PMID- 31936038
OWN - NLM
STAT- PubMed-not-MEDLINE
DCOM- 20200116
LR  - 20240328
IS  - 1424-8220 (Electronic)
IS  - 1424-8220 (Linking)
VI  - 20
IP  - 2
DP  - 2020 Jan 7
TI  - Evaluation of Recent Advanced Soft Computing Techniques for Gully Erosion 
      Susceptibility Mapping: A Comparative Study.
LID - 10.3390/s20020335 [doi]
LID - 335
AB  - Gully erosion is a problem; therefore, it must be predicted using highly accurate 
      predictive models to avoid losses caused by gully development and to guarantee 
      sustainable development. This research investigates the predictive performance of 
      seven multiple-criteria decision-making (MCDM), statistical, and machine learning 
      (ML)-based models and their ensembles for gully erosion susceptibility mapping 
      (GESM). A case study of the Dasjard River watershed, Iran uses a database of 306 
      gully head cuts and 15 conditioning factors. The database was divided 70:30 to 
      train and verify the models. Their performance was assessed with the area under 
      prediction rate curve (AUPRC), the area under success rate curve (AUSRC), 
      accuracy, and kappa. Results show that slope is key to gully formation. The 
      maximum entropy (ME) ML model has the best performance (AUSRC = 0.947, AUPRC = 
      0.948, accuracy = 0.849 and kappa = 0.699). The second best is the random forest 
      (RF) model (AUSRC = 0.965, AUPRC = 0.932, accuracy = 0.812 and kappa = 0.624). By 
      contrast, the TOPSIS (Technique for Order Preference by Similarity to Ideal 
      Solution) model was the least effective (AUSRC = 0.871, AUPRC = 0.867, accuracy = 
      0.758 and kappa = 0.516). RF increased the performance of statistical index (SI) 
      and frequency ratio (FR) statistical models. Furthermore, the combination of a 
      generalized linear model (GLM), and functional data analysis (FDA) improved their 
      performances. The results demonstrate that a combination of geographic 
      information systems (GIS) with remote sensing (RS)-based ML models can 
      successfully map gully erosion susceptibility, particularly in low-income and 
      developing regions. This method can aid the analyses and decisions of natural 
      resources managers and local planners to reduce damages by focusing attention and 
      resources on areas prone to the worst and most damaging gully erosion.
FAU - Arabameri, Alireza
AU  - Arabameri A
AUID- ORCID: 0000-0002-1142-1666
AD  - Department of Geomorphology, Tarbiat Modares University, Tehran 36581-17994, 
      Iran.
FAU - Blaschke, Thomas
AU  - Blaschke T
AUID- ORCID: 0000-0002-1860-8458
AD  - Department of Geoinformatics-Z_GIS, University of Salzburg, 5020 Salzburg, 
      Austria.
FAU - Pradhan, Biswajeet
AU  - Pradhan B
AUID- ORCID: 0000-0001-9863-2054
AD  - Centre for Advanced Modelling and Geospatial Information Systems (CAMGIS), 
      Faculty of Engineering and IT, University of Technology Sydney, Ultimo, NSW 2007, 
      Australia.
AD  - Department of Energy and Mineral Resources Engineering, Choongmu-gwan, Sejong 
      University, 209 Neungdong-ro, Gwangjin-gu, Seoul 05006, Korea.
FAU - Pourghasemi, Hamid Reza
AU  - Pourghasemi HR
AUID- ORCID: 0000-0003-2328-2998
AD  - Department of Natural Resources and Environmental Engineering, College of 
      Agriculture, Shiraz University, Shiraz 71441-65186, Iran.
FAU - Tiefenbacher, John P
AU  - Tiefenbacher JP
AUID- ORCID: 0000-0001-9342-6550
AD  - Department of Geography, Texas State University, San Marcos, TX 78666, USA.
FAU - Bui, Dieu Tien
AU  - Bui DT
AD  - Institute of Research and Development, Duy Tan University, Da Nang 550000, 
      Vietnam.
LA  - eng
GR  - DK W 1237-N23/Austrian Science Fund/
PT  - Journal Article
DEP - 20200107
PL  - Switzerland
TA  - Sensors (Basel)
JT  - Sensors (Basel, Switzerland)
JID - 101204366
SB  - IM
PMC - PMC7014250
OTO - NOTNLM
OT  - GIS
OT  - Iran
OT  - ensemble
OT  - gully erosion
OT  - hybrid model
OT  - soft computing
COIS- The authors declare no conflict of interest.
EDAT- 2020/01/16 06:00
MHDA- 2020/01/16 06:01
PMCR- 2020/01/01
CRDT- 2020/01/16 06:00
PHST- 2019/11/27 00:00 [received]
PHST- 2019/12/22 00:00 [revised]
PHST- 2019/12/31 00:00 [accepted]
PHST- 2020/01/16 06:00 [entrez]
PHST- 2020/01/16 06:00 [pubmed]
PHST- 2020/01/16 06:01 [medline]
PHST- 2020/01/01 00:00 [pmc-release]
AID - s20020335 [pii]
AID - sensors-20-00335 [pii]
AID - 10.3390/s20020335 [doi]
PST - epublish
SO  - Sensors (Basel). 2020 Jan 7;20(2):335. doi: 10.3390/s20020335.