Hydrologic prediction using pattern recognition and soft-computing techniques

Several studies indicate that the data-driven models have proven to be potentially useful tools in hydrological modeling. Nevertheless, it is a common perception among researchers and practitioners that the usefulness of the system theoretic models is limited to forecast applications, and they cannot be used as a tool for scientific investigations. Also, the system-theoretic models are believed to be less reliable as they characterize the hydrological processes by learning the input-output patterns embedded in the dataset and not based on strong physical understanding of the system. It is imperative that the above concerns needs to be addressed before the data-driven models can gain wider acceptability by researchers and practitioners.In this research different methods and tools that can be adopted to promote transparency in the data-driven models are probed with the objective of extending the usefulness of data-driven models beyond forecast applications as a tools for scientific investigations, by providing additional insights into the underlying input-output patterns based on which the data-driven models arrive at a decision. In this regard, the utility of self-organizing networks (competitive learning and self-organizing maps) in learning the patterns in the input space is evaluated by developing a novel neural network model called the spiking modular neural networks (SMNNs). The performance of the SMNNs is evaluated based on its ability to characterize streamflows and actual evapotranspiration process. Also the utility of self-organizing algorithms, namely genetic programming (GP), is evaluated with regards to its ability to promote transparency in data-driven models. The robustness of the GP to evolve its own model structure with relevant parameters is illustrated by applying GP to characterize the actual-evapotranspiration process. The results from this research indicate that self-organization in learning, both in terms of self-organizing networks and self-organizing algorithms, could be adopted to promote transparency in data-driven models.In pursuit of improving the reliability of the data-driven models, different methods for incorporating uncertainty estimates as part of the data-driven model building exercise is evaluated in this research. The local-scale models are shown to be more reliable than the global-scale models in characterizing the saturated hydraulic conductivity of soils. In addition, in this research, the importance of model structure uncertainty in geophysical modeling is emphasized by developing a framework to account for the model structure uncertainty in geophysical modeling. The contribution of the model structure uncertainty to the predictive uncertainty of the model is shown to be larger than the uncertainty associated with the model parameters. Also it has been demonstrated that increasing the model complexity may lead to a better fit of the function, but at the cost of an increasing level of uncertainty. It is recommended that the effect of model structure uncertainty should be considered for developing reliable hydrological models

Engineering Education and Research Using MATLAB

MATLAB is a software package used primarily in the field of engineering for signal processing, numerical data analysis, modeling, programming, simulation, and computer graphic visualization. In the last few years, it has become widely accepted as an efficient tool, and, therefore, its use has significantly increased in scientific communities and academic institutions. This book consists of 20 chapters presenting research works using MATLAB tools. Chapters include techniques for programming and developing Graphical User Interfaces (GUIs), dynamic systems, electric machines, signal and image processing, power electronics, mixed signal circuits, genetic programming, digital watermarking, control systems, time-series regression modeling, and artificial neural networks

Data science for buildings, a multi-scale approach bridging occupants to smart-city energy planning

In a context of global carbon emission reduction goals, buildings have been identified to detain valuable energy-saving abilities. With the exponential increase of smart, connected building automation systems, massive amounts of data are now accessible for analysis. These coupled with powerful data science methods and machine learning algorithms present a unique opportunity to identify untapped energy-saving potentials from field information, and effectively turn buildings into active assets of the built energy infrastructure.However, the diversity of building occupants, infrastructures, and the disparities in collected information has produced disjointed scales of analytics that make it tedious for approaches to scale and generalize over the building stock.This coupled with the lack of standards in the sector has hindered the broader adoption of data science practices in the field, and engendered the following questioning:How can data science facilitate the scaling of approaches and bridge disconnected spatiotemporal scales of the built environment to deliver enhanced energy-saving strategies?This thesis focuses on addressing this interrogation by investigating data-driven, scalable, interpretable, and multi-scale approaches across varying types of analytical classes. The work particularly explores descriptive, predictive, and prescriptive analytics to connect occupants, buildings, and urban energy planning together for improved energy performances.First, a novel multi-dimensional data-mining framework is developed, producing distinct dimensional outlines supporting systematic methodological approaches and refined knowledge discovery. Second, an automated building heat dynamics identification method is put forward, supporting large-scale thermal performance examination of buildings in a non-intrusive manner. The method produced 64\% of good quality model fits, against 14\% close, and 22\% poor ones out of 225 Dutch residential buildings. %, which were open-sourced in the interest of developing benchmarks. Third, a pioneering hierarchical forecasting method was designed, bridging individual and aggregated building load predictions in a coherent, data-efficient fashion. The approach was evaluated over hierarchies of 37, 140, and 383 nodal elements and showcased improved accuracy and coherency performances against disjointed prediction systems.Finally, building occupants and urban energy planning strategies are investigated under the prism of uncertainty. In a neighborhood of 41 Dutch residential buildings, occupants were determined to significantly impact optimal energy community designs in the context of weather and economic uncertainties.Overall, the thesis demonstrated the added value of multi-scale approaches in all analytical classes while fostering best data-science practices in the sector from benchmarks and open-source implementations

Challenges in nonlinear structural dynamics: New optimisation perspectives

nalysis of structural dynamics is of fundamental importance to countless engineering applications. Analyses in both research and industrial settings have traditionally relied on linear or close to linear approximations of the underlying physics. Perhaps the most pervasive framework, modal analysis, has become the default framework for consideration of linear dynamics. Modern hardware and software solutions have placed linear analysis of structural dynamics squarely in the mainstream. However, as demands for stronger and lighter structures increase, and as advanced manufacturing enables more and more intricate geometries, the assumption of linearity is becoming less and less realistic. This thesis envisages three grand challenges for the treatment of nonlinearity in structural dynamics. These are: nonlinear system identification, exact solutions to nonlinear differential equations, and a nonlinear extension to linear modal analysis. Of these challenges, this thesis presents results pertaining to the latter two. The first component of this thesis is the consideration of methods that may yield exact solutions to nonlinear differential equations. Here, the task of finding an exact solution is cast as a heuristic search problem. The structure of the search problem is analysed with a view to motivate methods that are predisposed to finding exact solutions. To this end, a novel methodology, the affine regression tree, is proposed. The novel approach is compared against alternatives from the literature in an expansive benchmark study. Also considered, are nonlinear extensions to linear modal analysis. Historically, several frameworks have been proposed, each of which is able to retain only a subset of the properties of the linear case. It is argued here that retention of the utilities of linear modal analysis should be viewed as the criteria for a practical nonlinear modal decomposition. A promising direction is seen to be the recently-proposed framework of Worden and Green. The approach takes a machine-learning viewpoint that requires statistical independence between the modal coordinates. In this thesis, a robust consideration of the method from several directions is attempted. Results from several analyses demonstrate that statistical-independence and other inductive biases can be sufficient for a meaningful nonlinear modal decomposition, opening the door to a practical, nonlinear extension to modal analysis. The results in this thesis take small but positive steps towards two pressing challenges facing nonlinear structural dynamics. It is hoped that further work will be able to build upon the results presented here to develop a greater understanding and treatment of nonlinearity in structural dynamics and elsewhere

New strategies for efficient and practical genetic programming.

2006/2007In the last decades, engineers and decision makers expressed a growing interest in the development of effective modeling and simulation methods to understand or predict the behavior of many phenomena in science and engineering. Many of these phenomena are translated in mathematical models for convenience and to carry out an easy interpretation. Methods commonly employed for this purpose include, for example, Neural Networks, Simulated Annealing, Genetic Algorithms, Tabu search, and so on. These methods all seek for the optimal or near optimal values of a predefined set of parameters of a model built a priori. But in this case, a suitable model should be known beforehand. When the form of this model cannot be found, the problem can be seen from another level where the goal is to find a program or a mathematical representation which can solve the problem. According to this idea the modeling step is performed automatically thanks to a quality criterion which drives the building process. In this thesis, we focus on the Genetic Programming (GP) approach as an automatic method for creating computer programs by means of artificial evolution based upon the original contributions of Darwin and Mendel. While GP has proven to be a powerful means for coping with problems in which finding a solution and its representation is difficult, its practical applicability is still severely limited by several factors. First, the GP approach is inherently a stochastic process. It means there is no guarantee to obtain a satisfactory solution at the end of the evolutionary loop. Second, the performances on a given problem may be strongly dependent on a broad range of parameters, including the number of variables involved, the quantity of data for each variable, the size and composition of the initial population, the number of generations and so on. On the contrary, when one uses Genetic Programming to solve a problem, he has two expectancies: on the one hand, maximize the probability to obtain an acceptable solution, and on the other hand, minimize the amount of computational resources to get this solution. Initially we present innovative and challenging applications related to several fields in science (computer science and mechanical science) which participate greatly in the experience gained in the GP field. Then we propose new strategies for improving the performances of the GP approach in terms of efficiency and accuracy. We probe our approach on a large set of benchmark problems in three different domains. Furthermore we introduce a new approach based on GP dedicated to symbolic regression of multivariate data-sets where the underlying phenomenon is best characterized by a discontinuous function. These contributions aim to provide a better understanding of the key features and the underlying relationships which make enhancements successful in improving the original algorithm.Negli ultimi anni, ingegneri e progettisti hanno espresso un interesse crescente nello sviluppo di nuovi metodi di simulazione e di modellazione per comprendere e predire il comportamento di diversi fenomeni sia in ambito scientifico che ingegneristico. Molti di questi fenomeni vengono descritti attraverso modelli matematici che ne facilitano l'interpretazione. A questo fine, i metodi più comunemente impiegati sono, le tecniche basate sui Reti Neurali, Simulated Annealing, gli Algoritmi Genetici, la ricerca Tabu, ecc. Questi metodi vanno a determinare i valori ottimali o quasi ottimali dei parametri di un modello costruito a priori. E evidente che in tal caso, si dovrebbe conoscere in anticipo un modello idoneo. Quando ciò non è possibile, il problema deve essere considerato da un altro punto di vista: l'obiettivo è trovare un programma o una rappresentazione matematica che possano risolvere il problema. A questo scopo, la fase di modellazione è svolta automaticamente in funzione di un criterio qualitativo che guida il processo di ricerca. Il tema di ricerca di questa tesi è la programmazione genetica (“Genetic Programming” che chiameremo GP) e le sue applicazioni. La programmazione genetica si può definire come un metodo automatico per la generazione di programmi attraverso una simulazione artificiale dei principi relativi all'evoluzione naturale basata sui contributi originali di Darwin e di Mendel. La programmazione genetica ha dimostrato di essere un potente mezzo per affrontare quei problemi in cui trovare una soluzione e la sua rappresentazione è difficile. Però la sua applicabilità rimane severamente limitata da diversi fattori. In primo luogo, il metodo GP è inerentemente un processo stocastico. Ciò significa che non garantisce che una soluzione soddisfacente sarà trovata alla fine del ciclo evolutivo. In secondo luogo, le prestazioni su un dato problema dipendono fortemente da una vasta gamma di parametri, compresi il numero di variabili impiegate, la quantità di dati per ogni variabile, la dimensione e la composizione della popolazione iniziale, il numero di generazioni e così via. Al contrario, un utente della programmazione genetica ha due aspettative: da una parte, massimizzare la probabilità di ottenere una soluzione accettabile, e dall'altra, minimizzare la quantità di risorse di calcolo per ottenerla. Nella fase iniziale di questo lavoro sono state considerate delle applicazioni particolarmente innovative relative a diversi campi della scienza (informatica e meccanica) che hanno contributo notevolmente all'esperienza acquisita nel campo della programmazione genetica. In questa tesi si propone un nuovo procedimento con lo scopo di migliorare le prestazioni della programmazione genetica in termini di efficienza ed accuratezza. Abbiamo testato il nostro approccio su un ampio insieme di benchmarks in tre domini applicativi diversi. Si propone inoltre una tecnica basata sul GP per la regressione simbolica di data-set multivariati dove il fenomeno di fondo è caratterizzato da una funzione discontinua. Questi contributi cercano di fornire una comprensione migliore degli elementi chiave e dei meccanismi interni che hanno consentito il miglioramento dell'algoritmo originale.XX Ciclo198

Exploring the adoption of a conceptual data analytics framework for subsurface energy production systems: a study of predictive maintenance, multi-phase flow estimation, and production optimization

Als die Technologie weiter fortschreitet und immer stärker in der Öl- und Gasindustrie integriert wird, steht eine enorme Menge an Daten in verschiedenen Wissenschaftsdisziplinen zur Verfügung, die neue Möglichkeiten bieten, informationsreiche und handlungsorientierte Informationen zu gewinnen. Die Konvergenz der digitalen Transformation mit der Physik des Flüssigkeitsflusses durch poröse Medien und Pipeline hat die Entwicklung und Anwendung von maschinellem Lernen (ML) vorangetrieben, um weiteren Mehrwert aus diesen Daten zu gewinnen. Als Folge hat sich die digitale Transformation und ihre zugehörigen maschinellen Lernanwendungen zu einem neuen Forschungsgebiet entwickelt. Die Transformation von Brownfields in digitale Ölfelder kann bei der Energieproduktion helfen, indem verschiedene Ziele erreicht werden, einschließlich erhöhter betrieblicher Effizienz, Produktionsoptimierung, Zusammenarbeit, Datenintegration, Entscheidungsunterstützung und Workflow-Automatisierung. Diese Arbeit zielt darauf ab, ein Rahmenwerk für diese Anwendungen zu präsentieren, insbesondere durch die Implementierung virtueller Sensoren, Vorhersageanalytik mithilfe von Vorhersagewartung für die Produktionshydraulik-Systeme (mit dem Schwerpunkt auf elektrischen Unterwasserpumpen) und präskriptiven Analytik für die Produktionsoptimierung in Dampf- und Wasserflutprojekten. In Bezug auf virtuelle Messungen ist eine genaue Schätzung von Mehrphasenströmen für die Überwachung und Verbesserung von Produktionsprozessen entscheidend. Diese Studie präsentiert einen datengetriebenen Ansatz zur Berechnung von Mehrphasenströmen mithilfe von Sensormessungen in elektrischen untergetauchten Pumpbrunnen. Es wird eine ausführliche exploratorische Datenanalyse durchgeführt, einschließlich einer Ein Variablen Studie der Zielausgänge (Flüssigkeitsrate und Wasseranteil), einer Mehrvariablen-Studie der Beziehungen zwischen Eingaben und Ausgaben sowie einer Datengruppierung basierend auf Hauptkomponentenprojektionen und Clusteralgorithmen. Feature Priorisierungsexperimente werden durchgeführt, um die einflussreichsten Parameter in der Vorhersage von Fließraten zu identifizieren. Die Modellvergleich erfolgt anhand des mittleren absoluten Fehlers, des mittleren quadratischen Fehlers und des Bestimmtheitskoeffizienten. Die Ergebnisse zeigen, dass die CNN-LSTM-Netzwerkarchitektur besonders effektiv bei der Zeitreihenanalyse von ESP-Sensordaten ist, da die 1D-CNN-Schichten automatisch Merkmale extrahieren und informative Darstellungen von Zeitreihendaten erzeugen können. Anschließend wird in dieser Studie eine Methodik zur Umsetzung von Vorhersagewartungen für künstliche Hebesysteme, insbesondere bei der Wartung von Elektrischen Untergetauchten Pumpen (ESP), vorgestellt. Conventional maintenance practices for ESPs require extensive resources and manpower, and are often initiated through reactive monitoring of multivariate sensor data. Um dieses Problem zu lösen, wird die Verwendung von Hauptkomponentenanalyse (PCA) und Extreme Gradient Boosting Trees (XGBoost) zur Analyse von Echtzeitsensordaten und Vorhersage möglicher Ausfälle in ESPs eingesetzt. PCA wird als unsupervised technique eingesetzt und sein Ausgang wird weiter vom XGBoost-Modell für die Vorhersage des Systemstatus verarbeitet. Das resultierende Vorhersagemodell hat gezeigt, dass es Signale von möglichen Ausfällen bis zu sieben Tagen im Voraus bereitstellen kann, mit einer F1-Bewertung größer als 0,71 im Testset. Diese Studie integriert auch Model-Free Reinforcement Learning (RL) Algorithmen zur Unterstützung bei Entscheidungen im Rahmen der Produktionsoptimierung. Die Aufgabe, die optimalen Injektionsstrategien zu bestimmen, stellt Herausforderungen aufgrund der Komplexität der zugrundeliegenden Dynamik, einschließlich nichtlinearer Formulierung, zeitlicher Variationen und Reservoirstrukturheterogenität. Um diese Herausforderungen zu bewältigen, wurde das Problem als Markov-Entscheidungsprozess reformuliert und RL-Algorithmen wurden eingesetzt, um Handlungen zu bestimmen, die die Produktion optimieren. Die Ergebnisse zeigen, dass der RL-Agent in der Lage war, den Netto-Barwert (NPV) durch kontinuierliche Interaktion mit der Umgebung und iterative Verfeinerung des dynamischen Prozesses über mehrere Episoden signifikant zu verbessern. Dies zeigt das Potenzial von RL-Algorithmen, effektive und effiziente Lösungen für komplexe Optimierungsprobleme im Produktionsbereich zu bieten.As technology continues to advance and become more integrated in the oil and gas industry, a vast amount of data is now prevalent across various scientific disciplines, providing new opportunities to gain insightful and actionable information. The convergence of digital transformation with the physics of fluid flow through porous media and pipelines has driven the advancement and application of machine learning (ML) techniques to extract further value from this data. As a result, digital transformation and its associated machine-learning applications have become a new area of scientific investigation. The transformation of brownfields into digital oilfields can aid in energy production by accomplishing various objectives, including increased operational efficiency, production optimization, collaboration, data integration, decision support, and workflow automation. This work aims to present a framework of these applications, specifically through the implementation of virtual sensing, predictive analytics using predictive maintenance on production hydraulic systems (with a focus on electrical submersible pumps), and prescriptive analytics for production optimization in steam and waterflooding projects. In terms of virtual sensing, the accurate estimation of multi-phase flow rates is crucial for monitoring and improving production processes. This study presents a data-driven approach for calculating multi-phase flow rates using sensor measurements located in electrical submersible pumped wells. An exhaustive exploratory data analysis is conducted, including a univariate study of the target outputs (liquid rate and water cut), a multivariate study of the relationships between inputs and outputs, and data grouping based on principal component projections and clustering algorithms. Feature prioritization experiments are performed to identify the most influential parameters in the prediction of flow rates. Model comparison is done using the mean absolute error, mean squared error and coefficient of determination. The results indicate that the CNN-LSTM network architecture is particularly effective in time series analysis for ESP sensor data, as the 1D-CNN layers are capable of extracting features and generating informative representations of time series data automatically. Subsequently, the study presented herein a methodology for implementing predictive maintenance on artificial lift systems, specifically regarding the maintenance of Electrical Submersible Pumps (ESPs). Conventional maintenance practices for ESPs require extensive resources and manpower and are often initiated through reactive monitoring of multivariate sensor data. To address this issue, the study employs the use of principal component analysis (PCA) and extreme gradient boosting trees (XGBoost) to analyze real-time sensor data and predict potential failures in ESPs. PCA is utilized as an unsupervised technique and its output is further processed by the XGBoost model for prediction of system status. The resulting predictive model has been shown to provide signals of potential failures up to seven days in advance, with an F1 score greater than 0.71 on the test set. In addition to the data-driven modeling approach, The present study also in- corporates model-free reinforcement learning (RL) algorithms to aid in decision-making in production optimization. The task of determining the optimal injection strategy poses challenges due to the complexity of the underlying dynamics, including nonlinear formulation, temporal variations, and reservoir heterogeneity. To tackle these challenges, the problem was reformulated as a Markov decision process and RL algorithms were employed to determine actions that maximize production yield. The results of the study demonstrate that the RL agent was able to significantly enhance the net present value (NPV) by continuously interacting with the environment and iteratively refining the dynamic process through multiple episodes. This showcases the potential for RL algorithms to provide effective and efficient solutions for complex optimization problems in the production domain. In conclusion, this study represents an original contribution to the field of data-driven applications in subsurface energy systems. It proposes a data-driven method for determining multi-phase flow rates in electrical submersible pumped (ESP) wells utilizing sensor measurements. The methodology includes conducting exploratory data analysis, conducting experiments to prioritize features, and evaluating models based on mean absolute error, mean squared error, and coefficient of determination. The findings indicate that a convolutional neural network-long short-term memory (CNN-LSTM) network is an effective approach for time series analysis in ESPs. In addition, the study implements principal component analysis (PCA) and extreme gradient boosting trees (XGBoost) to perform predictive maintenance on ESPs and anticipate potential failures up to a seven-day horizon. Furthermore, the study applies model-free reinforcement learning (RL) algorithms to aid decision-making in production optimization and enhance net present value (NPV)

Second CLIPS Conference Proceedings, volume 1

Topics covered at the 2nd CLIPS Conference held at the Johnson Space Center, September 23-25, 1991 are given. Topics include rule groupings, fault detection using expert systems, decision making using expert systems, knowledge representation, computer aided design and debugging expert systems

Artificial general intelligence: Proceedings of the Second Conference on Artificial General Intelligence, AGI 2009, Arlington, Virginia, USA, March 6-9, 2009

Artificial General Intelligence (AGI) research focuses on the original and ultimate goal of AI – to create broad human-like and transhuman intelligence, by exploring all available paths, including theoretical and experimental computer science, cognitive science, neuroscience, and innovative interdisciplinary methodologies. Due to the difficulty of this task, for the last few decades the majority of AI researchers have focused on what has been called narrow AI – the production of AI systems displaying intelligence regarding specific, highly constrained tasks. In recent years, however, more and more researchers have recognized the necessity – and feasibility – of returning to the original goals of the field. Increasingly, there is a call for a transition back to confronting the more difficult issues of human level intelligence and more broadly artificial general intelligence