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Mr. Kayvan Ghaderi
University of Kurdistan, Sanandaj, Iran

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Research Keywords & Expertise

0 Classification
0 Digital Image Processing
0 Machine Learning
0 Artifical Intelligence
0 Image Processing and Analysis

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Career Timeline

University of Kurdistan

University Lecturer

01 September 2012 - 01 September 2021




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Journal article
Published: 13 January 2020 in Remote Sensing
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Mapping flood-prone areas is a key activity in flood disaster management. In this paper, we propose a new flood susceptibility mapping technique. We employ new ensemble models based on bagging as a meta-classifier and K-Nearest Neighbor (KNN) coarse, cosine, cubic, and weighted base classifiers to spatially forecast flooding in the Haraz watershed in northern Iran. We identified flood-prone areas using data from Sentinel-1 sensor. We then selected 10 conditioning factors to spatially predict floods and assess their predictive power using the Relief Attribute Evaluation (RFAE) method. Model validation was performed using two statistical error indices and the area under the curve (AUC). Our results show that the Bagging–Cubic–KNN ensemble model outperformed other ensemble models. It decreased the overfitting and variance problems in the training dataset and enhanced the prediction accuracy of the Cubic–KNN model (AUC=0.660). We therefore recommend that the Bagging–Cubic–KNN model be more widely applied for the sustainable management of flood-prone areas.

ACS Style

Himan Shahabi; Ataollah Shirzadi; Kayvan Ghaderi; Ebrahim Omidvar; Nadhir Al-Ansari; John J. Clague; Marten Geertsema; Khabat Khosravi; Ata Amini; Sepideh Bahrami; Omid Rahmati; Kyoumars Habibi; Ayub Mohammadi; Hoang Nguyen; Assefa M. Melesse; Baharin Bin Ahmad; Anuar Ahmad. Flood Detection and Susceptibility Mapping Using Sentinel-1 Remote Sensing Data and a Machine Learning Approach: Hybrid Intelligence of Bagging Ensemble Based on K-Nearest Neighbor Classifier. Remote Sensing 2020, 12, 266 .

AMA Style

Himan Shahabi, Ataollah Shirzadi, Kayvan Ghaderi, Ebrahim Omidvar, Nadhir Al-Ansari, John J. Clague, Marten Geertsema, Khabat Khosravi, Ata Amini, Sepideh Bahrami, Omid Rahmati, Kyoumars Habibi, Ayub Mohammadi, Hoang Nguyen, Assefa M. Melesse, Baharin Bin Ahmad, Anuar Ahmad. Flood Detection and Susceptibility Mapping Using Sentinel-1 Remote Sensing Data and a Machine Learning Approach: Hybrid Intelligence of Bagging Ensemble Based on K-Nearest Neighbor Classifier. Remote Sensing. 2020; 12 (2):266.

Chicago/Turabian Style

Himan Shahabi; Ataollah Shirzadi; Kayvan Ghaderi; Ebrahim Omidvar; Nadhir Al-Ansari; John J. Clague; Marten Geertsema; Khabat Khosravi; Ata Amini; Sepideh Bahrami; Omid Rahmati; Kyoumars Habibi; Ayub Mohammadi; Hoang Nguyen; Assefa M. Melesse; Baharin Bin Ahmad; Anuar Ahmad. 2020. "Flood Detection and Susceptibility Mapping Using Sentinel-1 Remote Sensing Data and a Machine Learning Approach: Hybrid Intelligence of Bagging Ensemble Based on K-Nearest Neighbor Classifier." Remote Sensing 12, no. 2: 266.

Conference paper
Published: 01 May 2013 in 2013 21st Iranian Conference on Electrical Engineering (ICEE)
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The paper presents a novel semi-blind watermarking scheme for image copyright protection, which is developed in the Lifting Wavelet Transform (LWT) and is based on Singular Value Decomposition (SVD). We have been used fractal decoding to make a very compact representation of watermark image. In the embedding phase of watermarking scheme, at first; we perform decomposing of the host image with 2D-LWT transform, then SVD is applied to sub-bands of transformed image, and embed the watermark by modifying the singular values. In the watermark extraction phase, the embedded codes are extracted from the watermarked image. Then the watermark image is rendered by running the extracted code. The experiments indicate that the watermark is robust against the different attacks, such as average filter, Jpeg compression, rotation and etc.

ACS Style

Kayvan. Ghaderi; Fardin. Akhlaghian; Parham. Moradi. A new robust semi-blind digital image watermarking approach based on LWT-SVD and fractal images. 2013 21st Iranian Conference on Electrical Engineering (ICEE) 2013, 1 -5.

AMA Style

Kayvan. Ghaderi, Fardin. Akhlaghian, Parham. Moradi. A new robust semi-blind digital image watermarking approach based on LWT-SVD and fractal images. 2013 21st Iranian Conference on Electrical Engineering (ICEE). 2013; ():1-5.

Chicago/Turabian Style

Kayvan. Ghaderi; Fardin. Akhlaghian; Parham. Moradi. 2013. "A new robust semi-blind digital image watermarking approach based on LWT-SVD and fractal images." 2013 21st Iranian Conference on Electrical Engineering (ICEE) , no. : 1-5.

Conference paper
Published: 01 October 2012 in 2012 2nd International eConference on Computer and Knowledge Engineering (ICCKE)
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Digital watermarking has been proposed as a way to claim ownership. In this paper, a new approach in digital image watermarking based on discrete wavelet transform (DWT) and singular value decomposition (SVD) is presented. We use compositional pattern producing networks (CPPNs) to make a very compact representation of watermark. Using Neuro Evolution of Augmenting Topologies (NEAT) will evolve the CPPN structure to produce a suitable watermark image. In the embedding phase, at first we perform decomposing of the host image with 2D-DWT transform at 5-level, then the SVD is applied to LH3-LH5 sub-bands of transformed image, and embed the watermark by modifying the singular values. In watermark extraction phase, the embedded coefficients of CPPN neat are extracted from the watermarked image. Then the watermark image is rendered by CPPN. The experiments indicate that the watermark is robust against the different attacks, such as average filter, Jpeg compression and etc.

ACS Style

Kayvan Ghaderi; Fardin Akhlghian; Parham Moradi. A new digital image watermarking approach based on DWT-SVD and CPPN-NEAT. 2012 2nd International eConference on Computer and Knowledge Engineering (ICCKE) 2012, 12 -17.

AMA Style

Kayvan Ghaderi, Fardin Akhlghian, Parham Moradi. A new digital image watermarking approach based on DWT-SVD and CPPN-NEAT. 2012 2nd International eConference on Computer and Knowledge Engineering (ICCKE). 2012; ():12-17.

Chicago/Turabian Style

Kayvan Ghaderi; Fardin Akhlghian; Parham Moradi. 2012. "A new digital image watermarking approach based on DWT-SVD and CPPN-NEAT." 2012 2nd International eConference on Computer and Knowledge Engineering (ICCKE) , no. : 12-17.