Improvement of LCC Prediction Modeling Based on Correlated Parameters and Model Structure Uncertainty Propagation
Land cover change (LCC) mapping is one of the basic tasks for environmental monitoring and management. The most significant factors in determining the performance of model of LCC prediction are its structure and parameter optimization. However, these fact
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Abstract. Land cover change (LCC) mapping is one of the basic tasks for environmental monitoring and management. The most significant factors in determining the performance of model of LCC prediction are its structure and parameter optimization. However, these factors are generally marred by uncertainties which affect the reliability of decision about changes. The reduction of these uncertainties is deemed as essential elements for LCC prediction modeling. Propagation of uncertainty appears as good alternative for decreasing the uncertainty related to LCC prediction process and therefore obtain more relevant decision. On the other hand, correlation analysis between model parameters is often neglected. This affects the reliability of the model and makes it difficult to better determine the uncertainty related to model parameters. Several studies in literature depicts that evidence theory can be applied to propagate uncertainty associated to LCC prediction models and to solve multidimensional problems. This paper presents an effective optimization scheme for the LCC prediction modeling based on the uncertainty propagation of model parameters and model structure. Uncertainty propagation is analyzed by using evidence theory without and with considering correlations. In this study, change prediction of land cover in Saint-Denis City, Reunion Island of next 5 years (2016) was anticipated using multitemporal Spot-4 satellite images acquired at the dates 2006 and 2011. Results show good performances of the proposed approach in improving prediction of the LCC. Results also demonstrate that the proposed approach is an effective and efficient method due to its adequate degree of accuracy. Keywords: LCC prediction · Parameter uncertainty · Correlation analysis · Model structure uncertainty · Uncertainty propagation · Evidence theory
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Introduction
LCC is a topic that has recently received considerable attention in the prospective modeling domain. Predicting changes in forthcoming years may play a c Springer International Publishing AG 2017 J.J. Merelo et al. (eds.), Computational Intelligence, Studies in Computational Intelligence 669, DOI 10.1007/978-3-319-48506-5 14
Improvement of LCC Prediction Modeling
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significant role in planning and optimal use of resources and harnessing the non-normative changes in the future [7,8]. In literature, several models are proposed to predict LCC such as cellular automata models [1], markov chain model [5], logistic regression models [2], agent-based models [3], data mining models [6], and artificial neural networks [4]. In general, input parameters of each of these models and the prediction model structure are marred by uncertainties which affect the reliability of decision about these changes [7,9,10]. Uncertainty in model parameters is due to natural variability, measurement inaccuracy, and errors in handling and processing data [11,12]. Model structure encloses uncertainty which is due to model assumptions/approximations, hypotheses, and scale effects. Propagation of uncertainty helps impro
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