Architecture of a pan-European framework for Integrated Soil Water Erosion Assessment

Soil erosion implications on future food security are gaining global attention because in many areas worldwide there is an imbalance between soil loss and its subsequent deposition. Soil erosion is a complex phenomenon affected by many factors such as climate, topography and land cover (in particular forest resources, natural vegetation and agriculture) while directly influencing water sediment transport, the quality of water resources and water storage loss. A modeling architecture, based on the Revised Universal Soil Loss Equation, is proposed and applied to evaluate and validate at regional scale potential and actual soil water erosion, enabling it to be linked to other involved natural resources. The methodology benefits from the array programming paradigm with semantic constraints (lightweight array behavioural contracts provided by the Mastrave library) to concisely implement models as composition of interoperable modules and to process heterogeneous data.JRC.H.3-Forest Resources and Climat

Final Report of the DAUFIN project

DAUFIN = Data Assimulation within Unifying Framework for Improved river basiN modeling (EC 5th framework Project

Efficient Decision Support Systems

This series is directed to diverse managerial professionals who are leading the transformation of individual domains by using expert information and domain knowledge to drive decision support systems (DSSs). The series offers a broad range of subjects addressed in specific areas such as health care, business management, banking, agriculture, environmental improvement, natural resource and spatial management, aviation administration, and hybrid applications of information technology aimed to interdisciplinary issues. This book series is composed of three volumes: Volume 1 consists of general concepts and methodology of DSSs; Volume 2 consists of applications of DSSs in the biomedical domain; Volume 3 consists of hybrid applications of DSSs in multidisciplinary domains. The book is shaped decision support strategies in the new infrastructure that assists the readers in full use of the creative technology to manipulate input data and to transform information into useful decisions for decision makers

Machine Learning Approaches for Natural Resource Data

Abstract Real life applications involving efficient management of natural resources are dependent on accurate geographical information. This information is usually obtained by manual on-site data collection, via automatic remote sensing methods, or by the mixture of the two. Natural resource management, besides accurate data collection, also requires detailed analysis of this data, which in the era of data flood can be a cumbersome process. With the rising trend in both computational power and storage capacity, together with lowering hardware prices, data-driven decision analysis has an ever greater role. In this thesis, we examine the predictability of terrain trafficability conditions and forest attributes by using a machine learning approach with geographic information system data. Quantitative measures on the prediction performance of terrain conditions using natural resource data sets are given through five distinct research areas located around Finland. Furthermore, the estimation capability of key forest attributes is inspected with a multitude of modeling and feature selection techniques. The research results provide empirical evidence on whether the used natural resource data is sufficiently accurate enough for practical applications, or if further refinement on the data is needed. The results are important especially to forest industry since even slight improvements to the natural resource data sets utilized in practice can result in high saves in terms of operation time and costs. Model evaluation is also addressed in this thesis by proposing a novel method for estimating the prediction performance of spatial models. Classical model goodness of fit measures usually rely on the assumption of independently and identically distributed data samples, a characteristic which normally is not true in the case of spatial data sets. Spatio-temporal data sets contain an intrinsic property called spatial autocorrelation, which is partly responsible for breaking these assumptions. The proposed cross validation based evaluation method provides model performance estimation where optimistic bias due to spatial autocorrelation is decreased by partitioning the data sets in a suitable way. Keywords: Open natural resource data, machine learning, model evaluationTiivistelmä Käytännön sovellukset, joihin sisältyy luonnonvarojen hallintaa ovat riippuvaisia tarkasta paikkatietoaineistosta. Tämä paikkatietoaineisto kerätään usein manuaalisesti paikan päällä, automaattisilla kaukokartoitusmenetelmillä tai kahden edellisen yhdistelmällä. Luonnonvarojen hallinta vaatii tarkan aineiston keräämisen lisäksi myös sen yksityiskohtaisen analysoinnin, joka tietotulvan aikakautena voi olla vaativa prosessi. Nousevan laskentatehon, tallennustilan sekä alenevien laitteistohintojen myötä datapohjainen päätöksenteko on yhä suuremmassa roolissa. Tämä väitöskirja tutkii maaston kuljettavuuden ja metsäpiirteiden ennustettavuutta käyttäen koneoppimismenetelmiä paikkatietoaineistojen kanssa. Maaston kuljettavuuden ennustamista mitataan kvantitatiivisesti käyttäen kaukokartoitusaineistoa viideltä eri tutkimusalueelta ympäri Suomea. Tarkastelemme lisäksi tärkeimpien metsäpiirteiden ennustettavuutta monilla eri mallintamistekniikoilla ja piirteiden valinnalla. Väitöstyön tulokset tarjoavat empiiristä todistusaineistoa siitä, onko käytetty luonnonvaraaineisto riittävän laadukas käytettäväksi käytännön sovelluksissa vai ei. Tutkimustulokset ovat tärkeitä erityisesti metsäteollisuudelle, koska pienetkin parannukset luonnonvara-aineistoihin käytännön sovelluksissa voivat johtaa suuriin säästöihin niin operaatioiden ajankäyttöön kuin kuluihin. Tässä työssä otetaan kantaa myös mallin evaluointiin esittämällä uuden menetelmän spatiaalisten mallien ennustuskyvyn estimointiin. Klassiset mallinvalintakriteerit nojaavat yleensä riippumattomien ja identtisesti jakautuneiden datanäytteiden oletukseen, joka ei useimmiten pidä paikkaansa spatiaalisilla datajoukoilla. Spatio-temporaaliset datajoukot sisältävät luontaisen ominaisuuden, jota kutsutaan spatiaaliseksi autokorrelaatioksi. Tämä ominaisuus on osittain vastuussa näiden oletusten rikkomisesta. Esitetty ristiinvalidointiin perustuva evaluointimenetelmä tarjoaa mallin ennustuskyvyn mitan, missä spatiaalisen autokorrelaation vaikutusta vähennetään jakamalla datajoukot sopivalla tavalla. Avainsanat: Avoin luonnonvara-aineisto, koneoppiminen, mallin evaluoint

Avances en la regionalización estadística de escenarios de cambio climático para precipitación basados en técnicas de aprendizaje automático

A pesar de ser la principal herramienta para estudiar el cambio climático, los modelos globales de clima (GCM) siguen teniendo una resolución espacial limitada y presentan errores sistemáticos considerables con respecto al clima observado. La regionalización estadística pretende resolver este problema aprendiendo relaciones empíricas entre variables de larga escala, bien reproducidas por los GCM (por ejemplo, los vientos sinópticos o el geopotencial), y observaciones locales de la variable en superficie de interés, como la precipitación, objeto de esta tesis. Proponemos una serie de desarrollos novedosos que permiten mejorar la consistencia de los campos regionalizados y producir escenarios regionales plausibles de cambio climático. Los resultados de esta tesis tienen importantes implicaciones para los diferentes sectores que necesitan información fiable de precipitación para llevar a cabo sus evaluaciones de impactos.Even though they are the main tool to study climate change, global climate models (GCMs) still have a limited spatial resolution and exhibit considerable systematic errors with respect to the observed climate. Statistical downscaling aims to solve this issue by learning empirical relationships between large-scale variables, well reproduced by GCMs (such as synoptic winds or geopotential), and local observations of the target surface variable, such as precipitation, the focus of this thesis. We propose a series of novel developments which allow for improving the consistency of the downscaled fields and producing plausible local-to-regional climate change scenarios. The results of this thesis have important implications for the different sectors in need of reliable precipitation information to undertake their impact assessments

24th International Conference on Information Modelling and Knowledge Bases

In the last three decades information modelling and knowledge bases have become essentially important subjects not only in academic communities related to information systems and computer science but also in the business area where information technology is applied. The series of European – Japanese Conference on Information Modelling and Knowledge Bases (EJC) originally started as a co-operation initiative between Japan and Finland in 1982. The practical operations were then organised by professor Ohsuga in Japan and professors Hannu Kangassalo and Hannu Jaakkola in Finland (Nordic countries). Geographical scope has expanded to cover Europe and also other countries. Workshop characteristic - discussion, enough time for presentations and limited number of participants (50) / papers (30) - is typical for the conference. Suggested topics include, but are not limited to: 1. Conceptual modelling: Modelling and specification languages; Domain-specific conceptual modelling; Concepts, concept theories and ontologies; Conceptual modelling of large and heterogeneous systems; Conceptual modelling of spatial, temporal and biological data; Methods for developing, validating and communicating conceptual models. 2. Knowledge and information modelling and discovery: Knowledge discovery, knowledge representation and knowledge management; Advanced data mining and analysis methods; Conceptions of knowledge and information; Modelling information requirements; Intelligent information systems; Information recognition and information modelling. 3. Linguistic modelling: Models of HCI; Information delivery to users; Intelligent informal querying; Linguistic foundation of information and knowledge; Fuzzy linguistic models; Philosophical and linguistic foundations of conceptual models. 4. Cross-cultural communication and social computing: Cross-cultural support systems; Integration, evolution and migration of systems; Collaborative societies; Multicultural web-based software systems; Intercultural collaboration and support systems; Social computing, behavioral modeling and prediction. 5. Environmental modelling and engineering: Environmental information systems (architecture); Spatial, temporal and observational information systems; Large-scale environmental systems; Collaborative knowledge base systems; Agent concepts and conceptualisation; Hazard prediction, prevention and steering systems. 6. Multimedia data modelling and systems: Modelling multimedia information and knowledge; Contentbased multimedia data management; Content-based multimedia retrieval; Privacy and context enhancing technologies; Semantics and pragmatics of multimedia data; Metadata for multimedia information systems. Overall we received 56 submissions. After careful evaluation, 16 papers have been selected as long paper, 17 papers as short papers, 5 papers as position papers, and 3 papers for presentation of perspective challenges. We thank all colleagues for their support of this issue of the EJC conference, especially the program committee, the organising committee, and the programme coordination team. The long and the short papers presented in the conference are revised after the conference and published in the Series of “Frontiers in Artificial Intelligence” by IOS Press (Amsterdam). The books “Information Modelling and Knowledge Bases” are edited by the Editing Committee of the conference. We believe that the conference will be productive and fruitful in the advance of research and application of information modelling and knowledge bases. Bernhard Thalheim Hannu Jaakkola Yasushi Kiyok

Assessing Uncertainty Associated with Groundwater and Watershed Problems Using Fuzzy Mathematics and Generalized Regression Neural Networks

When trying to represent an environmental process using mathematical models, uncertainty is an integral part of numerical representation. Physically-based parameters are required by such models in order to forecast or make predictions. Typically, when the uncertainty inherent in models is addressed, only aleatory uncertainty (irreducible uncertainty) is considered. This type of uncertainty is amenable to analysis using probability theory. However, uncertainty due to lack of knowledge about the system, or epistemic uncertainty, should also be considered. Fuzzy set theory and fuzzy measure theory are tools that can be used to better assess epistemic, as well as aleatory, uncertainty in the mathematical representation of the environment. In this work, four applications of fuzzy mathematics and generalized regression neural networks (GRNN) are presented. In the first, Dempster-Shafer theory (DST) is used to account for uncertainty that surrounds permeability measurements and is typically lost in data analysis. The theory is used to combine multiple sources of subjective information from two expert hydrologists and is applied to three different data collection techniques: drill-stem, core, and pump-test analysis. In the second, a modification is made to the fuzzy least-squares regression model and is used to account for uncertainty involved in using the Cooper-Jacob method to determine transmissivity and the storage coefficient. A third application, involves the development of a GRNN to allow for the use of fuzzy numbers. A small example using stream geomorphic condition assessments conducted in the state of Vermont is provided. Ultimately, this fuzzy GRNN will be used to better understand the relationship between the geomorphic and habitat conditions of stream reaches and their corresponding biological health. Finally, an application of the GRNN algorithm to explore links between physical stream geomorphic and habitat conditions and biological health of stream reaches is provided. The GRNN proves useful; however, physical and biological data collected concurrently is needed to enhance accuracy

Solstice: An Electronic Journal of Geography and Mathematics, Volume XIII, Number 2

This document was delivered over the internet. The .zip file contains all static images, animated images, and text files.The purpose of Solstice is to promote interaction between geography and mathematics. Articles in which elements of one discipline are used to shed light on the other are particularly sought. Also welcome, are original contributions that are purely geographical or purely mathematical. These may be prefaced (by editor or author) with commentary suggesting directions that might lead toward the desired interaction. Contributed articles will be refereed by geographers and/or mathematicians. Invited articles will be screened by suitable members of the editorial board. IMaGe is open to having authors suggest, and furnish material for, new regular features.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/58224/2/win02.ziphttp://deepblue.lib.umich.edu/bitstream/2027.42/58224/66/2002SolsticeVol13.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/58224/68/SolsticeVolXIIINo2.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/58224/70/SolsticeVolXIIINo2.pdfDescription of SolsticeVolXIIINo2.pdf : Cover of JournalDescription of 2002SolsticeVol13.pdf : Solstice, 2002, Numbers 1 and 2. Contains attachments.Description of SolsticeVolXIIINo2.pdf : Cover fil