A new integrated data mining model to map spatial variation in the susceptibility of land to act as a source of aeolian dust

A - Papers appearing in refereed journals

Gholami, H., Mohammadifar, A., Pourghasemi, H. R. and Collins, A. L. 2020. A new integrated data mining model to map spatial variation in the susceptibility of land to act as a source of aeolian dust. Environmental Science and Pollution Research. https://doi.org/10.1007/s11356-020-10168-6

AuthorsGholami, H., Mohammadifar, A., Pourghasemi, H. R. and Collins, A. L.
Abstract

This research developed a more efficient integrated model (IM) based on combining the Nash-Sutcliffe efficiency coefficient (NSEC) and individual data mining (DM) algorithms for the spatial mapping of dust provenance in the Hamoun-e-Hirmand Basin, southeastern Iran. This region experiences severe wind erosion and includes the Sistan plain which is one of the most PM2.5-polluted regions in the world. Due to a prolonged drought over the last two decades, the frequency of dust storms in the study area is increasing remarkably. Herein, 14 factors controlling dust emissions (FCDEs) including soil characteristics, climatic variables, digital elevation map, normalized difference vegetation index, land use and geology were mapped. Correlation and collinearity among the FCDEs were examined by the Pearson test, tolerance coefficient (TC) and variance inflation factor (VIF), with the results suggesting a lack of collinearity between FCDEs. A tree-based genetic algorithm was applied to prioritize and quantify the importance weights of the FCDEs. Thirteen individual data mining models were applied for mapping dust provenance. The model performance was assessed using root mean square error, mean absolute error and NSEC. Based on clustering analysis, the 13 DM models were grouped into five clusters and then the cluster with the highest NSEC values used in an integrated modelling process. Based on the results, the IM (NSEC = 93%) outperformed the individual DM models (the NSEC values range between 51 and 92%). Using the IM, 11, 5, 7 and 77% of the total study area were classified into low, moderate, high and very high susceptibility classes for dust provenance, respectively. Overall, the results illustrate the benefits of an IM for mapping spatial variation in the susceptibility of catchment areas to act as dust sources.

KeywordsDust provenance susceptibility; Integrated modelling; Cluster analysis; Data mining algorithms
Year of Publication2020
JournalEnvironmental Science and Pollution Research
Digital Object Identifier (DOI)https://doi.org/10.1007/s11356-020-10168-6
Open accessPublished as non-open access
FunderBiotechnology and Biological Sciences Research Council
Funder project or codeS2N - Soil to Nutrition - Work package 3 (WP3) - Sustainable intensification - optimisation at multiple scales
Output statusPublished
Publication dates
Online23 Jul 2020
Publication process dates
Accepted16 Jul 2020
PublisherSpringer Heidelberg
ISSN0944-1344

Permalink - https://repository.rothamsted.ac.uk/item/981q3/a-new-integrated-data-mining-model-to-map-spatial-variation-in-the-susceptibility-of-land-to-act-as-a-source-of-aeolian-dust

Restricted files

Publisher's version

Under embargo indefinitely

8 total views
1 total downloads
0 views this month
0 downloads this month