Weight of Evidence Method and Its Applications and Development

AbstractThe development and applications about the weight of evidence technology in recent years are reviewed. This paper introduced the improved weight of evidence in remote sensing image processing and in different fields of application. Summary its constraints and existent problems. Look forward to the weight of evidence for the practical application

Rapid methods of landslide hazard mapping : Fiji case study

A landslide hazard probability map can help planners (1) prepare for, and/or mitigate against, the effects of landsliding on communities and infrastructure, and (2) avoid or minimise the risks associated with new developments. The aims of the project were to establish, by means of studies in a few test areas, a generic method by which remote sensing and data analysis using a geographic information system (GIS) could provide a provisional landslide hazard zonation map. The provision of basic hazard information is an underpinning theme of the UN’s International Decade for Natural Disaster Reduction (IDNDR). It is an essential requirement for disaster preparedness and mitigation planning. This report forms part of BGS project 92/7 (R5554) ‘Rapid assessment of landslip hazards’ Carried out under the ODA/BGS Technology Development and Research Programme as part of the British Government’s provision of aid to developing countries. It provides a detailed technical account of work undertaken in a test area in Viti Levu in collaboration with Fiji Mineral Resources Department. The study represents a demonstration of a methodology that is applicable to many developing countries. The underlying principle is that relationships between past landsliding events, interpreted from remote sensing, and factors such as the geology, relief, soils etc provide the basis for modelling where future landslides are most likely to occur. This is achieved using a GIS by ‘weighting’ each class of each variable (e.g. each lithology ‘class’ of the variable ‘geology’) according to the proportion of landslides occurring within it compared to the regional average. Combinations of variables, produced by summing the weights in individual classes, provide ‘models’ of landslide probability. The approach is empirical but has the advantage of potentially being able to provide regional scale hazard maps over large areas quickly and cheaply; this is unlikely to be achieved using conventional ground-based geotechnical methods. In Fiji, landslides are usually triggered by intense rain storms commonly associated with tropical cyclones. However, the regional distribution of landslides has not been mapped nor is it known how far geology and landscape influence the location and severity of landsliding events. The report discusses the remote sensing and GIS methodology, and describes the results of the pilot study over an area of 713 km2 in south east Viti Levu. The landslide model uses geology, elevation, slope angle, slope aspect, soil type, and forest cover as inputs. The resulting provisional landslide hazard zonation map, divided into high, medium and low zones of landslide hazard probability, suggests that whilst rainfall is the immediate cause, others controls do exert a significant influence. It is recommended that consideration be given in Fiji to implementing the techniques as part of a national strategic plan for landslide hazard zonation mapping

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

The marine mineral resources of the UK Continental Shelf : final report

In 2011, The Crown Estate commissioned the British Geological Survey (BGS) to begin a two year research project to undertake a Mineral Resource Assessment of the UK Continental Shelf with the results being depicted as a series of maps, accompanying reports and associated GIS data. This report details the process behind the compilation of these maps. It outlines the data sources used in the project, the methodology used to compile the data, the confidence in the data and any caveats associated with the data and its use. This report focuses on the national model for sand and gravel, where relevant information on the data for other minerals is included for completeness. Knowledge of mineral resources is essential for effective and sustainable planning decisions. The marine mineral resource maps provide a comprehensive, relevant and accessible information base. This information will allow all stakeholders (planners, industry and members of the public) to visualise the distribution of offshore minerals to a common standard and at a common scale, an important requirement of an integrated marine planning system. The maps will also facilitate the conservation (safeguarding) of non-renewable mineral resources for future generations in accordance with the principles of sustainable development

CIRSS vertical data integration, San Bernardino study

The creation and use of a vertically integrated data base, including LANDSAT data, for local planning purposes in a portion of San Bernardino County, California are described. The project illustrates that a vertically integrated approach can benefit local users, can be used to identify and rectify discrepancies in various data sources, and that the LANDSAT component can be effectively used to identify change, perform initial capability/suitability modeling, update existing data, and refine existing data in a geographic information system. Local analyses were developed which produced data of value to planners in the San Bernardino County Planning Department and the San Bernardino National Forest staff

Landslide riskscapes in the Colorado Front Range: a quantitative geospatial approach for modeling human-environment interactions

2021 Spring.Includes bibliographical references.This research investigated the application of riskscapes to landslides in the context of geospatial inquiry. Riskscapes are framed as a landscape of risk to represent risk spatially. Geospatial models for landslide riskscapes were developed to improve our understanding of the spatial context for landslides and their risks as part of the system of human-environment interactions. Spatial analysis using Geographic Information Systems (GIS) leveraged modeling methods and the distributed properties of riskscapes to identify and preserve these spatial relationships. This dissertation is comprised of four separate manuscripts. These projects defined riskscapes in the context of landslides, applied geospatial analyses to create a novel riskscape model to introduce spatial autocorrelation methods to the riskscape framework, compared geostatistical analysis methods in these landslide riskscape assessments, and described limitations of spatial science identified in the riskscape development process. The first project addressed the current literature for riskscapes and introduced landslides as a measurable feature for riskscapes. Riskscapes are founded in social constructivist theory and landslide studies are frequently based on quantitative risk assessment practices. The uniqueness of a riskscape is the inclusion of human geography and environmental factors, which are not consistently incorporated in geologic or natural hazard studies. I proposed the addition of spatial theory constructs and methods to create spatially measurable products. I developed a conceptual framework for a landslide riskscape by describing the current riskscape applications as compared to existing landslide and GIS risk model processes. A spatial modeling formula to create a weighted sum landslide riskscape was presented as a modification to a natural hazard risk equation to incorporate the spatial dimension of risk factors. The second project created a novel method for three geospatial riskscapes as an approach to model landslide susceptibility areas in Boulder and Larimer Counties, Colorado. This study synthesized physical and human geography to create multiple landslide riskscape models using GIS methods. These analysis methods used a process model interface in GIS. Binary, ranked, and human factor weighted sum riskscapes were created, using frequency ratio as the basis for developing a weighting scheme. Further, spatial autocorrelation was introduced as a recommended practice to quantify the spatial relationships in landslide riskscape development. Results demonstrated that riskscapes, particularly those for ranked and human factor riskscapes, were highly autocorrelated, non-random, and exhibited clustering. These findings indicated that a riskscape model can support improvements to response modeling, based on the identification of spatially significant clustering of hazardous areas. The third project extended landslide riskscapes to measurable geostatistical comparisons using geostatistical tools within a GIS platform. Logistic regression, weights of evidence, and probabilistic neural networks methods were used to analyze the weighted sum landslide riskscape models using ArcGIS and Spatial Data Modeler (ArcSDM). Results showed weights of evidence models performed better than both logistic regression and neural networks methods. Receiver Operator Characteristic (ROC) curves and Area Under the Curve validation tests were performed and found the weights of evidence model performed best in both posterior probability prediction and AUC validation. A fourth project was developed based on the limitations discovered during the analytical process evaluations from the riskscape model development and geostatistical analysis. This project reviewed the issues with data quality, the variations in results predicated on the input parameters within the analytical toolsets, and the issues surrounding open-source application tools. These limitations stress the importance of parameter selection in a geospatial analytical environment. These projects collectively determined methods for riskscape development related to landslide features. The models presented demonstrate the importance and influence of spatial distributions on landslide riskscapes. Based on the proposed conceptual framework of a spatial riskscape for landslides, weighted sum riskscapes can provide a basis for prioritization of resources for landslides. Ranked and human factor riskscapes indicate the need to provide planning and protection for areas at increased risk for landslides. These studies provide a context for riskscapes to further our understanding of the benefits and limitations of a quantitative riskscape approach. The development of a methodological framework for quantitative riskscape models provides an approach that can be applied to other hazards or study areas to identify areas of increased human-environment interaction. Riskscape models can then be evaluated to inform mitigation and land-use planning activities to reduce impacts of natural hazards in the anthropogenic environment

Shallow landslide susceptibility : modelling and validation

Rainfall frequently triggers shallow landslides in mountainous areas worldwide. Landslide susceptibility maps express the probability of occurrence of landslides based on terrain conditions; they are useful for disaster prevention and land use planning. This report is about val- idating a qualitative approach to map global landslide susceptibility, based on the weighted linear combination (WLC) of slope gradient, soil type, soil texture, elevation, land cover and drainage density. The parameters are derived from digital global databases. The accuracy assessment was based on a detailed landslide inventory of a 160-km2 area in Japan, using the receiver-operating characteristic (ROC) plot area under the curve (AUC). The AUC permitted to compare analysis approaches and dierent parameter combinations. The AUC for the WLC model was 0.47, below a random classication. Two approaches improved the model accuracy, using the weights of evidence (WOE) approach raised the accuracy to 0.64, and using a higher resolution DEM raised the accuracy to 0.66. On the other hand, a quantitat- ive approach based on logistic regression (LR) and using the software package Spatial Data Modeller (SDM) produced models with AUC between 0.67 and 0.71. The highest accuracy for a model including lithology, slope gradient, prole curvature, plan curvature and elev- ation. The reason for the higher accuracy of the LR models is that the occurrence of landslides depends on local conditions, expressed by the quantitative relations, while the qualitative weights of the WLC model were developed for a global model using different criteria

Multi-level processes of integration and disintegration. Proceedings of the Third Green Week Scientific Conference

CONTENTS: ACKNOWLEDGEMENTS ... I; ABOUT THE MACE PROJECT... III; PLENARY PRESENTATION ... 1; Landscape agroecology: Managing interactions between agriculture, nature and socio-economy... 3, Tommy Dalgaard; DEVELOPMENT CHALLENGES IN RURAL AREAS ... 13; Patterns of rural development in mountainous areas of the Mediterranean: Between innovation and tradition ... 15, Angela Guarino; Agro ecology: Hypothesis for a sustainable local development?... 22, Silvia Doneddu; The farmers' early retirement scheme as an instrument of structural changes in the rural areas after Poland's accession to the EU ... 29, Michal Dudek; FOOD MARKETS AND AGRICULTURAL MARKETING... 37; G/Local brand challenges in the Austrian agricultural food market ... 39, Bernadette Frech, Ana Azevedo, Hildegard Liebl; Willingness of food industry companies to co-finance collective agricultural marketing actions... 48, Anikó Tóth, Csaba Forgács; MULTIFUNCTIONAL AGRICULTURE ... 57; The role of multifunctional agriculture for rural development in Bulgaria... 59, Violeta Dirimanova; A methodological review of multifunctional agriculture ... 66, Concettina Guarino, Francesco Di Iacovo; A spatially explicit decision-making support tool for integral rural development ... 75, Catherine Pfeifer, Jetse Stoorvogel; AGRICULTURAL EXTENSION AND NETWORKS IN RURAL AREAS... 89; Feasibility and implementation strategies of dairy extension in Ulaanbaatar/Mongolia... 91, Baast Erdenebolor, Volker Hoffmann; The relevance of social networks for the implementation of the LEADER programme in Romania ... 99, Doris Marquardt, Gertrud Buchenrieder, Judith Möllers; Quality assessment problems of agricultural advisory centres' services... 113, Gunta Grinberga; INTEGRATION PROCESSES INTO INTERNATIONAL MARKETS... 125; Competition or market power in the Ukrainian meat supply chain? ... 127, Andriy Matyukha, Oleksandr Perekhozhuk; Integration of the Hungarian cereal market into EU 15 markets ... 138, Attila Jambor; Regional specialisation of agriculture and competitive advantages of East-European countries... 146, Oleksandr Zhemoyda, Stephan J. Goetz; GOVERNANCE AND USE OF NATURAL RESOURCES ... 155; An analysis of biodiversity governance in the Kiskunság National Park according to the GoverNat Framework... 157, Cordula Mertens, Eszter Kelemen, György Pataki; Hierarchical network modelling and multicriteria analysis for agri-environmental measures in Poland ... 168, Jadwiga Ziolkowska; Assessing rural livelihood development strategies combining socioeconomic and spatial methodologies ... 179, K.C. Krishna Bahadur; SUSTAINABLE AGRICULTURAL LAND USE... 189; Linking economic and energy modelling with environmental assessment when modelling the on-farm implementation of Anaerobic Digestion ... 191, Andreas Muskolus, Andrew M. Salter, Philip J. Jones; Phytoremediation of a heavy metal-contaminated agricultural area combined with energy production. Multifunctional use of energy maize, rapeseed and short rotation crops in the Campine (BE)... 200, Nele Witters, Stijn Van Slycken, Erik Meers, Kristin Adriaensen, Linda Meiresonne, Filip Tack, Theo Thewys, Jaco Vangronsveld --

Geographical extrapolation domain analysis: Scaling up watershed management research projects, a toolkit to guide implementation

Funding agencies, research programs, and organizations involved in the implementation of research need to know the potential worldwide impact and applicability of their efforts and investments. The extrapolation domain analysis method (EDA) was developed to produce information about the location, areas, and population potentially influenced by research outputs. This working paper presents detailed steps how to implement an EDA. For a particular research project, it starts with establishing a baseline assessment of the project, and proceeds through data collection, preparation, and similarity modeling concluding with reporting and validation. The guide is designed for users with intermediate knowledge of GIS and Bayesian statistics for a smooth and easy implementation of the method. It also requires the participation of the members of the research project for proper identification of key variables to be used in the process

ELECTRONIC REGIONAL RISK ATLAS: DEVELOPMENT, STRUCTURE AND APPLICATION PRACTICE IN REPUBLIC OF ARMENIA

Initially ERRA was developed as a web-service presenting natural (landslides, flooding, earthquake, wildfire, strong winds) and man-made hazards and risks