Uncertainty analysis of environmental models within GIS environments

Hwang, Dongming; Karimi, Hassan A.; Byun, Daewon W.

doi:10.1016/s0098-3004(97)00133-7

Cited by 32 publications

(19 citation statements)

References 13 publications

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“…DEM error was assessed by comparison with a higher accuracy data source obtained from a GPS survey and used to filter normally distributed random error fields. (Openshaw et al, 1991, p. 78) Methods have been developed that transfer information from a GIS into external error propagation analysis tools (see for example Heuvelink (1998), Hwang et al (1998). Output from these external systems is either returned to the GIS for mapping and visualization or exported to graphic charts or statistical tables (Hwang et al, 1998).…”

Section: Higher Accuracy Data Availablementioning

confidence: 99%

Uncertainties associated with digital elevation models for hydrologic applications: a review

Wechsler

2007

Hydrol. Earth Syst. Sci.

296

181

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Abstract. Digital elevation models (DEMs) represent the topography that drives surface flow and are arguably one of the more important data sources for deriving variables used by numerous hydrologic models. A considerable amount of research has been conducted to address uncertainty associated with error in digital elevation models (DEMs) and the propagation of error to derived terrain parameters. This review brings together a discussion of research in fundamental topical areas related to DEM uncertainty that affect the use of DEMs for hydrologic applications. These areas include: (a) DEM error; (b) topographic parameters frequently derived from DEMs and the associated algorithms used to derive these parameters; (c) the influence of DEM scale as imposed by grid cell resolution; (d) DEM interpolation; and (e) terrain surface modification used to generate hydrologicallyviable DEM surfaces. Each of these topical areas contributes to DEM uncertainty and may potentially influence results of distributed parameter hydrologic models that rely on DEMs for the derivation of input parameters. The current state of research on methods developed to quantify DEM uncertainty is reviewed. Based on this review, implications of DEM uncertainty and suggestions for the GIS research and user communities are offered.

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Section: Higher Accuracy Data Availablementioning

confidence: 99%

Uncertainties associated with digital elevation models for hydrologic applications: a review

Wechsler

2007

Hydrol. Earth Syst. Sci.

296

181

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“…A sensitivity analysis should be carried out to confirm the stability and the reliability of the proposed system's results with respect to changes of its parameters [48][49][50]. In this study, the sensitivity of the Fuzzy-GA is assessed against any changes in GA's parameters: (a) the number of iterations; (b) the number of population; (c) the mutation rate; and (d) the crossover rate.…”

Section: Stage 4: Damage Map and Accuracy Assessmentmentioning

confidence: 99%

A Fuzzy-GA Based Decision Making System for Detecting Damaged Buildings from High-Spatial Resolution Optical Images

Janalipour

Mohammadzadeh

2017

Remote Sensing

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Abstract:In this research, a semi-automated building damage detection system is addressed under the umbrella of high-spatial resolution remotely sensed images. The aim of this study was to develop a semi-automated fuzzy decision making system using Genetic Algorithm (GA). Our proposed system contains four main stages. In the first stage, post-event optical images were pre-processed. In the second stage, textural features were extracted from the pre-processed post-event optical images using Haralick texture extraction method. Afterwards, in the third stage, a semi-automated Fuzzy-GA (Fuzzy Genetic Algorithm) decision making system was used to identify damaged buildings from the extracted texture features. In the fourth stage, a comprehensive sensitivity analysis was performed to achieve parameters of GA leading to more accurate results. Finally, the accuracy of results was assessed using check and test samples. The proposed system was tested over the 2010 Haiti earthquake (Area 1 and Area 2) and the 2003 Bam earthquake (Area 3). The proposed system resulted in overall accuracies of 76.88 ± 1.22%, 65.43 ± 0.29%, and 90.96 ± 0.15% over Area 1, Area 2, and Area 3, respectively. On the one hand, based on the concept of the proposed Fuzzy-GA decision making system, the automation level of this system is higher than other existing systems. On the other hand, based on the accuracy of our proposed system and four advanced machine learning techniques, i.e., bagging, boosting, random forests, and support vector machine, in the detection of damaged buildings, it seems that our proposed system is robust and efficient.

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“…Several authors have already introduced methods for the measurement of data uncertainity as well as that of model sensitivity analysis in studies using GIS (Hwang et al, 1998;Crosetto et al, 2000;Gahegan and Ehlers, 2000). Li et al (1998) created a monitoring and management GIS for Malaysia's shoreline together with an international and interdisciplinary team supported by the Asian Development Bank.…”

Section: Short Literature Reviewmentioning

confidence: 99%

A Review: Applications of Geographic Information Systems (Gis) in Marine Areas

Kaymaz¹,

Yabanlı²

2017

J Aquacult Eng Fish Res

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Abstract:In this review, the focus is the research of scientists which studied different GIS applications in marine areas. Based on literature review, the scientists stressed the use of GIS to assess and predict effects on marine environment. Marine spatial data are fundamental for a sustainable environmental management of marine areas, largely surrounded by countries with borders to the sea. In modern age, known as ''information age'', data acquisition, analysis and management are essential for rapid, precise and economical resolutions. GIS applications were recommended to supply a principle from combining past and present circumstances in order to predict future for marine areas. This review provides an example to how GIS applications can be used in the monitoring, conservation, management, sustainable development and environmental protection stages of marine areas.

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Uncertainty analysis of environmental models within GIS environments

Cited by 32 publications

References 13 publications

Uncertainties associated with digital elevation models for hydrologic applications: a review

Uncertainties associated with digital elevation models for hydrologic applications: a review

A Fuzzy-GA Based Decision Making System for Detecting Damaged Buildings from High-Spatial Resolution Optical Images

A Review: Applications of Geographic Information Systems (Gis) in Marine Areas

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