Automatic Outlier Detection in Automated Water Quality Measurement Stations
Automatic Outlier Detection in Automated Water Quality Measurement Stations

Author: Atefeh Saberi

Publisher:

Published: 2015

Total Pages: 96

ISBN-13:

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Water quality monitoring stations are used to measure water quality at high frequency. For effective data management, the quality of the data must be evaluated. In a previously developed univariate method both outliers and faults were detected in the data measured by these stations by using exponential smoothing models that give one-step ahead forecasts and their confidence intervals. In the present study, the outlier detection step of the univariate method is improved by identifying an auto-regressive moving average model for a moving window of data and forecasting one-step ahead. The turbidity data measured at the inlet of a municipal treatment plant in Denmark is used as case study to compare the performance of the use of the two models. The results show that the forecasts made by the new model are more accurate. Also, inclusion of the new forecasting model in the univariate method shows satisfactory performance for detecting outliers and faults in the case study data.

Proceedings of 2019 Chinese Intelligent Automation Conference
Proceedings of 2019 Chinese Intelligent Automation Conference

Author: Zhidong Deng

Publisher: Springer

Published: 2019-09-07

Total Pages: 747

ISBN-13: 9813290501

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The proceedings present selected research papers from the CIAC2019, held in Jiangsu, China on September 20-22, 2019. It covers a wide range of topics including intelligent control, robotics, artificial intelligence, pattern recognition, unmanned systems, IoT and machine learning. It includes original research and the latest advances in the field of intelligent automation. Engineers and researchers from academia, industry, and government can gain valuable insights into solutions combining ideas from multiple disciplines in this field.

Watershed Management for Potable Water Supply
Watershed Management for Potable Water Supply

Author: National Research Council

Publisher: National Academies Press

Published: 2000-02-17

Total Pages: 569

ISBN-13: 0309172683

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In 1997, New York City adopted a mammoth watershed agreement to protect its drinking water and avoid filtration of its large upstate surface water supply. Shortly thereafter, the NRC began an analysis of the agreement's scientific validity. The resulting book finds New York City's watershed agreement to be a good template for proactive watershed management that, if properly implemented, will maintain high water quality. However, it cautions that the agreement is not a guarantee of permanent filtration avoidance because of changing regulations, uncertainties regarding pollution sources, advances in treatment technologies, and natural variations in watershed conditions. The book recommends that New York City place its highest priority on pathogenic microorganisms in the watershed and direct its resources toward improving methods for detecting pathogens, understanding pathogen transport and fate, and demonstrating that best management practices will remove pathogens. Other recommendations, which are broadly applicable to surface water supplies across the country, target buffer zones, stormwater management, water quality monitoring, and effluent trading.

Computational and Statistical Methods for Analysing Big Data with Applications
Computational and Statistical Methods for Analysing Big Data with Applications

Author: Shen Liu

Publisher: Academic Press

Published: 2015-11-20

Total Pages: 208

ISBN-13: 0081006519

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Due to the scale and complexity of data sets currently being collected in areas such as health, transportation, environmental science, engineering, information technology, business and finance, modern quantitative analysts are seeking improved and appropriate computational and statistical methods to explore, model and draw inferences from big data. This book aims to introduce suitable approaches for such endeavours, providing applications and case studies for the purpose of demonstration. Computational and Statistical Methods for Analysing Big Data with Applications starts with an overview of the era of big data. It then goes onto explain the computational and statistical methods which have been commonly applied in the big data revolution. For each of these methods, an example is provided as a guide to its application. Five case studies are presented next, focusing on computer vision with massive training data, spatial data analysis, advanced experimental design methods for big data, big data in clinical medicine, and analysing data collected from mobile devices, respectively. The book concludes with some final thoughts and suggested areas for future research in big data. Advanced computational and statistical methodologies for analysing big data are developed Experimental design methodologies are described and implemented to make the analysis of big data more computationally tractable Case studies are discussed to demonstrate the implementation of the developed methods Five high-impact areas of application are studied: computer vision, geosciences, commerce, healthcare and transportation Computing code/programs are provided where appropriate

Statistical Methods in Water Resources
Statistical Methods in Water Resources

Author: D.R. Helsel

Publisher: Elsevier

Published: 1993-03-03

Total Pages: 539

ISBN-13: 0080875084

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Data on water quality and other environmental issues are being collected at an ever-increasing rate. In the past, however, the techniques used by scientists to interpret this data have not progressed as quickly. This is a book of modern statistical methods for analysis of practical problems in water quality and water resources. The last fifteen years have seen major advances in the fields of exploratory data analysis (EDA) and robust statistical methods. The 'real-life' characteristics of environmental data tend to drive analysis towards the use of these methods. These advances are presented in a practical and relevant format. Alternate methods are compared, highlighting the strengths and weaknesses of each as applied to environmental data. Techniques for trend analysis and dealing with water below the detection limit are topics covered, which are of great interest to consultants in water-quality and hydrology, scientists in state, provincial and federal water resources, and geological survey agencies. The practising water resources scientist will find the worked examples using actual field data from case studies of environmental problems, of real value. Exercises at the end of each chapter enable the mechanics of the methodological process to be fully understood, with data sets included on diskette for easy use. The result is a book that is both up-to-date and immediately relevant to ongoing work in the environmental and water sciences.

Assessment of Treatment Plant Performance and Water Quality Data: A Guide for Students, Researchers and Practitioners
Assessment of Treatment Plant Performance and Water Quality Data: A Guide for Students, Researchers and Practitioners

Author: Marcos von Sperling

Publisher: IWA Publishing

Published: 2020-01-15

Total Pages: 668

ISBN-13: 1780409311

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This book presents the basic principles for evaluating water quality and treatment plant performance in a clear, innovative and didactic way, using a combined approach that involves the interpretation of monitoring data associated with (i) the basic processes that take place in water bodies and in water and wastewater treatment plants and (ii) data management and statistical calculations to allow a deep interpretation of the data. This book is problem-oriented and works from practice to theory, covering most of the information you will need, such as (a) obtaining flow data and working with the concept of loading, (b) organizing sampling programmes and measurements, (c) connecting laboratory analysis to data management, (e) using numerical and graphical methods for describing monitoring data (descriptive statistics), (f) understanding and reporting removal efficiencies, (g) recognizing symmetry and asymmetry in monitoring data (normal and log-normal distributions), (h) evaluating compliance with targets and regulatory standards for effluents and water bodies, (i) making comparisons with the monitoring data (tests of hypothesis), (j) understanding the relationship between monitoring variables (correlation and regression analysis), (k) making water and mass balances, (l) understanding the different loading rates applied to treatment units, (m) learning the principles of reaction kinetics and reactor hydraulics and (n) performing calibration and verification of models. The major concepts are illustrated by 92 fully worked-out examples, which are supported by 75 freely-downloadable Excel spreadsheets. Each chapter concludes with a checklist for your report. If you are a student, researcher or practitioner planning to use or already using treatment plant and water quality monitoring data, then this book is for you! 75 Excel spreadsheets are available to download.