Characterizing urban landscapes using fuzzy sets

Characterizing urban landscapes is important given the present and future projections of global population that favor urban growth. The definition of “urban” on a thematic map has proven to be problematic since urban areas are heterogeneous in terms of land use and land cover. Further, certain urban classes are inherently imprecise due to the difficulty in integrating various social and environmental inputs into a precise definition. Social components often include demographic patterns, transportation, building type and density while ecological components include soils, elevation, hydrology, climate, vegetation and tree cover. In this paper, we adopt a coupled human and natural system (CHANS) integrated scientific framework for characterizing urban landscapes. We implement the framework by adopting a fuzzy sets concept of “urban characterization” since fuzzy sets relate to classes of object with imprecise boundaries in which membership is a matter of degree. For dynamic mapping applications, user-defined classification schemes involving rules combining different social and ecological inputs can lead to a degree of quantification in class labeling varying from “highly urban” to “least urban”. A socio-economic perspective of urban may include threshold values for population and road network density while a more ecological perspective of urban may utilize the ratio of natural versus built area and percent forest cover. Threshold values are defined to derive the fuzzy rules of membership, in each case, and various combinations of rules offer a greater flexibility to characterize the many facets of the urban landscape. We illustrate the flexibility and utility of this fuzzy inference approach called the Fuzzy Urban Index for the Boston Metro region with five inputs and eighteen rules. The resulting classification map shows levels of fuzzy membership ranging from highly urban to least urban or rural in the Boston study region. We validate our approach using two experts assessing accuracy of the resulting fuzzy urban map. We discuss how our approach can be applied in other urban contexts with newly emerging descriptors of urban sustainability, urban ecology and urban metabolism.This research was partially supported by "Boston University Initiative on Cities Early Stage Urban Research Awards 2015-16" (Gopal & Phillips) and the Frederick S. Pardee Center for the Study of the Longer-Range Future at Boston University. We thank the anonymous reviewers for their careful reading of our manuscript and their many insightful comments and suggestions. (Boston University Initiative on Cities Early Stage Urban Research Awards; Frederick S. Pardee Center for the Study of the Longer-Range Future at Boston University)https://doi.org/10.1016/j.compenvurbsys.2016.02.002Published versio

Development of procedures for land use assessment at the regional scale

Multi-criteria land evaluation is an important process required for sustainable resource management. During the process of land evaluation, various factors related to land and corresponding resources need to be addressed. Availability of simple, ready to use procedures is particularly valuable for land evaluation. In this thesis approaches and tools aimed at the evaluation of land use change processes and land suitability for rural tourism, as well as sensitivity analysis procedure for land evaluation models are presented

A review of wildland fire spread modelling, 1990-present 3: Mathematical analogues and simulation models

In recent years, advances in computational power and spatial data analysis (GIS, remote sensing, etc) have led to an increase in attempts to model the spread and behvaiour of wildland fires across the landscape. This series of review papers endeavours to critically and comprehensively review all types of surface fire spread models developed since 1990. This paper reviews models of a simulation or mathematical analogue nature. Most simulation models are implementations of existing empirical or quasi-empirical models and their primary function is to convert these generally one dimensional models to two dimensions and then propagate a fire perimeter across a modelled landscape. Mathematical analogue models are those that are based on some mathematical conceit (rather than a physical representation of fire spread) that coincidentally simulates the spread of fire. Other papers in the series review models of an physical or quasi-physical nature and empirical or quasi-empirical nature. Many models are extensions or refinements of models developed before 1990. Where this is the case, these models are also discussed but much less comprehensively.Comment: 20 pages + 9 pages references + 1 page figures. Submitted to the International Journal of Wildland Fir

Automatic generation of textual descriptions in data-to-text systems using a fuzzy temporal ontology: Application in air quality index data series

In this paper we present a model based on computational intelligence and natural language generation for the automatic generation of textual summaries from numerical data series, aiming to provide insights which help users to understand the relevant information hidden in the data. Our model includes a fuzzy temporal ontology with temporal references which addresses the problem of managing imprecise temporal knowledge, which is relevant in data series. We fully describe a real use case of application in the environmental information systems field, providing linguistic descriptions about the air quality index (AQI), which is a very well-known indicator provided by all meteorological agencies worldwide. We consider two different data sources of real AQI data provided by the official Galician (NW Spain) Meteorology Agency: (i) AQI distribution in the stations of the meteorological observation network and (ii) time series which describe the state and evolution of the AQI in each meteorological station. Both application models were evaluated following the current standards and good practices of manual human expert evaluation of the Natural Language Generation field. Assessment results by two experts meteorologists were very satisfactory, which empirically confirm that the proposed textual descriptions fit this type of data and service both in content and layoutThis research was funded by the Spanish Ministry for Science, Innovation and Universities (grants TIN2017-84796-C2-1-R, PID2020-112623GB-I00, and PDC2021-121072-C21) and the Galician Ministry of Education, University and Professional Training, Spain (grants ED431C2018/29 and ED431G2019/04). All grants were co-funded by the European Regional Development Fund (ERDF/FEDER program)S

Design and conceptual development of a novel hybrid intelligent decision support system applied towards the prevention and early detection of forest fires

Forest fires have become a major problem that every year has devastating consequences at the environmental level, negatively impacting the social and economic spheres of the affected regions. Aiming to mitigate these terrible effects, intelligent prediction models focused on early fire detection are becoming common practice. Considering mainly a preventive approach, these models often use tools that indifferently apply statistical or symbolic inference techniques. However, exploring the potential for the hybrid use of both, as is already being done in other research areas, is a significant novelty with direct application to early fire detection. In this line, this work proposes the design, development, and proof of concept of a new intelligent hybrid system that aims to provide support to the decisions of the teams responsible for defining strategies for the prevention, detection, and extinction of forest fires. The system determines three risk levels: a general one called Objective Technical Fire Risk, based on machine learning algorithms, which determines the global danger of a fire in some area of the region under study, and two more specific others which indicate the risk over a limited area of the region. These last two risk levels, expressed in matrix form and called Technical Risk Matrix and Expert Risk Matrix, are calculated through a convolutional neural network and an expert system, respectively. After that, they are combined by means of another expert system to determine the Global Risk Matrix that quantifies the risk of fire in each of the study regions and generates a visual representation of these results through a color map of the region itself. The proof of concept of the system has been carried out on a set of historical data from fires that occurred in the Montesinho Natural Park (Portugal), demonstrating its potential utility as a tool for the prevention and early detection of forest fires. The intelligent hybrid system designed has demonstrated excellent predictive capabilities in such a complex environment as forest fires, which are conditioned by multiple factors. Future improvements associated with data integration and the formalization of knowledge bases will make it possible to obtain a standard tool that could be used and validated in real time in different forest areas

Third CLIPS Conference Proceedings, volume 1

Expert systems are computed programs which emulate human expertise in well defined problem domains. The potential payoff from expert systems is high: valuable expertise can be captured and preserved, repetitive and/or mundane tasks requiring human expertise can be automated, and uniformity can be applied in decision making processes. The C Language Integrated Production Systems (CLIPS) is an expert system building tool, developed at the Johnson Space Center, which provides a complete environment for the development and delivery of rule and/or object based expert systems. CLIPS was specifically designed to provide a low cost option for developing and deploying expert system applications across a wide range of hardware platforms. The development of CLIPS has helped to improve the ability to deliver expert systems technology throughout the public and private sectors for a wide range of applications and diverse computing environments