Pece V. Gorsevski

Integrating multi-criteria evaluation techniques with geographic information systems for landfill site selection: a case study using ordered weighted average.

This paper presents a GIS-based multi-criteria decision analysis approach for evaluating the suitability for landfill site selection in the Polog Region, Macedonia. The multi-criteria decision framework considers environmental and economic factors which are standardized by fuzzy membership functions and combined by integration of analytical hierarchy process (AHP) and ordered weighted average (OWA) techniques. The AHP is used for the elicitation of attribute weights while the OWA operator function is used to generate a wide range of decision alternatives for addressing uncertainty associated with interaction between multiple criteria. The usefulness of the approach is illustrated by different OWA scenarios that report landfill suitability on a scale between 0 and 1. The OWA scenarios are intended to quantify the level of risk taking (i.e., optimistic, pessimistic, and neutral) and to facilitate a better understanding of patterns that emerge from decision alternatives involved in the decision making process.

An optimized solution of multi-criteria evaluation analysis of landslide susceptibility using fuzzy sets and Kalman filter

The Kalman recursive algorithm has been very widely used for integrating navigation sensor data to achieve optimal system performances. This paper explores the use of the Kalman filter to extend the aggregation of spatial multi-criteria evaluation (MCE) and to find optimal solutions with respect to a decision strategy space where a possible decision rule falls. The approach was tested in a case study in the Clearwater National Forest in central Idaho, using existing landslide datasets from roaded and roadless areas and terrain attributes. In this approach, fuzzy membership functions were used to standardize terrain attributes and develop criteria, while the aggregation of the criteria was achieved by the use of a Kalman filter. The approach presented here offers advantages over the classical MCE theory because the final solution includes both the aggregated solution and the areas of uncertainty expressed in terms of standard deviation. A comparison of this methodology with similar approaches suggested that this approach is promising for predicting landslide susceptibility and further application as a spatial decision support system.

Regional non-hazardous landfill site selection by integrating fuzzy logic, AHP and geographic information systems

This study presents a geographic information systems-based multi-criteria site selection of non-hazardous regional landfill in Polog Region, Macedonia. The multi-criteria decision framework integrates legal requirements and physical constraints that relate to environmental and economic concerns and builds a hierarchy model for landfill suitability. The methodology is used for preliminary assessment of the most suitable landfill sites by combining fuzzy set theory and analytic hierarchy process (AHP). The fuzzy set theory is used to standardize criteria using different fuzzy membership functions while the AHP is used to establish the relative importance of the criteria. The AHP makes pairwise comparisons of relative importance between hierarchy elements grouped by environmental and economic decision criteria. The landfill suitability is achieved by applying weighted linear combination that uses a comparison matrix to aggregate different importance scenarios associated with environmental and economic objectives. The results from the study suggested that a least suitable landfill area of 1.0% from the total is generated when environmental and economic objectives are valued equally while a most suitable landfill area of 1.8% area is generated when the economic objective is valued higher. Such results are aimed for enhancement of regional landfill site selection in the country that is compliant with modern EU standards.

Discerning landslide susceptibility using rough sets

Rough set theory has been primarily known as a mathematical approach for analysis of a vague description of objects. This paper explores the use of rough set theory to manage the complexity of geographic characteristics of landslide susceptibility and extract rules describing the relationships between landslide conditioning factors and landslide events. The proposed modeling approach is illustrated using a case study of the Clearwater National Forest in central Idaho, which experienced significant and widespread landslide events in recent years. In this approach the landslide susceptibility is derived from decision rules of variable strengths computed in rough set analysis and presented on maps for roaded and roadless areas. The rough set approach to modeling landslide susceptibility offers advantages over other modeling methods in accounting for data vagueness and uncertainty and in potentially reducing data collection needs. From an application perspective the rough set-based approach is promising as a decision support tool in forest planning involving the maintenance, obliteration or development of new forest roads in steep mountainous terrain.

Detecting grain boundaries in deformed rocks using a cellular automata approach

Cellular automata (CA) are widely used in geospatial dynamic modeling and image processing. Here, we explore the application of two-dimensional cellular automata to the problem of grain boundary detection and extraction in digital images of thin sections from deformed rocks. The automated extraction of boundaries, which contain rich sources of information such as shape, orientation, and spatial distribution of grains, involves a CA Moores neighborhood-based rules approach. The Moores neighborhood is a 3x3 matrix that is used for changing states by comparing differences between a central pixel and its neighbors. In this dynamic approach, the future state of a pixel depends upon its current state and that of its neighbors. The rules that are defined determine the future state of each cell (i.e., on or off) while the number of iterations to simulate boundaries detection are specified by the user. Each iteration outputs different detection scenarios of grain boundaries that can be evaluated and assessed for accuracy. For a deformed quartz arenite, an r^2 of 0.724 was obtained by comparing manually digitized grains to model derived grains. The value of this proposed method is compared against a traditional manual digitization approach and a recent GIS-based method developed for this purpose by Li et al. (2007).

A Fuzzy k-Means Classification and a Bayesian Approach for Spatial Prediction of Landslide Hazard

The increasing availability of geospatial data and the rapid advances in the Geographic Information Systems (GIS) technology for statistical and mathematical modeling and simulation have led to a variety of applications and a growing spatial literature. Spatial statistical methods and techniques have been widely used in a number of discipline-specific applications, some of which are described in this handbook and result from those rapid advances in GIS tools, techniques, and literature.

Android-Based Multi-Criteria Evaluation Approach for Enhancing Public Participation for a Wind Farm Site Selection

This project presents a hypothetical case study of an interactive mobile-based Public Participation Geographical Information Systems (PPGIS) prototype for selection of best alternative for new offshore wind farm development in Lake Erie, northern Ohio. The prototype implements a client-server architecture where Android operating system is used for the client side, and Google Cloud Platform services and GeoServer/PostgreSQL for the server side. The potential benefits from this prototype are demonstrated through an interactive Android interface where the importance of three decision alternatives is evaluated by multiple participants using different evaluation criteria. The individual evaluation scores are aggregated by using a mathematical Pairwise comparison voting method while the sum of all individual Pairwise comparison scores yields the group solution. The results from the group solution are interactively returned and used for building consensus and to aid understanding of potential solutions coalesced from multiple participants’ perspectives.

Estimating Leaf Area Index by Bayesian Linear Regression Using Terrestrial LiDAR, LAI-2200 Plant Canopy Analyzer, and Landsat TM Spectral Indices

Abstract Leaf area index (LAI) is a key biophysical variable and an ecosystem condition indicator that is measured from multiple methods. In this study, LAI was measured by a terrestrial laser scanner (TLS) and Li-Cor LAI-2200 plant canopy analyzer for understanding differences. A total of six different methods that consider leaf clumping, leaf–wood separation, and orthographic and stereographic projection were compared. A reasonable agreement among all methods for LAI estimates was observed (i.e., correlations r > 0.50). The Bayesian linear regression (BLR) approach was used to scale up the six different LAI estimates and to produce continuous field surfaces for the Oak Openings Region in northwest Ohio using Landsat TM–derived spectral vegetation indices (weighted difference vegetation index [WDVI], difference vegetation index [DVI], normalized difference vegetation index [NDVI], soil-adjusted vegetation index [SAVI], and perpendicular vegetation index 3 [PVI3]). The BLR approach provides details about the parameter uncertainties that may arise from foliage and wooden biomass and can be used for model comparisons. In this study, the TLS estimates of foliage derived by orthographic projection had the lowest residual scatter and overall model uncertainty. The deviation departure from the mean BLR estimates revealed that sparse and open areas were associated with the highest error and spatial uncertainties.

Using Bayesian inference to account for uncertainty in parameter estimates in modelled invasive flowering rush

Quantification of uncertainty that arises from a number of sources can enhance our understanding of biophysical processes of invasive species and the use of effective techniques, which results in more informed decision-making. To predict invasive flowering rush (FR) (Butomus umbellatus L.) within high spatial resolution imagery (<20 cm) from Applanix 439 Digital Sensor System (DSS) (Applanix Corporation, Richmond Hill, ON, Canada) requires a statistical approach with inherent modelling uncertainty. This letter provides a preliminary assessment of Bayesian logistic regression that is used to represent complex relationships between covariates derived from DSS imagery and estimates of inherent uncertainty in FR distributions. The Markov Chain Monte Carlo technique was used to generate the results of posterior parameter estimates, which were used to compare the sensitivity and robustness of different posterior estimators such as mean, median and other statistical descriptors. Predicted scenario of FR from the simulated posterior distributions was assessed by receiver operating characteristic (ROC) curves and their corresponding area under the ROC curves (AUC). The numerical value of the AUC suggested that the highest overall quantitative index of accuracy corresponded to 0.777 (AUC), while the lowest overall quantitative index of accuracy corresponded to 0.696 (AUC). The potential of this approach is illustrated in a case study in Ottawa National Wildlife Refuge in Northwest Ohio.

An ontology-based framework for extracting spatio-temporal influenza data using Twitter

ABSTRACT Early detection of influenza outbreaks is one of the key priorities on a national level for preparedness and planning. This study presents the design and implementation of Fluwitter, which is a spatio-temporal web-based prototype framework for pseudo real-time detection of influenza outbreaks from Twitter. Specifically, the framework integrates PostgreSQL database server with PostGIS spatial extension, Twitter streaming client, pre-processor, tagger and similarity calculator for semantic information extraction (IE). The IE of tagged terms is supported by Natural Language Processing (NLP) techniques, DBpediaSpotlight and WordNet Similarity for Java (WS4J), while data analytics, visualization, and mapping are supported by GeoServer and other GIS Free Open Source Software (FOSS). The prototype was calibrated to maximize detection of influenza using rules developed from ontology-based semantic similarity scores. The Twitter-generated influenza cases were validated by weekly hospitalization records issued by Ohio Department of Health (ODH). The optimized rule produced a final F-measure value of 0.72 and accuracy (ACC) value of 94.4%. The validation suggested the existence of moderate correlations for the beginning of the time period Southeast region (r = 0.52), the Northwestern region (r = 0.38), and the Central region (r = 0.33) and weak correlations for the entire time period. The potential strengths and benefits of the prototype are shown through spatio-temporal assessment and visualization of influenza potential in Ohio.