University of Oulu

Zahra Karimidastenaei, Ali Torabi Haghighi, Omid Rahmati, Kabir Rasouli, Sajad Rozbeh, Abdollah Pirnia, Biswajeet Pradhan, Bjørn Kløve, Fog-water harvesting Capability Index (FCI) mapping for a semi-humid catchment based on socio-environmental variables and using artificial intelligence algorithms, Science of The Total Environment, Volume 708, 2020, 135115, ISSN 0048-9697,

Fog-water harvesting Capability Index (FCI) mapping for a semi-humid catchment based on socio-environmental variables and using artificial intelligence algorithms

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Author: Karimidastenaei, Zahra1; Torabi Haghighi, Ali1; Rahamati, Omid2;
Organizations: 1Water, Energy and Environmental Engineering Research Unit, University of Oulu, P.O. Box 4300, FIN-90014 Oulu, Finland
2Soil Conservation and Watershed Management Research Department, Kurdistan Agricultural and Natural Resources Research and Education Center, AREEO, Sanandaj 6616936311, Iran
3Meteorological Service of Canada, Environment and Climate Change Canada, Canada
4Department of Watershed Management, Sari Agriculture Science and Natural Resources University, P.O. Box 737, Sari, Iran
5Centre for Advanced Modelling and Geospatial Information Systems (CAMGIS), Faculty of Engineering and Information Technology, University of Technology Sydney, 2007 New South Wales, Australia
6Department of Energy and Mineral Resources Engineering, Sejong University, Choongmu-gwan, 209 Neungdong-ro, Gwangjingu, Seoul 05006, Republic of Korea
Format: article
Version: accepted version
Access: open
Online Access: PDF Full Text (PDF, 0.4 MB)
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Language: English
Published: Elsevier, 2020
Publish Date: 2021-11-20


Fog is an important component of the water cycle in northern coastal regions of Iran. Having accurate tools for mapping the precise spatial distribution of fog is vital for water harvesting within integrated water resources management in this semi-humid region. In this study, environmental variables were considered in prediction mapping of areas with high concentrations of fog in the Vazroud watershed, Iran. Fog probability maps were derived from four artificial intelligence algorithms (Generalized Linear Model, Generalized Additive Model, Generalized Boosted Model, and Generalized Dissimilarity Model). Models accuracy were assessed using Receiver Operating characteristic Curve (ROC). Three social variables were also selected according to their relevance for fog suitability mapping. Finally, Fog-water harvesting Capability Index (FCI) maps were produced by multiplying fog probability by fog suitability maps. The results showed high accuracy in fog probability mapping for the study area, with all models proving capable of identifying areas with high fog concentrations in the south and southeast. For all models, the highest values of importance were obtained for sky view factor and the lowest for slope curvature. Analytic Hierarchy Process results showed the relative importance of social conditioning factors in fog suitability mapping, with the highest weight given to distance to residential area, followed by distance to livestock buildings and distance to road. Based on the fog suitability map, southeast and southern parts of the study area are most suitable for fog water harvesting. The fog spatial distribution maps obtained can increase fog water harvesting efficiency. They also indicate areas for future study with regions where fog is a critical component in the water cycle.

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Series: Science of the total environment
ISSN: 0048-9697
ISSN-E: 1879-1026
ISSN-L: 0048-9697
Volume: 708
Article number: 135115
DOI: 10.1016/j.scitotenv.2019.135115
Type of Publication: A1 Journal article – refereed
Field of Science: 1171 Geosciences
Copyright information: © 2019 Elsevier B.V. This manuscript version is made available under the CC-BY-NC-ND 4.0 license