A novel framework for spatio-temporal prediction of environmental data using deep learning

As the role played by statistical and computational sciences in climate and environmental modelling and prediction becomes more important, Machine Learning researchers are becoming more aware of the relevance of their work to help tackle the climate crisis. Indeed, being universal nonlinear function approximation tools, Machine Learning algorithms are efficient in analysing and modelling spatially and temporally variable environmental data. While Deep Learning models have proved to be able to capture spatial, temporal, and spatio-temporal dependencies through their automatic feature representation learning, the problem of the interpolation of continuous spatio-temporal fields measured on a set of irregular points in space is still under-investigated. To fill this gap, we introduce here a framework for spatio-temporal prediction of climate and environmental data using deep learning. Specifically, we show how spatio-temporal processes can be decomposed in terms of a sum of products of temporally referenced basis functions, and of stochastic spatial coefficients which can be spatially modelled and mapped on a regular grid, allowing the reconstruction of the complete spatio-temporal signal. Applications on two case studies based on simulated and real-world data will show the effectiveness of the proposed framework in modelling coherent spatio-temporal fields.Comment: 11 pages, 8 figure

Spatio-temporal Modelling of Remote-sensing Lake Surface Water Temperature Data

Remote-sensing technology is widely used in environmental monitoring. The coverage and resolution of satellite based data provide scientists with great opportunities to study and understand environmental change. However, the large volume and the missing observations in the remote-sensing data present challenges to statistical analysis. This paper investigates two approaches to the spatio-temporal modelling of remote-sensing lake surface water temperature data. Both methods use the state space framework, but with different parameterizations to reflect different aspects of the problem. The appropriateness of the methods for identifying spatial/temporal patterns in the data is discussed

P-spline anova-type interaction models for spatio-temporal smoothing

In recent years, spatial and spatio-temporal modelling have become an important area of research in many fields (epidemiology, environmental studies, disease mapping, ...). However, most of the models developed are constrained by the large amounts of data available. We propose the use of Penalized splines (P-splines) in a mixed model framework for smoothing spatio-temporal data. Our approach allows the consideration of interaction terms which can be decomposed as a sum of smooth functions similarly as an ANOVA decomposition. The properties of the bases used for regression allow the use of algorithms that can handle large amount of data. We show that imposing the same constraints as in a factorial design it is possible to avoid identifiability problems. We illustrate the methodology for Europe ozone levels in the period 1999-2005

Spatial-temporal data modelling and processing for personalised decision support

The purpose of this research is to undertake the modelling of dynamic data without losing any of the temporal relationships, and to be able to predict likelihood of outcome as far in advance of actual occurrence as possible. To this end a novel computational architecture for personalised ( individualised) modelling of spatio-temporal data based on spiking neural network methods (PMeSNNr), with a three dimensional visualisation of relationships between variables is proposed. In brief, the architecture is able to transfer spatio-temporal data patterns from a multidimensional input stream into internal patterns in the spiking neural network reservoir. These patterns are then analysed to produce a personalised model for either classification or prediction dependent on the specific needs of the situation. The architecture described above was constructed using MatLab© in several individual modules linked together to form NeuCube (M1). This methodology has been applied to two real world case studies. Firstly, it has been applied to data for the prediction of stroke occurrences on an individual basis. Secondly, it has been applied to ecological data on aphid pest abundance prediction. Two main objectives for this research when judging outcomes of the modelling are accurate prediction and to have this at the earliest possible time point. The implications of these findings are not insignificant in terms of health care management and environmental control. As the case studies utilised here represent vastly different application fields, it reveals more of the potential and usefulness of NeuCube (M1) for modelling data in an integrated manner. This in turn can identify previously unknown (or less understood) interactions thus both increasing the level of reliance that can be placed on the model created, and enhancing our human understanding of the complexities of the world around us without the need for over simplification. Read less Keywords Personalised modelling; Spiking neural network; Spatial-temporal data modelling; Computational intelligence; Predictive modelling; Stroke risk predictio

Spatial-temporal data modelling and processing for personalised decision support

The purpose of this research is to undertake the modelling of dynamic data without losing any of the temporal relationships, and to be able to predict likelihood of outcome as far in advance of actual occurrence as possible. To this end a novel computational architecture for personalised ( individualised) modelling of spatio-temporal data based on spiking neural network methods (PMeSNNr), with a three dimensional visualisation of relationships between variables is proposed. In brief, the architecture is able to transfer spatio-temporal data patterns from a multidimensional input stream into internal patterns in the spiking neural network reservoir. These patterns are then analysed to produce a personalised model for either classification or prediction dependent on the specific needs of the situation. The architecture described above was constructed using MatLab© in several individual modules linked together to form NeuCube (M1). This methodology has been applied to two real world case studies. Firstly, it has been applied to data for the prediction of stroke occurrences on an individual basis. Secondly, it has been applied to ecological data on aphid pest abundance prediction. Two main objectives for this research when judging outcomes of the modelling are accurate prediction and to have this at the earliest possible time point. The implications of these findings are not insignificant in terms of health care management and environmental control. As the case studies utilised here represent vastly different application fields, it reveals more of the potential and usefulness of NeuCube (M1) for modelling data in an integrated manner. This in turn can identify previously unknown (or less understood) interactions thus both increasing the level of reliance that can be placed on the model created, and enhancing our human understanding of the complexities of the world around us without the need for over simplification. Read less Keywords Personalised modelling; Spiking neural network; Spatial-temporal data modelling; Computational intelligence; Predictive modelling; Stroke risk predictio

Environmental modelling in urban areas with Geographical Information System (GIS)

More accurate spatio-temporal predictions of urban environment are needed as a basis for assessing exposures as a part of environmental studies, and to inform urban protection policy and management. This paper is focused on modelling in the GIS to estimate air, water and soil pollution in urban areas. The basic environmental components are complemented by bio-monitoring, waste management and noise exposure. The models, which use data from long-time monitoring, are developed using correlation, regression and factor analysis; simulation of dynamic relation and spatio-temporal phenomena. Integration of a wide range of relatively independent factors enables more complex analysis of environment in urban areas. GIS, which can integrate a wide range of spatial and temporal data, is used for data management, input and output of data, visualization and development of programming modules that extend GIS with other statistical analysis and dynamic modelling. The analysis and models were built in ArcGIS with ArcObjects. In spite of the fact that the models are calibrated and tested by application in the urban areas of Prague, the structure of the GIS project is applicable on other similar areas. The fundamental part of the environmental models is focused on modelling of surface-water quality, soil pollution and their relation to human activities and air pollution. The models use data measured during decades, which are collected from manually and automatic pollution monitoring networks. The map layers are divided into a few classes that represent basic maps of urban areas in the scale 1:500, thematic maps, aerial photographs, monitoring networks, and outputs of environmental models. The spatio-temporal analysis and dynamic environmental models are accessible through the user interface of the GIS project

Geospatial Narratives and their Spatio-Temporal Dynamics: Commonsense Reasoning for High-level Analyses in Geographic Information Systems

The modelling, analysis, and visualisation of dynamic geospatial phenomena has been identified as a key developmental challenge for next-generation Geographic Information Systems (GIS). In this context, the envisaged paradigmatic extensions to contemporary foundational GIS technology raises fundamental questions concerning the ontological, formal representational, and (analytical) computational methods that would underlie their spatial information theoretic underpinnings. We present the conceptual overview and architecture for the development of high-level semantic and qualitative analytical capabilities for dynamic geospatial domains. Building on formal methods in the areas of commonsense reasoning, qualitative reasoning, spatial and temporal representation and reasoning, reasoning about actions and change, and computational models of narrative, we identify concrete theoretical and practical challenges that accrue in the context of formal reasoning about `space, events, actions, and change'. With this as a basis, and within the backdrop of an illustrated scenario involving the spatio-temporal dynamics of urban narratives, we address specific problems and solutions techniques chiefly involving `qualitative abstraction', `data integration and spatial consistency', and `practical geospatial abduction'. From a broad topical viewpoint, we propose that next-generation dynamic GIS technology demands a transdisciplinary scientific perspective that brings together Geography, Artificial Intelligence, and Cognitive Science. Keywords: artificial intelligence; cognitive systems; human-computer interaction; geographic information systems; spatio-temporal dynamics; computational models of narrative; geospatial analysis; geospatial modelling; ontology; qualitative spatial modelling and reasoning; spatial assistance systemsComment: ISPRS International Journal of Geo-Information (ISSN 2220-9964); Special Issue on: Geospatial Monitoring and Modelling of Environmental Change}. IJGI. Editor: Duccio Rocchini. (pre-print of article in press

Spatio-temporal statistical methods in environmental and biometrical problems

This is the editorial letter for the Special Issue dedicated to the VIII International Workshop on Spatio-temporal Modelling (METMAVIII) which took place in Valencia (Spain) from 1 to 3 June 2016, and to the second Galician-Portuguese meeting of Biometry, with applications to Health Sciences, Ecology and Environmental Sciences (BIOAPP2016) held in Santiago de Compostela (Spain), 30–2 July 2016. This special issue summarises and discusses selected peer-reviewed contributions related to spatial and spatio-temporal statistical methodologies comprising both new methodological approaches and a wide range of applications related to environmental and biometrical problems. Point processes, lattice data and geostatistical methods are covered. These methods are illustrated with statistical analyses of animal or plant species in ecological studies, seismic data, temperatures and monthly precipitation, daily ozone concentration values, air pollution data, breast cancer incidence rates, mussels, wildfires, pore structures in pharmaceutical coatings, hake recruitment and cancer mortality data.(undefined)info:eu-repo/semantics/publishedVersio

Economic Development And Transfrontier Shipments Of Waste In Poland – Spatio-Temporal Analysis

The aim of the paper is to apply the spatio-temporal Environmental Kuznets Curve (SpEKC) to test the relationship between economic growth and the amount of collected mixed municipal waste. The analysis was conducted at the level of sixty-six Polish sub-regions. The study contained selected environmental indicators. The dependent variable - the amount of municipal waste generated in kilograms per capita characterized the state of the environment. The GDP per capita in constant prices (as an explanatory variable) presented the level of economic development of the sub-regions. In the empirical part of the research there were used spatial panel data models based on EKCs. It determined the levels of economic development, at which the amount of produced wastes has fallen or increased, depending on the wealth of the region. The application of different types of spatial weight matrices was an important element of this modelling. Data obtained the years 2005-2012. Models were estimated in the RCran package