“Data-Driven Modeling: Using MATLAB® in Water Resources and Environmental Engineering” provides a systematic account of major concepts and methodologies for data-driven models and presents a unified framework that makes the subject more accessible to and applicable for researchers and practitioners. It integrates important theories and applications of data-driven models and uses them to deal with a wide range of problems in the field of water resources and environmental engineering such as hydrological forecasting, flood analysis, water quality monitoring, regionalizing climatic data, and general function approximation. The book presents the statistical-based models including basic statistical analysis, nonparametric and logistic regression methods, time series analysis and modeling, and support vector machines. It also deals with the analysis and modeling based on artificial intelligence techniques including static and dynamic neural networks, statistical neural networks, fuzzy inference systems, and fuzzy regression. The book also discusses hybrid models as well as multi-model data fusion to wrap up the covered models and techniques. The source files of relatively simple and advanced programs demonstrating how to use the models are presented together with practical advice on how to best apply them. The programs, which have been developed using the MATLAB® unified platform, can be found on extras.springer.com. The main audience of this book includes graduate students in water resources engineering, environmental engineering, agricultural engineering, and natural resources engineering. This book may be adapted for use as a senior undergraduate and graduate textbook by focusing on selected topics. Alternatively, it may also be used as a valuable resource book for practicing engineers, consulting engineers, scientists and others involved in water resources and environmental engineering.

This book gathers the proceedings of the 1st International Conference on Engineering, Applied Sciences and System Modeling (ICEASSM), a four-day event (18th–21st April 2017) held in Accra, Ghana. It focuses on research work promoting a better understanding of engineering problems through applied sciences and modeling, and on solutions generated in an African setting but with relevance to the world as a whole. The book provides a holistic overview of challenges facing Africa, and addresses various areas from research and development perspectives. Presenting contributions by scientists, engineers and experts hailing from a host of international institutions, the book offers original approaches and technological solutions to help solve real-world problems through research and knowledge sharing. Further, it explores promising opportunities for collaborative research on issues of scientific, economic and social development, making it of interest to researchers, scientists and practitioners looking to conduct research in disciplines such as water supply, control, civil engineering, statistical modeling, renewable energy and sustainable urban development.

A textbook covering data-science and machine learning methods for modelling and control in engineering and science, with Python and MATLAB®.

Changing climatic conditions, global warming trends, global population increase, water-related conflicts, and water shortages have resulted in changes in the water cycle, and hydrologic processes which were once thought to be simple are now known to be highly nonlinear. This compels the development of more sophisticated tools for enhanced and intensive water resources management. Data-driven tools have gained in popularity in recent years and have spawned a plethora of applications in water resources. Despite enjoying tremendous success in small-scale studies, there are very few applications to field-scale problems so far because various issues must be understood in order to make data-driven tools more practical to hydrologic applications. In the current research, three problem areas in hydrologic modeling have been identified that limit the applicability of data-driven tools: parameter specification, missing or incomplete data, and data compatibility. Each of these is studied in the present research, and solutions are provided. A new multiobjective calibration procedure in the form of Multiobjective Particle Swarm Optimization (MOPSO) is developed and tested. A solution to the problem of missing data is found through local least square imputation methodology. Furthermore, a downscaling algorithm is developed for the scale reconciliation problem. These tools are examined in various applications such as soil moisture forecasting, streamflow estimation, groundwater level forecasting, and downscaling of remotely sensed soil moisture. The current research only focuses on data-driven tools, and hence all the problems are examined in this same context. At the same time, the tools that are developed might well be appropriate for other modeling applications. This research addresses significant problems in the use of data-driven modeling tools so that they can be more effectively used in water resources management and hydrologic science. The results from the research show that the techniques developed and demonstrated here are sound and can help to remove some of the limitations in the use of data-driven tools, making them more attractive for application in hydrologic sciences.

Using important examples, this book showcases the potential of the latest data-based and data-driven methodologies for filter and control design. It discusses the most important classes of dynamic systems, along with the statistical and set membership analysis and design frameworks.

This book explores the interdisciplinary and transdisciplinary fields of energy systems, occupant behavior, thermal comfort, air quality and economic modelling across levels of building, communities and cities, through various data analytical approaches. It highlights the complex interplay of heating/cooling, ventilation and power systems in different processes, such as design, renovation and operation, for buildings, communities and cities. Methods from classical statistics, machine learning and artificial intelligence are applied into analyses for different building/urban components and systems. Knowledge from this book assists to accelerate sustainability of the society, which would contribute to a prospective improvement through data analysis in the liveability of both built and urban environment. This book targets a broad readership with specific experience and knowledge in data analysis, energy system, built environment and urban planning. As such, it appeals to researchers, graduate students, data scientists, engineers, consultants, urban scientists, investors and policymakers, with interests in energy flexibility, building/city resilience and climate neutrality.