Propagating aleatory and epistemic uncertainty in land cover change prediction process

Abstract

An objective of satellite remote sensing is to predict or characterize the land cover change (LCC) over time. Sometimes we are capable of describing the changes of land cover with a probability distribution. However, we need sufficient knowledge about the natural variability of these changes, which is not always possible. In general, uncertainties can be subdivided into aleatory and epistemic. The main problem is that classical probability theory does not make a clear distinction between aleatory and epistemic uncertainties in the way they are represented, i.e., both of them are described with a probability distribution. The aim of this paper is to propagate the aleatory and epistemic uncertainty associated with both input parameters (features extracted from satellite image object) and model structure of LCC prediction process using belief function theory. This will help reducing in a significant way the uncertainty about future changes of land cover. In this study, the changes prediction of land cover in Cairo region, Egypt for next 16 years (2030) is anticipated using multi-temporal Landsat TM5 satellite images in 1987 and 2014. The LCC prediction model results indicated that 15% of the agriculture and 6.5% of the desert will be urbanized in 2030. We conclude that our method based on belief function theory has a potential to reduce uncertainty and improve the prediction accuracy and is applicable in LCC analysis.

Standalone noise and anomaly detection in wireless sensor networks: A novel time‐series and adaptive Bayesian‐network‐based approach

“…Another challenging topic to be explored is the effect of the percentage of the total noisy data. We can think to integrate uncertainty modeling to LODA to improve the outlier detection in WSNs . Moreover, the focus of this study is mainly in the context of sensor networks; hence, an extension to the current work would be the consideration of outlier detection over data streams.…”

“…We can think to integrate uncertainty modeling to LODA to improve the outlier detection in WSNs. 34,35 Moreover, the focus of this study is mainly in the context of sensor networks; hence, an extension to the current work would be the consideration of outlier detection over data streams. Finally, the state-of-the-art in machine learning, such as deep learning approaches, is achieving excellent results in many domains and can be also exploited in our field.…”

Mining frequent approximate patterns in large networks

“…Comparison of the number of frequent mined patterns between mining frequent approximate pattern (MFAP) and Gaston will integrate uncertainty modeling in the proposed method. [47][48][49]…”

A novel decision support system for the interpretation of remote sensing big data

“…This imperfection is accentuated with the huge amount of big data. Therefore, building reliable decision support systems in RS fields requires modeling imperfection in different stages of the decision support process [43] [48] [49].…”

“…In previous works, we proposed a set of attributes to describe objects extracted from satellite images [41] [42] [43]. The following attributes will be considered in this study: The considered features in this study can be substituted according to a specific field of application.…”