A Comparison of Machine-Learning Methods to Select Socioeconomic Indicators in Cultural Landscapes

Cultural landscapes are regarded to be complex socioecological systems that originated as a result of the interaction between humanity and nature across time. Cultural landscapes present complex-system properties, including nonlinear dynamics among their components. There is a close relationship between socioeconomy and landscape in cultural landscapes, so that changes in the socioeconomic dynamic have an effect on the structure and functionality of the landscape. Several numerical analyses have been carried out to study this relationship, with linear regression models being widely used. However, cultural landscapes comprise a considerable amount of elements and processes, whose interactions might not be properly captured by a linear model. In recent years, machine-learning techniques have increasingly been applied to the field of ecology to solve regression tasks. These techniques provide sound methods and algorithms for dealing with complex systems under uncertainty. The term ‘machine learning’ includes a wide variety of methods to learn models from data. In this paper, we study the relationship between socioeconomy and cultural landscape (in Andalusia, Spain) at two different spatial scales aiming at comparing different regression models from a predictive-accuracy point of view, including model trees and neural or Bayesian networks

Recommendations for environmental baseline monitoring in areas of shale gas development

Environmental monitoring plays a key role in risk assessment and management of industrial operations where there is the potential for the release of contaminants to the environment (i.e. air and water) or for structural damage (i.e. seismicity). The shale-gas industry is one such industry. It is also new to the UK and so specific environmental regulation and other controls have been introduced only recently. Associated with this is a need to carry out monitoring to demonstrate that the management measures to minimise the risk to the environment are being effective. While much of the monitoring required is common to other industries and potentially polluting activities, there are a number of requirements specific to shale gas and to what is a new and undeveloped industry. This report presents recommendations for environmental monitoring associated with shale-gas activities and in particular the monitoring required to inform risk assessment and establish the pre-existing environmental conditions at a site and surrounding area. This baseline monitoring is essential to provide robust data and criteria for detecting any future adverse environmental changes caused by the shale-gas operations. Monitoring is therefore required throughout the lifecycle of a shale gas operation. During this lifecycle, the objectives of the monitoring will change, from baseline characterisation to operational and post-operational monitoring. Monitoring requirements will also change. This report focusses on good practice in baseline monitoring and places it in the context of the longer-term environmental monitoring programme, recognising the need to transition from the baseline condition and to establish criteria for detecting any changes within the regulatory framework. The core suite of environmental monitoring activities currently required to support regulatory compliance, i.e. meet environmental and other permit conditions, encompasses monitoring of seismicity, water quality (groundwater and surface water) and air quality. Recommendations for each of these are included in this report. Additionally, recommendations for a number of other types of environmental monitoring are included – radon in air, soil gas and ground motion (subsidence/uplift). These are not associated directly with regulatory compliance but can provide information to support interpretation of statutory monitoring results. They are also considered important for public reassurance. Health impacts arising from radon and damage caused by ground motion are both issues of public concern in relation to shale gas

INFORMATION SYSTEMS FACILITATING GROUNDWATER SUSTAINABILITY MANAGEMENT

Groundwater resources are a major source of drinking water and increasingly require management to be sustained. Achieving this requires IS (Information Systems) supporting the revelation of contamination information from data obtained via monitoring projects. For effective contamination control, its type, size, structure and degree must be unveiled. Unfortunately, when contamination occurs such holistic information is not readily available from monitoring data. Monitoring groundwater quality is limited to specific locations, namely the monitoring wells. Hence, from limited and fragmented data, contamination information must quickly be implied. This study analyzes alternatives of designing IS to facilitate contamination control from the limited sources of data. For this purpose we analyze the monitoring process and different methodologies for data collection from monitoring wells. We have analyzed the efficiency of the various methods with an aquifer domain comprising a small part of the Coastal Plain Aquifer (CPA) of Israel. The results suggest that systematic sampling approaches are the most efficient for attaining sustainability goals

Salford postgraduate annual research conference (SPARC) 2012 proceedings

These proceedings bring together a selection of papers from the 2012 Salford Postgraduate Annual Research Conference (SPARC). They reflect the breadth and diversity of research interests showcased at the conference, at which over 130 researchers from Salford, the North West and other UK universities presented their work. 21 papers are collated here from the humanities, arts, social sciences, health, engineering, environment and life sciences, built environment and business

Superstructure optimisation of a water minimisation network with a embedded multicontaminant electrodialysis model

A dissertation submitted to the Faculty of Engineering and the Built Environment, University of the Witwatersrand, Johannesburg, in fulfilment of the requirements for the degree of Master of Science in Engineering, 2016The water-energy nexus considers the relationship between water and energy resources. Increases in environmental degradation and social pressures in recent years have necessitated the development of manufacturing processes that are conservative with respect to both these resources, while maintaining financial viability. This can be achieved by process integration (PI); a holistic approach to design which emphasises the unity of processes. Within the realm of PI, water network synthesis (WNS) explores avenues for reuse, recycle and regeneration of effluent in order to minimise freshwater consumption and wastewater production. When regeneration is required, membrane-based treatment processes may be employed. These processes are energy intensive and result in a trade-off between water and energy minimisation, thus creating an avenue for optimisation. Previous work in WNS employed a black box approach to represent regenerators in water minimisation problems. However, this misrepresents the cost of regeneration and underestimates the energy requirements of a system. The aim of the research presented in this dissertation is to develop an integrated water regeneration network synthesis model to simultaneously minimise water and energy in a water network. A novel MINLP model for the design of an electrodialysis (ED) unit that is capable of treating a binary mixture of simple salts was developed from first principles. This ED model was embedded into a water network superstructure optimisation model, where the objective was to minimise freshwater and energy consumption, wastewater productions, and associated costs. The model was applied to a pulp and paper case study, considering several scenarios. Global optimisation of the integrated water network and ED design model, with variable contaminant removal ratios, was found to yield the best results. A total of 38% savings in freshwater, 68% reduction in wastewater production and 55% overall cost reduction were observed when compared with the original design. This model also led to a 80% reduction in regeneration (energy) cost.GS201

Environmental risk assessment in the mediterranean region using artificial neural networks

Los mapas auto-organizados han demostrado ser una herramienta apropiada para la clasificación y visualización de grupos de datos complejos. Redes neuronales, como los mapas auto-organizados (SOM) o las redes difusas ARTMAP (FAM), se utilizan en este estudio para evaluar el impacto medioambiental acumulativo en diferentes medios (aguas subterráneas, aire y salud humana). Los SOMs también se utilizan para generar mapas de concentraciones de contaminantes en aguas subterráneas simulando las técnicas geostadísticas de interpolación como kriging y cokriging. Para evaluar la confiabilidad de las metodologías desarrolladas en esta tesis, se utilizan procedimientos de referencia como puntos de comparación: la metodología DRASTIC para el estudio de vulnerabilidad en aguas subterráneas y el método de interpolación espacio-temporal conocido como Bayesian Maximum Entropy (BME) para el análisis de calidad del aire. Esta tesis contribuye a demostrar las capacidades de las redes neuronales en el desarrollo de nuevas metodologías y modelos que explícitamente permiten evaluar las dimensiones temporales y espaciales de riesgos acumulativos

A comprehensive survey on cultural algorithms

Peer reviewedPostprin

Application of geostatistics to expedited site characterization

This thesis develops geostatistical methods for use in the USDOE Expedited Site Characterization (ESC) process with focus on application to sites with contaminated soil and groundwater. Statistical methods for on-site sample location selection for geological and environmental sampling, characterizing uncertainty in the geologic and contaminant models, modeling the spatial distribution of contamination in the presence of non-detect data, determining when sufficient data have been collected, and post investigation analysis of geological and environmental data are given. These statistical methods are applied to data from an ESC demonstration at a former manufactured gas plant where soils were contaminated with polynuclear aromatic hydrocarbons and an ESC project at the Savannah River Site in South Carolina to characterize metal, pesticide, and volatile organic contamination in groundwater. A recommended approach to the use of geophysical methods and direct push technologies, in conjunction with geostatistical methods, for the characterization of the geologic environment and contaminant spatial distribution is developed. A summary of the USEPA Data Quality Objectives process, the USDOE Streamlined Approach for Environmental Restoration, and the USEPA Site Accelerated Cleanup Model is given, with focus on how ESC fits into the environmental restoration process