour. How do we refine the valuable intelligence out of such explosions of urban data and information became one of the profitable questions in the field of data mining and urban computing. In this dissertation, four innovative applications are proposed to solve real-world problems with big data of the urban sensors. In addition, the foreseeable ethical vulnerabilities in the research fields of urban computing and event predictions are addressed. The first work explores the connection between urban perception and crime inferences. StreetNet is proposed to learn crime rankings from street view images. This work presents the design of a street view images retrieval algorithm to improve the representation of urban perception. A data-driven, spatiotemporal algorithm is proposed to find unbiased label mappings between the street view images and the crime ranking records. The second work proposes a traffic incident duration prediction model that simultaneously predicts the impact of the traffic incidents and identifies the critical groups of temporal features via a multi-task learning framework. Such functionality provided by this model is helpful for the transportation operators and first responders to judge the influences of traffic incidents. In the third work, a social media-based traffic status monitoring system is established. The system is initiated by a transportation-related keyword generation process. A state-of-the-art tweets summarization algorithm is designed to eliminate the redundant tweets information. In addition, we show that the proposed tweets query expansion algorithm outperforms the previous methods. The fourth work aims to investigate the viability of an automatic multiclass cyberbullying detection model that is able to classify whether a cyberbully is targeting a victim's age, ethnicity, gender, religion, or other quality. This work represents a step forward for establishing an active anti-cyberbullying presence in social media and a step forward towards a future without cyberbullying. Finally, a discussion of the ethical issues in the urban computing community is addressed. This work seeks to identify ethical vulnerabilities from three primary research directions of urban computing: urban safety analysis, urban transportation analysis, and social media analysis for urban events. Visions for future improvements in the perspective of ethics are pointed out.
Khalid, Shehzad. "Motion classification using spatiotemporal approximation of object trajectories". Thesis, University of Manchester, 2009. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.492915.
Texto completo da fonteLau, Ada. "Probabilistic wind power forecasts : from aggregated approach to spatiotemporal models". Thesis, University of Oxford, 2011. http://ora.ox.ac.uk/objects/uuid:f5a66568-baac-4f11-ab1e-dc79061cfb0f.
Texto completo da fonteLeasor, Zachary T. "Spatiotemporal Variations of Drought Persistence in the South-Central United States". The Ohio State University, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=osu1497444478957738.
Texto completo da fonteRosswog, James. "Improving classification of spatiotemporal data using adaptive history filtering". Diss., Online access via UMI:, 2007.
Encontre o texto completo da fonteLo, Shin-Lian. "High-dimensional classification and attribute-based forecasting". Diss., Georgia Institute of Technology, 2010. http://hdl.handle.net/1853/37193.
Texto completo da fonteHaensly, Paul J. "The Application of Statistical Classification to Business Failure Prediction". Thesis, University of North Texas, 1994. https://digital.library.unt.edu/ark:/67531/metadc278187/.
Texto completo da fonteAlbanwan, Hessah AMYM. "Remote Sensing Image Enhancement through Spatiotemporal Filtering". The Ohio State University, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=osu1492011122078055.
Texto completo da fonteWei, Xinyu. "Modelling and predicting adversarial behaviour using large amounts of spatiotemporal data". Thesis, Queensland University of Technology, 2016. https://eprints.qut.edu.au/101959/1/Xinyu_Wei_Thesis.pdf.
Texto completo da fonteWorthy, Paul James. "Investigation of artificial neural networks for forecasting and classification". Thesis, City University London, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.264247.
Texto completo da fonteLopez, Farias Rodrigo. "Time series forecasting based on classification of dynamic patterns". Thesis, IMT Alti Studi Lucca, 2015. http://e-theses.imtlucca.it/187/1/Farias_phdthesis.pdf.
Texto completo da fonteErler, Frido. "Spatiotemporal calcium-dynamics in presynaptic terminals". Doctoral thesis, Technische Universität Dresden, 2004. https://tud.qucosa.de/id/qucosa%3A24527.
Texto completo da fonteStrasser, Klaus-Peter. "Kinetic oscillations and spatiotemporal self-organization in electrocatalytic reactions experimental analysis, modeling and classification /". [S.l. : s.n.], 1999. http://www.diss.fu-berlin.de/1999/25/index.html.
Texto completo da fonteLundkvist, Emil. "Decision Tree Classification and Forecasting of Pricing Time Series Data". Thesis, KTH, Reglerteknik, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-151017.
Texto completo da fonteLeverger, Colin. "Investigation of a framework for seasonal time series forecasting". Thesis, Rennes 1, 2020. http://www.theses.fr/2020REN1S033.
Texto completo da fonteTo deploy web applications, using web servers is paramount. If there is too few of them, applications performances can quickly deteriorate. However, if they are too numerous, the resources are wasted and the cost increased. In this context, engineers use capacity planning tools to follow the performances of the servers, to collect time series data and to anticipate future needs. The necessity to create reliable forecasts seems clear. Data generated by the infrastructure often exhibit seasonality. The activity cycle followed by the infrastructure is determined by some seasonal cycles (for example, the user’s daily rhythms). This thesis introduces a framework for seasonal time series forecasting. This framework is composed of two machine learning models (e.g. clustering and classification) and aims at producing reliable midterm forecasts with a limited number of parameters. Three instantiations of the framework are presented: one baseline, one deterministic and one probabilistic. The baseline is composed of K-means clustering algorithms and Markov Models. The deterministic version is composed of several clustering algorithms (K-means, K-shape, GAK and MODL) and of several classifiers (naive-bayes, decision trees, random forests and logistic regression). The probabilistic version relies on coclustering to create time series probabilistic grids, that are used to describe the data in an unsupervised way. The performances of the various implementations are compared with several state-of-the-art models, including the autoregressive models, ARIMA and SARIMA, Holt Winters, or even Prophet for the probabilistic paradigm. The results of the baseline are encouraging and confirm the interest for the framework proposed. Good results are observed for the deterministic implementation, and correct results for the probabilistic version. One Orange use case is studied, and the interest and limits of the methodology are discussed
Tang, Adelina Lai Toh. "Application of the tree augmented naive Bayes network to classification and forecasting /". [St. Lucia, Qld.], 2004. http://www.library.uq.edu.au/pdfserve.php?image=thesisabs/absthe.pdf.
Texto completo da fonteHovey, Erik P. "Forecasting the Marine Corps Enlisted Classification Plan Assessment of An Alternative Model". Thesis, Monterey, California. Naval Postgraduate School, 2012. http://hdl.handle.net/10945/6810.
Texto completo da fonteAfolabi, David Olalekan. "Interference reduction in classification and forecasting tasks through cluster and trend analysis". Thesis, University of Liverpool, 2018. http://livrepository.liverpool.ac.uk/3027593/.
Texto completo da fonteKarimi, Ahmad Maroof. "DATA SCIENCE AND MACHINE LEARNING TO PREDICT DEGRADATION AND POWER OF PHOTOVOLTAIC SYSTEMS: CONVOLUTIONAL AND SPATIOTEMPORAL GRAPH NEURAL NETWORK". Case Western Reserve University School of Graduate Studies / OhioLINK, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=case1601082841477951.
Texto completo da fonteZhou, Enwang. "Evolutionary intelligent systems for pattern classification and price based electric load forecasting applications". Ann Arbor, Mich. : ProQuest, 2007. http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqdiss&rft_dat=xri:pqdiss:3258041.
Texto completo da fonteTitle from PDF title page (viewed Mar. 18, 2008). Source: Dissertation Abstracts International, Volume: 68-03, Section: B, page: 1852. Adviser: Alireza Khotanzad. Includes bibliographical references.
Oliveira, Adriano Lorena Inácio de. "Neural networks forecasting and classification-based techniques for novelty detection in time series". Universidade Federal de Pernambuco, 2011. https://repositorio.ufpe.br/handle/123456789/1825.
Texto completo da fonteO problema da detecção de novidades pode ser definido como a identificação de dados novos ou desconhecidos aos quais um sistema de aprendizagem de máquina não teve acesso durante o treinamento. Os algoritmos para detecção de novidades são projetados para classificar um dado padrão de entrada como normal ou novidade. Esses algoritmos são usados em diversas areas, como visão computacional, detecçãao de falhas em máquinas, segurança de redes de computadores e detecção de fraudes. Um grande número de sistemas pode ter seu comportamento modelado por séries temporais. Recentemente o pro oblema de detecção de novidades em séries temporais tem recebido considerável atenção. Várias técnicas foram propostas, incluindo téecnicas baseadas em previsão de séries temporais com redes neurais artificiais e em classificação de janelas das s´eries temporais. As t´ecnicas de detec¸c ao de novidades em s´eries temporais atrav´es de previs ao t em sido criticadas devido a seu desempenho considerado insatisfat´orio. Em muitos problemas pr´aticos, a quantidade de dados dispon´ıveis nas s´eries ´e bastante pequena tornando a previs ao um problema ainda mais complexo. Este ´e o caso de alguns problemas importantes de auditoria, como auditoria cont´abil e auditoria de folhas de pagamento. Como alternativa aos m´etodos baseados em previs ao, alguns m´etodos baseados em classificação foram recentemente propostos para detecção de novidades em séries temporais, incluindo m´etodos baseados em sistemas imunol´ogicos artificiais, wavelets e m´aquinas de vetor de suporte com uma ´unica classe. Esta tese prop oe um conjunto de m´etodos baseados em redes neurais artificiais para detecção de novidades em séries temporais. Os métodos propostos foram projetados especificamente para detec¸c ao de fraudes decorrentes de desvios relativamente pequenos, que s ao bastante importantes em aplica¸c oes de detec¸c ao de fraudes em sistemas financeiros. O primeiro m´etodo foi proposto para melhorar o desempenho de detec¸c ao de novidades baseada em previs ao. Este m´etodo ´e baseado em intervalos de confian¸ca robustos, que s ao usados para definir valores adequados para os limiares a serem usados para detec¸c ao de novidades. O m´etodo proposto foi aplicado a diversas s´eries temporais financeiras e obteve resultados bem melhores que m´etodos anteriores baseados em previs ao. Esta tese tamb´em prop oe dois diferentes m´etodos baseados em classifica¸c ao para detec ¸c ao de novidades em s´eries temporais. O primeiro m´etodo ´e baseado em amostras negativas, enquanto que o segundo m´etodo ´e baseado em redes neurais artificiais RBFDDA e n ao usa amostras negativas na fase de treinamento. Resultados de simula¸c ao usando diversas s´eries temporais extra´ıdas de aplica¸c oes reais mostraram que o segundo m´etodo obt´em melhor desempenho que o primeiro. Al´em disso, o desempenho do segundo m´etodo n ao depende do tamanho do conjunto de teste, ao contr´ario do que acontece com o primeiro m´etodo. Al´em dos m´etodos para detec¸c ao de novidades em s´eries temporais, esta tese prop oe e investiga quatro diferentes m´etodos para melhorar o desempenho de redes neurais RBF-DDA. Os m´etodos propostos foram avaliados usando seis conjuntos de dados do reposit´orio UCI e os resultados mostraram que eles melhoram consideravelmente o desempenho de redes RBF-DDA e tamb´em que eles obt em melhor desempenho que redes MLP e que o m´etodo AdaBoost. Al´em disso, mostramos que os m´etodos propostos obt em resultados similares a k-NN. Os m´etodos propostos para melhorar RBF-DDA foram tamb´em usados em conjunto com o m´etodo proposto nesta tese para detec¸c ao de novidades em s´eries temporais baseado em amostras negativas. Os resultados de diversos experimentos mostraram que esses m´etodos tamb´em melhoram bastante o desempenho da detec¸c ao de fraudes em s´eries temporais, que ´e o foco principal desta tese.
Lack, Steven A. "Cell identification, verification, and classification using shape analysis techniques". Diss., Columbia, Mo. : University of Missouri-Columbia, 2007. http://hdl.handle.net/10355/6017.
Texto completo da fonteThe entire dissertation/thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file (which also appears in the research.pdf); a non-technical general description, or public abstract, appears in the public.pdf file. Title from title screen of research.pdf file (viewed on March 11, 2008) Includes bibliographical references.
Krauße, Thomas. "Development of a Class Framework for Flood Forecasting". Technische Universität Dresden, 2007. https://tud.qucosa.de/id/qucosa%3A26441.
Texto completo da fonteAl, Nasseri Alya Ali Mansoor. "The predictive power of stock micro-blogging sentiment in forecasting stock market behaviour". Thesis, Brunel University, 2016. http://bura.brunel.ac.uk/handle/2438/13575.
Texto completo da fonteWang, Jian. "From local to global: Complex behavior of spatiotemporal systems with fluctuating delay times: From local to global: Complex behavior of spatiotemporal systemswith fluctuating delay times". Doctoral thesis, Universitätsverlag der Technischen Universität Chemnitz, 2013. https://monarch.qucosa.de/id/qucosa%3A20006.
Texto completo da fonteZiel der vorliegenden Arbeit ist die Untersuchung der Einflüsse der zeitlich fluktuierenden Verzögerungen in räumlich ausgedehnten diffusiven Systemen. Durch den Vergleich von Systemen mit konstanter Verzögerung bzw. Systemen ohne räumliche Kopplung erhält man ein tieferes Verständnis und eine bessere Beschreibungsweise der Dynamik des räumlich ausgedehnten diffusiven Systems mit fluktuierenden Verzögerungen. Im ersten Teil werden diskrete Systeme in Form von diffusiven Coupled Map Lattices untersucht. Als die lokale iterierte Abbildung des betrachteten Systems wird die logistische Abbildung mit Verzögerung gewählt. In diesem Teil liegt der Fokus auf Musterbildung, Existenz von Multiattraktoren und laufenden Wellen sowie der Möglichkeit der vollen Synchronisation. Masterstabilitätsfunktion, Lyapunov Exponent und Spektrumsanalyse werden benutzt, um das dynamische Verhalten zu verstehen. Im zweiten Teil betrachten wir kontinuierliche Systeme. Hier wird die Fisher-KPP Gleichung mit Verzögerungen im Reaktionsteil untersucht. In diesem Teil liegt der Fokus auf der Existenz der Turing Instabilität. Mit Hilfe von analytischen und numerischen Berechnungen wird gezeigt, dass bei fluktuierenden Verzögerungen eine Turing Instabilität auch in 1-Komponenten-Reaktions-Diffusionsgleichungen gefunden werden kann
Diez, Franziska [Verfasser], e Ralf [Akademischer Betreuer] Korn. "Yield Curves and Chance-Risk Classification: Modeling, Forecasting, and Pension Product Portfolios / Franziska Diez ; Betreuer: Ralf Korn". Kaiserslautern : Technische Universität Kaiserslautern, 2021. http://d-nb.info/1238074472/34.
Texto completo da fonteNguyen, Van O. "Analysis of the U.S. Marine Corps' steady state Markov model for forecasting annual first-term enlisted classification requirements". Monterey, California. Naval Postgraduate School, 1997. http://hdl.handle.net/10945/25685.
Texto completo da fonteThe Marine Corps accesses approximately 29,000 to 36,000 new recruits annually. Determining how to classify these new enlistees into more than 200 Military Occupational Specialties is a critical task. These classification estimates must be precise, so the units within the Fleet Marine Force will have the necessary personnel to accomplish their mission. At the same time, these manpower planners must also balance the force structure to minimize personnel overages which could lead to excessive labor and training costs as well as promotion delays. The purpose of this research is to validate and, if necessary, improve the steady state Markov model currently being utilized by the manpower planners at Headquarters, U.S. Marine Corps (Code MPP-23) to forecast the annual personnel classification requirements of new recruits. From a mathematical perspective, all the essential elements of their model were present; however, some of the components like the year 1 continuation rate were not computed according to standard practice, and their estimates of the classification stocks are imprecise due to rounding errors inherent in their forecasting procedure. As a result, a revised model was developed to improve the accuracy and timeliness of the personnel classification forecasts. The recommendations were to implement the revised model and to review the computation of the continuation rates
Nepali, Anjeev. "County Level Population Estimation Using Knowledge-Based Image Classification and Regression Models". Thesis, University of North Texas, 2010. https://digital.library.unt.edu/ark:/67531/metadc30498/.
Texto completo da fonteRicci, Lorenzo. "Essays on tail risk in macroeconomics and finance: measurement and forecasting". Doctoral thesis, Universite Libre de Bruxelles, 2017. http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/242122.
Texto completo da fonteDoctorat en Sciences économiques et de gestion
info:eu-repo/semantics/nonPublished
Zhao, Tao. "A new method for detection and classification of out-of-control signals in autocorrelated multivariate processes". Morgantown, W. Va. : [West Virginia University Libraries], 2008. https://eidr.wvu.edu/etd/documentdata.eTD?documentid=5615.
Texto completo da fonteTitle from document title page. Document formatted into pages; contains x, 111 p. : ill. Includes abstract. Includes bibliographical references (p. 102-106).
BOZIC, Maja. "Impact of the retail environment drivers on sales and demand forecasting". Doctoral thesis, Università degli studi di Cassino, 2021. http://hdl.handle.net/11580/84146.
Texto completo da fonteSarmadi, Soheil. "On the Feasibility of Profiling, Forecasting and Authenticating Internet Usage Based on Privacy Preserving NetFlow Logs". Scholar Commons, 2018. https://scholarcommons.usf.edu/etd/7568.
Texto completo da fonteReinoso, Nicholas L. "Forecasting Harmful Algal Blooms for Western Lake Erie using Data Driven Machine Learning Techniques". Cleveland State University / OhioLINK, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=csu1494343783463819.
Texto completo da fonteKotriwala, Arzam Muzaffar. "Load Forecasting for Temporary Power Installations : A Machine Learning Approach". Thesis, KTH, Skolan för informations- och kommunikationsteknik (ICT), 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-211554.
Texto completo da fonteSportevenemang, festivaler, byggarbetsplatser och film platser är exempel på fall där kraften krävs Tillfälligt eller och bort från elnätet. Tillfälliga Kraft Installationer avser system som inrättats för en begränsad tid med Vanligtvis ström genereras på plats. De flesta lastprognoser forskning har kretsat kring inställningar med permanent eller strömförsörjning (zoals i bostadshus). Tvärtom föreslår detta arbete maskininlärning metoder för att noggrant prognos belastning under Tillfälliga anläggningar. I praktiken är thesis Typiskt system drivs med dieselgeneratorer som är överdimensionerad och följaktligen arbetar ineffektivt vid låga belastningsnivåer. I denna avhandling är en ‘Pre-Event Casting’ Föreslagen metod för att ta itu med denna ineffektivitet genom att klassificera ett nytt tillfälligt ström Installation till ett kluster av installationer med liknande lastmönster. Genom att göra så, kan dimensioneringen av generatorer och kraftproduktion planering optimeras därigenom förbättra systemets effektivitet. Load prognoser för Tillfälliga Kraft installationer är ook användbar Medan en tillfällig ström Installationen är i drift. En ‘Prognoser Real-Time’ Föreslagen metod är att använda övervakade lastdata strömmas till en server att förutse belastningen två timmar eller mer i förväg. Genom att göra så, kan praktiska åtgärder vidtas i realtid för att möta oväntade höga och låga effektbehov och därigenom förbättra systemets tillförlitlighet.
Li, Mao Li. "Spatial-temporal classification enhancement via 3-D iterative filtering for multi-temporal Very-High-Resolution satellite images". The Ohio State University, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=osu1514939565470669.
Texto completo da fonteChhajed, Tejashree Rakumar. "Deploying contrail forecasting service to reduce the impact of aviation on Environment". Master's thesis, Universitätsbibliothek Chemnitz, 2016. http://nbn-resolving.de/urn:nbn:de:bsz:ch1-qucosa-202587.
Texto completo da fonteUtterberg, Oscar, e Martin Rand. "Klassificering av reservdelar för effektivare reservdelshantering". Thesis, Tekniska Högskolan, Högskolan i Jönköping, JTH, Industriell organisation och produktion, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:hj:diva-41665.
Texto completo da fontePurpose–The purpose of this study was to find a classification tool that can ease the decision-making process of spare parts planning and forecasting. To accomplish this, three research questions were formulated; Which analysis tools can be used for a systematic classification of spare parts into different groups? How can the classified groups be used when planning and forecasting spare parts? How can the forecasting be done for the different classified groupsconsidering thecustomer service level? Method– The study was deductively through theory-building, with both an empirical case study and analytical conceptual approach. The methods used were; litterateur research, interviews and collection of secondary data. The litterateur research has been conducted in the areas; spare parts classification and forecasting. Findings– The finding of this study was that a multi criteria method is needed for a systematic classification of spare parts, because of the complex nature of spare part handling. The classification model can then be used for multiple tasks. The tasks that this study found were; help in deciding the customer service level, help in choosing forecast method for the different spare part groups and finding the spare parts that have shifted in demand trend. Implications– The classification model intends to ease companies spare parts planning and forecasting process. With help from the model the case company should have a more effective process now in choosing which customer service level and forecasting method to use for their spare part. Limitations– This studies limitation was that only one case company was studied because of time constraints. This makes the modified model very company specific and needs to be further validated on other companies. Keywords– Classification, Spare parts classification, Decision support, Forecasting, Spare parts forecasting, customer service level
Goehry, Benjamin. "Prévision multi-échelle par agrégation de forêts aléatoires. Application à la consommation électrique". Thesis, Université Paris-Saclay (ComUE), 2019. http://www.theses.fr/2019SACLS461/document.
Texto completo da fonteThis thesis has two objectives. A first objective concerns the forecast of a total load in the context of Smart Grids using approaches that are based on the bottom-up forecasting method. The second objective is based on the study of random forests when observations are dependent, more precisely on time series. In this context, we are extending the consistency results of Breiman’s random forests as well as the convergence rates for a simplified random forest that have both been hitherto only established for independent and identically distributed observations. The last contribution on random forests describes a new methodology that incorporates the time-dependent structure in the construction of forests and thus have a gain in performance in the case of time series, illustrated with an application of load forecasting of a building
Tran, Thai Thanh, Quang Xuan Ngo, Hieu Hoang Ha e Nhan Phan Nguyen. "Short-term forecasting of salinity intrusion in Ham Luong river, Ben Tre province using Simple Exponential Smoothing method". Technische Universität Dresden, 2019. https://tud.qucosa.de/id/qucosa%3A70822.
Texto completo da fonteXâm nhập mặn có thể gây tác động xấu đến đời sống con người, tuy nhiên nó hoàn toàn có thể dự báo được. Cho nên, một điều quan trọng là tìm được phương pháp kỹ thuật phù hợp để dự báo và giám sát xâm nhập mặn trên sông. Trong bài báo này, chúng tôi sử dụng phương pháp Simple Exponential Smoothing để dự báo xâm nhập mặn trên sông Hàm Luông, tỉnh Bến Tre. Kết quả cho thấy mô hình dự báo phù hợp cho các vị trí An Thuận, Sơn Đốc, và Phú Khánh. Tuy nhiên, các vị trí Mỹ Hóa, An Hiệp, và Vàm Mơn có thể tìm các phương pháp khác phù hợp hơn. Phương pháp Simple Exponential Smoothing rất dễ ứng dụng trong quản lý nguồn nước dựa vào việc cảnh báo xâm nhập mặn.
Köhler, Thomas, Norbert Pengel, Jana Riedel e Werner Wollersheim. "Forecasting EduTech for the next decade. Scenario development teaching patterns in general versus academic education". TUDpress, 2019. https://tud.qucosa.de/id/qucosa%3A36572.
Texto completo da fonteDinh, Thi Lan Anh. "Crop yield simulation using statistical and machine learning models. From the monitoring to the seasonal and climate forecasting". Electronic Thesis or Diss., Sorbonne université, 2022. http://www.theses.fr/2022SORUS425.
Texto completo da fonteWeather and climate strongly impact crop yields. Many studies based on different techniques have been done to measure this impact. This thesis focuses on statistical models to measure the sensitivity of crops to weather conditions based on historical records. When using a statistical model, a critical difficulty arises when data is scarce, which is often the case with statistical crop modelling. There is a high risk of overfitting if the model development is not done carefully. Thus, careful validation and selection of statistical models are major concerns of this thesis. Two statistical approaches are developed. The first one uses linear regression with regularization and leave-one-out cross-validation (or LOO), applied to Robusta coffee in the main coffee-producing area of Vietnam (i.e. the Central Highlands). Coffee is a valuable commodity crop, sensitive to weather, and has a very complex phenology due to its perennial nature. Results suggest that precipitation and temperature information can be used to forecast the yield anomaly with 3–6 months' anticipation depending on the location. Estimates of Robusta yield at the end of the season show that weather explains up to 36 % of historical yield anomalies. The first approach using LOO is widely used in the literature; however, it can be misused for many reasons: it is technical, misinterpreted, and requires experience. As an alternative, the “leave-two-out nested cross-validation” (or LTO) approach, is proposed to choose the suitable model and assess its true generalization ability. This method is sophisticated but straightforward; its benefits are demonstrated for Robusta coffee in Vietnam and grain maize in France. In both cases, a simpler model with fewer potential predictors and inputs is more appropriate. Using only the LOO method, without any regularization, can be highly misleading as it encourages choosing a model that overfits the data in an indirect way. The LTO approach is also useful in seasonal forecasting applications. The end-of-season grain maize yield estimates suggest that weather can account for more than 40 % of the variability in yield anomaly. Climate change's impacts on coffee production in Brazil and Vietnam are also studied using climate simulations and suitability models. Climate data are, however, biased compared to the real-world climate. Therefore, many “bias correction” methods (called here instead “calibration”) have been introduced to correct these biases. An up-to-date review of the available methods is provided to better understand each method's assumptions, properties, and applicative purposes. The climate simulations are then calibrated by a quantile-based method before being used in the suitability models. The suitability models are developed based on census data of coffee areas, and potential climate variables are based on a review of previous studies using impact models for coffee and expert recommendations. Results show that suitable arabica areas in Brazil could decrease by about 26 % by the mid-century in the high-emissions scenario, while the decrease is surprisingly high for Vietnamese Robusta coffee (≈ 60 %). Impacts are significant at low elevations for both coffee types, suggesting potential shifts in production to higher locations. The used statistical approaches, especially the LTO technique, can contribute to the development of crop modelling. They can be applied to a complex perennial crop like coffee or more industrialized annual crops like grain maize. They can be used in seasonal forecasts or end-of-season estimations, which are helpful in crop management and monitoring. Estimating the future crop suitability helps to anticipate the consequences of climate change on the agricultural system and to define adaptation or mitigation strategies. Methodologies used in this thesis can be easily generalized to other cultures and regions worldwide
Cullmann, Johannes. "Online flood forecasting in fast responding catchments on the basis of a synthesis of artificial neural networks and process models". Doctoral thesis, Technische Universität Dresden, 2006. https://tud.qucosa.de/id/qucosa%3A24948.
Texto completo da fontePohlmann, Tobias, e Friedrich Bernhard. "A combined method to forecast and estimate traffic demand in urban networks". Elsevier, 2013. https://publish.fid-move.qucosa.de/id/qucosa%3A33932.
Texto completo da fonteEngström, Olof. "Deep Learning for Anomaly Detection in Microwave Links : Challenges and Impact on Weather Classification". Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-276676.
Texto completo da fonteArtificiell intelligens har fått mycket uppmärksamhet inom olika teknik- och vetenskapsområden på grund av dess många lovande tillämpningar. I dagens samhälle är väderklassificeringsmodeller med hög noggrannhet av yttersta vikt. Ett alternativ till att använda konventionell väderradar är att använda uppmätta dämpningsdata i mikrovågslänkar som indata till djupinlärningsbaserade väderklassificeringsmodeller. Detektering av avvikelser i uppmätta dämpningsdata är av stor betydelse eftersom en klassificeringsmodells pålitlighet minskar om träningsdatat innehåller avvikelser. Att utforma en noggrann klassificeringsmodell är svårt på grund av bristen på fördefinierade kännetecken för olika typer av väderförhållanden, och på grund av de specifika domänkrav som ofta ställs när det gäller exekveringstid och detekteringskänslighet. I det här examensarbetet undersöker vi förhållandet mellan avvikelser i uppmätta dämpningsdata från mikrovågslänkar, och felklassificeringar gjorda av en väderklassificeringsmodell. För detta ändamål utvärderar vi avvikelsedetektering inom ramen för väderklassificering med hjälp av två djupinlärningsmodeller, baserade på long short-term memory-nätverk (LSTM) och faltningsnätverk (CNN). Vi utvärderar genomförbarhet och generaliserbarhet av den föreslagna metodiken i en industriell fallstudie hos Ericsson AB. Resultaten visar att båda föreslagna metoder kan upptäcka avvikelser som korrelerar med felklassificeringar gjorda av väderklassificeringsmodellen. LSTM-modellen presterade bättre än CNN-modellen både med hänsyn till toppprestanda på en länk och med hänsyn till genomsnittlig prestanda över alla 5 testade länkar, men CNNmodellens prestanda var mer konsistent.
Treiber, Martin, e Arne Kesting. "Evidence of Convective Instability in Congested Traffic Flow: A Systematic Empirical and Theoretical Investigation". Elsevier, 2011. https://publish.fid-move.qucosa.de/id/qucosa%3A33815.
Texto completo da fonteHahmann, Martin, Claudio Hartmann, Lars Kegel, Dirk Habich e Wolfgang Lehner. "Big by blocks: Modular Analytics". De Gruyter, 2016. https://tud.qucosa.de/id/qucosa%3A72848.
Texto completo da fonteAbdoun, Oussama. "Analyse spatiotemporelle de données MEA pour l'étude de la dynamique de l'activité de la moelle épinière et du tronc cérébral immatures chez la souris". Thesis, Bordeaux 1, 2012. http://www.theses.fr/2012BOR15266/document.
Texto completo da fonteImmature neural networks generate a peculiar type of activity that persists even in the absence of electrical inputs and was termed for this reason “endogenous”or “spontaneous”. This activity is ubiquitous and was found involved in a wide range of developmental events. In vitro, it can be observed as calcium or electrical waves propagating over great distances, often invading the whole preparation,but its dynamics remain poorly described. In order to somewhat fill this gap,we used multielectrode arrays (MEAs) to characterise the spontaneous rhythmic activity in the mouse developing spinal cord, in both acute and cultured isolated hindbrain-spinal cord preparations.To extract relevant information from the massive amounts of data yielded by MEA recordings, adapted analysis tools are needed. Thus, we have developedmethods for the detection, classification and mapping of spatiotemporal patternsof activity in multichannel data. Our mapping approach is based on the thin plates pline interpolation and includes the possibility to combine maps of activity with anatomical or stained data for multimodal imaging.These methods allowed us to analyse in great detail the evolution of spontaneousactivity at early stages (E12.5–E15.5). In addition, we have localised theinitiation site of E14.5 activity in the medulla and shown that it matches a densemidline population of serotoninergic neurons, suggesting a new role for 5-HTpathways in the maturation of spinal networks. Finally, we have recorded andtracked spontaneous limb movements of E14.5 embryos and found that features of motility were consistent with patterns of spinal activity
Ghibellini, Alessandro. "Trend prediction in financial time series: a model and a software framework". Master's thesis, Alma Mater Studiorum - Università di Bologna, 2021. http://amslaurea.unibo.it/24708/.
Texto completo da fonteShaif, Ayad. "Predictive Maintenance in Smart Agriculture Using Machine Learning : A Novel Algorithm for Drift Fault Detection in Hydroponic Sensors". Thesis, Mittuniversitetet, Institutionen för informationssystem och –teknologi, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:miun:diva-42270.
Texto completo da fonte