The value of human data annotation for machine learning based anomaly detection in environmental systems

The Fourth-Revolution in the Water Sector Encounters the Digital Revolution

(2020) Manel Garrido-Baserba et al.   ENVIRONMENTAL SCIENCE & TECHNOLOGY

Active learning for anomaly detection in environmental data

(2020) Stefania Russo et al.   ENVIRONMENTAL MODELLING & SOFTWARE

A framework for automated anomaly detection in high frequency water-quality data from in situ sensors

(2019) Catherine Leigh et al.   SCIENCE OF THE TOTAL ENVIRONMENT

Interactive effects of foundation species on ecosystem functioning and stability in response to disturbance

(2019) Anita Narwani et al.   PROCEEDINGS OF THE ROYAL SOCIETY B-BIOLOGICAL SCIENCES

Environmental Data Science

(2018) Karina Gibert et al.   ENVIRONMENTAL MODELLING & SOFTWARE

Transforming data into knowledge for improved wastewater treatment operation: A critical review of techniques

(2018) Ll. Corominas et al.   ENVIRONMENTAL MODELLING & SOFTWARE

Clustering and Support Vector Regression for Water Demand Forecasting and Anomaly Detection

(2017) Antonio Candelieri   Water

A domain-independent methodology to analyze IoT data streams in real-time. A proof of concept implementation for anomaly detection from environmental data

(2016) Sergio Trilles et al.   International Journal of Digital Earth

Online flow cytometry reveals microbial dynamics influenced by concurrent natural and operational events in groundwater used for drinking water treatment

(2016) Michael D. Besmer et al.   Scientific Reports

One-class classification: taxonomy of study and review of techniques

(2014) Shehroz S. Khan et al.   KNOWLEDGE ENGINEERING REVIEW

The feasibility of automated online flow cytometry for in-situ monitoring of microbial dynamics in aquatic ecosystems

(2014) Michael D. Besmer et al.   Frontiers in Microbiology

An Efficient Approach for Outlier Detection with Imperfect Data Labels

(2013) Bo Liu et al.   IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING

Advanced monitoring of water systems using in situ measurement stations: data validation and fault detection

(2013) Janelcy Alferes et al.   WATER SCIENCE AND TECHNOLOGY

Functional unfold principal component analysis for automatic plant-based stress detection in grapevine

(2012) Annelies Baert et al.   FUNCTIONAL PLANT BIOLOGY

A nonlinear kernel Gaussian mixture model based inferential monitoring approach for fault detection and diagnosis of chemical processes

(2011) Jie Yu   CHEMICAL ENGINEERING SCIENCE

Anomaly Detection in Environmental Monitoring Networks [Application Notes]

(2011) James Bezdek et al.   IEEE Computational Intelligence Magazine

Abrupt Event Monitoring for Water Environment System Based on KPCA and SVM

(2011) Jianjun Ni et al.   IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT

Performance evaluation of fault detection methods for wastewater treatment processes

(2010) Lluís Corominas et al.   BIOTECHNOLOGY AND BIOENGINEERING

Anomaly detection

(2009) Varun Chandola et al.   ACM COMPUTING SURVEYS

Anomaly detection in streaming environmental sensor data: A data-driven modeling approach

(2009) David J. Hill et al.   ENVIRONMENTAL MODELLING & SOFTWARE

Real-time remote monitoring of small-scaled biological wastewater treatment plants by a multivariate statistical process control and neural network-based software sensors

(2008) Min Woo Lee et al.   PROCESS BIOCHEMISTRY

Combining multiway principal component analysis (MPCA) and clustering for efficient data mining of historical data sets of SBR processes

(2008) Kris Villez et al.   WATER SCIENCE AND TECHNOLOGY

Multivariate statistical monitoring of continuous wastewater treatment plants

(2007) Daniel Aguado et al.   ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE