Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/96582
DC Field | Value | Language |
---|---|---|
dc.contributor | Department of Civil and Environmental Engineering | - |
dc.creator | Moazenzadeh, R | en_US |
dc.creator | Mohammadi, B | en_US |
dc.creator | Safari, MJS | en_US |
dc.creator | Chau, KW | en_US |
dc.date.accessioned | 2022-12-07T02:55:30Z | - |
dc.date.available | 2022-12-07T02:55:30Z | - |
dc.identifier.issn | 1994-2060 | en_US |
dc.identifier.uri | http://hdl.handle.net/10397/96582 | - |
dc.language.iso | en | en_US |
dc.publisher | Hong Kong Polytechnic University, Department of Civil and Structural Engineering | en_US |
dc.rights | © 2022 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. | en_US |
dc.rights | This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. | en_US |
dc.rights | The following publication Moazenzadeh, R., Mohammadi, B., Safari, M. J. S., & Chau, K. W. (2022). Soil moisture estimation using novel bio-inspired soft computing approaches. Engineering Applications of Computational Fluid Mechanics, 16(1), 826-840 is available at https://doi.org/10.1080/19942060.2022.2037467. | en_US |
dc.subject | ANFIS | en_US |
dc.subject | Bio-inspired optimization algorithms | en_US |
dc.subject | Data-driven models | en_US |
dc.subject | Meteorological variables | en_US |
dc.subject | Soil moisture | en_US |
dc.subject | Turkey | en_US |
dc.title | Soil moisture estimation using novel bio-inspired soft computing approaches | en_US |
dc.type | Journal/Magazine Article | en_US |
dc.identifier.spage | 826 | en_US |
dc.identifier.epage | 840 | en_US |
dc.identifier.volume | 16 | en_US |
dc.identifier.issue | 1 | en_US |
dc.identifier.doi | 10.1080/19942060.2022.2037467 | en_US |
dcterms.abstract | Soil moisture (SM) is of paramount importance in irrigation scheduling, infiltration, runoff, and agricultural drought monitoring. This work aimed at evaluating the performance of the classical ANFIS (Adaptive Neuro-Fuzzy Inference System) model as well as ANFIS coupled with three bio-inspired metaheuristic optimization methods including whale optimization algorithm (ANFIS-WOA), krill herd algorithm (ANFIS-KHA) and firefly algorithm (ANFIS-FA) in estimating SM. Daily air temperature, relative humidity, wind speed and sunshine hours data at Istanbul Bolge station in Turkey and soil temperature values measured over 2008–2009 were fed into the models under six different scenarios. ANFIS-WOA (RMSE = 1.68, MAPE = 0.04) and ANFIS (RMSE = 2.55, MAPE = 0.07) exhibited the best and worst performance in SM estimation, respectively. All three hybrid models (ANFIS-WOA, ANFIS-KHA and ANFIS-FA) improved SM estimates, reducing RMSE by 34, 28 and 27% relative to the base ANFIS model, respectively. A more detailed analysis of model performances in estimating moisture content over three intervals including [15–25), [25–35) and ≥35% revealed that ANFIS-WOA has had the lowest errors with RMSEs of 1.69, 1.89 and 1.55 in the three SM intervals, respectively. From the perspective of under- or over-estimation of moisture values, ANFIS-WOA (RMSE = 1.44, MAPE = 0.03) in under-estimation set and ANFIS-KHA (RMSE = 1.94, MAPE = 0.05) in over-estimation set showed the highest accuracies. Overall, all three hybrid models performed better in the underestimation set compared to overestimation set. | - |
dcterms.accessRights | open access | en_US |
dcterms.bibliographicCitation | Engineering applications of computational fluid mechanics, 2022, v. 16, no. 1, p. 826-840 | en_US |
dcterms.isPartOf | Engineering applications of computational fluid mechanics | en_US |
dcterms.issued | 2022 | - |
dc.identifier.scopus | 2-s2.0-85126854822 | - |
dc.identifier.eissn | 1997-003X | en_US |
dc.description.validate | 202212 bckw | - |
dc.description.oa | Version of Record | en_US |
dc.identifier.FolderNumber | OA_Scopus/WOS | - |
dc.description.pubStatus | Published | en_US |
Appears in Collections: | Journal/Magazine Article |
Files in This Item:
File | Description | Size | Format | |
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Moazenzadeh_Soil_Moisture_Estimation.pdf | 3.8 MB | Adobe PDF | View/Open |
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