@article{BandJanizadehSahaetal.,
  author    = {Band, Shahab S. and Janizadeh, Saeid and Saha, Sunil and Mukherjee, Kaustuv and Khosrobeigi Bozchaloei, Saeid and Cerd{\`a}, Artemi and Shokri, Manouchehr and Mosavi, Amir Hosein},
  title     = {Evaluating the Efficiency of Different Regression, Decision Tree, and Bayesian Machine Learning Algorithms in Spatial Piping Erosion Susceptibility Using ALOS/PALSAR Data},
  series = {Land},
  volume    = {2020},
  journal   = {Land},
  number    = {volume 9, issue 10, article 346},
  publisher = {MDPI},
  address   = {Basel},
  doi       = {10.3390/land9100346},
  url       = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20210122-43424},
  pages     = {1 -- 22},
  abstract  = {Piping erosion is one form of water erosion that leads to significant changes in the landscape and environmental degradation. In the present study, we evaluated piping erosion modeling in the Zarandieh watershed of Markazi province in Iran based on random forest (RF), support vector machine (SVM), and Bayesian generalized linear models (Bayesian GLM) machine learning algorithms. For this goal, due to the importance of various geo-environmental and soil properties in the evolution and creation of piping erosion, 18 variables were considered for modeling the piping erosion susceptibility in the Zarandieh watershed. A total of 152 points of piping erosion were recognized in the study area that were divided into training (70\%) and validation (30\%) for modeling. The area under curve (AUC) was used to assess the effeciency of the RF, SVM, and Bayesian GLM. Piping erosion susceptibility results indicated that all three RF, SVM, and Bayesian GLM models had high efficiency in the testing step, such as the AUC shown with values of 0.9 for RF, 0.88 for SVM, and 0.87 for Bayesian GLM. Altitude, pH, and bulk density were the variables that had the greatest influence on the piping erosion susceptibility in the Zarandieh watershed. This result indicates that geo-environmental and soil chemical variables are accountable for the expansion of piping erosion in the Zarandieh watershed.},
  subject      = {Maschinelles Lernen},
  language  = {en}
}
@article{MeiabadiMoradiKaramimoghadametal.,
  author    = {Meiabadi, Mohammad Saleh and Moradi, Mahmoud and Karamimoghadam, Mojtaba and Ardabili, Sina and Bodaghi, Mahdi and Shokri, Manouchehr and Mosavi, Amir Hosein},
  title     = {Modeling the Producibility of 3D Printing in Polylactic Acid Using Artificial Neural Networks and Fused Filament Fabrication},
  series = {polymers},
  volume    = {2021},
  journal   = {polymers},
  number    = {Volume 13, issue 19, article 3219},
  publisher = {MDPI},
  address   = {Basel},
  doi       = {10.3390/polym13193219},
  url       = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20220110-45518},
  pages     = {1 -- 21},
  abstract  = {Polylactic acid (PLA) is a highly applicable material that is used in 3D printers due to some significant features such as its deformation property and affordable cost. For improvement of the end-use quality, it is of significant importance to enhance the quality of fused filament fabrication (FFF)-printed objects in PLA. The purpose of this investigation was to boost toughness and to reduce the production cost of the FFF-printed tensile test samples with the desired part thickness. To remove the need for numerous and idle printing samples, the response surface method (RSM) was used. Statistical analysis was performed to deal with this concern by considering extruder temperature (ET), infill percentage (IP), and layer thickness (LT) as controlled factors. The artificial intelligence method of artificial neural network (ANN) and ANN-genetic algorithm (ANN-GA) were further developed to estimate the toughness, part thickness, and production-cost-dependent variables. Results were evaluated by correlation coefficient and RMSE values. According to the modeling results, ANN-GA as a hybrid machine learning (ML) technique could enhance the accuracy of modeling by about 7.5, 11.5, and 4.5\% for toughness, part thickness, and production cost, respectively, in comparison with those for the single ANN method. On the other hand, the optimization results confirm that the optimized specimen is cost-effective and able to comparatively undergo deformation, which enables the usability of printed PLA objects.},
  subject      = {3D-Druck},
  language  = {en}
}
@article{BandJanizadehChandraPaletal.,
  author    = {Band, Shahab S. and Janizadeh, Saeid and Chandra Pal, Subodh and Saha, Asish and Chakrabortty, Rabbin and Shokri, Manouchehr and Mosavi, Amir Hosein},
  title     = {Novel Ensemble Approach of Deep Learning Neural Network (DLNN) Model and Particle Swarm Optimization (PSO) Algorithm for Prediction of Gully Erosion Susceptibility},
  series = {Sensors},
  volume    = {2020},
  journal   = {Sensors},
  number    = {Volume 20, issue 19, article 5609},
  publisher = {MDPI},
  address   = {Basel},
  doi       = {10.3390/s20195609},
  url       = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20210122-43341},
  pages     = {1 -- 27},
  abstract  = {This study aims to evaluate a new approach in modeling gully erosion susceptibility (GES) based on a deep learning neural network (DLNN) model and an ensemble particle swarm optimization (PSO) algorithm with DLNN (PSO-DLNN), comparing these approaches with common artificial neural network (ANN) and support vector machine (SVM) models in Shirahan watershed, Iran. For this purpose, 13 independent variables affecting GES in the study area, namely, altitude, slope, aspect, plan curvature, profile curvature, drainage density, distance from a river, land use, soil, lithology, rainfall, stream power index (SPI), and topographic wetness index (TWI), were prepared. A total of 132 gully erosion locations were identified during field visits. To implement the proposed model, the dataset was divided into the two categories of training (70\%) and testing (30\%). The results indicate that the area under the curve (AUC) value from receiver operating characteristic (ROC) considering the testing datasets of PSO-DLNN is 0.89, which indicates superb accuracy. The rest of the models are associated with optimal accuracy and have similar results to the PSO-DLNN model; the AUC values from ROC of DLNN, SVM, and ANN for the testing datasets are 0.87, 0.85, and 0.84, respectively. The efficiency of the proposed model in terms of prediction of GES was increased. Therefore, it can be concluded that the DLNN model and its ensemble with the PSO algorithm can be used as a novel and practical method to predict gully erosion susceptibility, which can help planners and managers to manage and reduce the risk of this phenomenon.},
  subject      = {Geoinformatik},
  language  = {en}
}