An agent-based intelligent environmental monitoring system

Fairly rapid environmental changes call for continuous surveillance and on-line decision making. There are two main areas where IT technologies can be valuable. In this paper we present a multi-agent system for monitoring and assessing air-quality attributes, which uses data coming from a meteorological station. A community of software agents is assigned to monitor and validate measurements coming from several sensors, to assess air-quality, and, finally, to fire alarms to appropriate recipients, when needed. Data mining techniques have been used for adding data-driven, customized intelligence into agents. The architecture of the developed system, its domain ontology, and typical agent interactions are presented. Finally, the deployment of a real-world test case is demonstrated.Comment: Multi-Agent Systems, Intelligent Applications, Data Mining, Inductive Agents, Air-Quality Monitorin

Machine learning for regional crop yield forecasting in Europe

Crop yield forecasting at national level relies on predictors aggregated from smaller spatial units to larger ones according to harvested crop areas. Such crop areas come from land cover maps or reported statistics, both of which can have errors and uncertainties. Sub-national or regional crop yield forecasting minimizes the propagation of these errors to some extent. In addition, regional forecasts provide added value and insights to stakeholders on regional differences within a country, which would otherwise compensate each other at national level. We propose a crop yield forecasting approach for multiple spatial levels based on regional crop yield forecasts from machine learning. Machine learning, with its data-driven approach, can leverage larger data sizes and capture nonlinear relationships between predictors and yield at regional level. We designed a generic machine learning workflow to demonstrate the benefits of regional crop yield forecasting in Europe. To evaluate the quality and usefulness of regional forecasts, we predicted crop yields for 35 case studies, including nine countries that are major producers of six crops (soft wheat, spring barley, sunflower, grain maize, sugar beets and potatoes). Machine learning models at regional level had lower normalized root mean squared errors (NRMSE) and uncertainty than a linear trend model, with Wilcoxon p-values of 3e-7 and 2e-7 for 60 days before harvest and end of season respectively. Similarly, regional machine learning forecasts aggregated to national level had lower NRMSEs than forecasts from an operational system in 18 out of 35 cases 60 days before harvest, with a Wilcoxon p-value of 0.95 indicating similar performance. Our models have room for improvement, especially during extreme years. Nevertheless, regional crop yield forecasts from machine learning and aggregated national forecasts provide a consistent forecasting method across spatial levels and insights from regional differences to support important policy decisions

WASOS: An Ontology for Modelling Traditional Knowledge of Sustainable Water Stewardship

Recent work and publications concerning sustainable water stewardship in Rajasthan (India) highlight how contemporary challenges are eroding traditional, communal approaches to water stewardship through mechanised extraction beyond the renewable capacities of ecosystems. Our work is focused on developing a formal ontology for modelling the knowledge of traditional water stewardship in India’s drylands by capturing the key constitutional elements of regenerative methods. Our method follows an iterative evolving prototype process for delivering the first version of the Ontology for Sustainable Water Stewardship (WASOS). The ontology contains a moderate number of high-level classes and properties that represent the water management decisionmaking process. By making key relationships visible, we aim to support decision-making in complex catchments particularly where there are contested urban and rural claims on water

Exploding the myths: An introduction to artificial neural networks for prediction and forecasting

Artificial Neural Networks (ANNs), sometimes also called models for deep learning, are used extensively for the prediction of a range of environmental variables. While the potential of ANNs is unquestioned, they are surrounded by an air of mystery and intrigue, leading to a lack of understanding of their inner workings. This has led to the perpetuation of a number of myths, resulting in the misconception that applying ANNs primarily involves "throwing" a large amount of data at "black-box" software packages. While this is a convenient way to side-step the principles applied to the development of other types of models, this comes at significant cost in terms of the usefulness of the resulting models. To address these issues, this inroductory overview paper explodes a number of the common myths surrounding the use of ANNs and outlines state-of-the-art approaches to developing ANNs that enable them to be applied with confidence in practice

Eight grand challenges in socio-environmental systems modeling

Modeling is essential to characterize and explore complex societal and environmental issues in systematic and collaborative ways. Socio-environmental systems (SES) modeling integrates knowledge and perspectives into conceptual and computational tools that explicitly recognize how human decisions affect the environment. Depending on the modeling purpose, many SES modelers also realize that involvement of stakeholders and experts is fundamental to support social learning and decision-making processes for achieving improved environmental and social outcomes. The contribution of this paper lies in identifying and formulating grand challenges that need to be overcome to accelerate the development and adaptation of SES modeling. Eight challenges are delineated: bridging epistemologies across disciplines; multi-dimensional uncertainty assessment and management; scales and scaling issues; combining qualitative and quantitative methods and data; furthering the adoption and impacts of SES modeling on policy; capturing structural changes; representing human dimensions in SES; and leveraging new data types and sources. These challenges limit our ability to effectively use SES modeling to provide the knowledge and information essential for supporting decision making. Whereas some of these challenges are not unique to SES modeling and may be pervasive in other scientific fields, they still act as barriers as well as research opportunities for the SES modeling community. For each challenge, we outline basic steps that can be taken to surmount the underpinning barriers. Thus, the paper identifies priority research areas in SES modeling, chiefly related to progressing modeling products, processes and practices

Staging of Schizophrenia with the Use of PANSS: An International Multi-Center Study

Introduction: A specific clinically relevant staging model for schizophrenia has not yet been developed. The aim of the current study was to evaluate the factor structure of the PANSS and develop such a staging method.Methods: Twenty-nine centers from 25 countries contributed 2358 patients aged 37.21 ± 11.87 years with schizophrenia. Analysis of covariance, Exploratory Factor Analysis, Discriminant Function Analysis, and inspection of resultant plots were performed.Results: Exploratory Factor Analysis returned 5 factors explaining 59% of the variance (positive, negative, excitement/hostility, depression/anxiety, and neurocognition). The staging model included 4 main stages with substages that were predominantly characterized by a single domain of symptoms (stage 1: positive; stages 2a and 2b: excitement/hostility; stage 3a and 3b: depression/anxiety; stage 4a and 4b: neurocognition). There were no differences between sexes. The Discriminant Function Analysis developed an algorithm that correctly classified >85% of patients.Discussion: This study elaborates a 5-factor solution and a clinical staging method for patients with schizophrenia. It is the largest study to address these issues among patients who are more likely to remain affiliated with mental health services for prolonged periods of time.<br /

Anonymized survey responses from two Inception Workshops

To evaluate the effectiveness of the Inception Workshop approach we conducted a survey, in two experimental workshops. The survey was done in two stages, with pre- and post- workshop questionnaires. Both questionnaires included the same set of questions, in which the respondents were asked to evaluate their familiarity with environmental domain concepts and procedures, and IT tools and technologies. Anonymized responses are published here. The first workshop responses are in file `iw.q1.txt` and the second workshop in `iw.q2.txt`. Each row includes the question category (pre or post workshop), the question number (Qu1-Qu20), and the respondents answers in a 5-level Liker scale (from 1 to 5). A full list of questions are in the corresponding paper

A Miniature Data Repository on a Raspberry Pi

This work demonstrates a low-cost, miniature data repository proof-of-concept. Such a system needs to be resilient to power and network failures, and expose adequate processing power for persistent, long-term storage. Additional services are required for interoperable data sharing and visualization. We designed and implemented a software tool called Airchive to run on a Raspberry Pi, in order to assemble a data repository for archiving and openly sharing timeseries data. Airchive employs a relational database for storing data and implements two standards for sharing data (namely the Sensor Observation Service by the Open Geospatial Consortium and the Protocol for Metadata Harvesting by the Open Archives Initiative). The system is demonstrated in a realistic indoor air pollution data acquisition scenario in a four-month experiment evaluating its autonomy and robustness under power and network disruptions. A stress test was also conducted to evaluate its performance against concurrent client request