@article{HarirchianLahmerRasulzade, author = {Harirchian, Ehsan and Lahmer, Tom and Rasulzade, Shahla}, title = {Earthquake Hazard Safety Assessment of Existing Buildings Using Optimized Multi-Layer Perceptron Neural Network}, series = {Energies}, volume = {2020}, journal = {Energies}, number = {Volume 13, Issue 8, 2060}, publisher = {MDPI}, address = {Basel}, doi = {10.3390/en13082060}, url = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20200504-41575}, pages = {16}, abstract = {The latest earthquakes have proven that several existing buildings, particularly in developing countries, are not secured from damages of earthquake. A variety of statistical and machine-learning approaches have been proposed to identify vulnerable buildings for the prioritization of retrofitting. The present work aims to investigate earthquake susceptibility through the combination of six building performance variables that can be used to obtain an optimal prediction of the damage state of reinforced concrete buildings using artificial neural network (ANN). In this regard, a multi-layer perceptron network is trained and optimized using a database of 484 damaged buildings from the D{\"u}zce earthquake in Turkey. The results demonstrate the feasibility and effectiveness of the selected ANN approach to classify concrete structural damage that can be used as a preliminary assessment technique to identify vulnerable buildings in disaster risk-management programs.}, subject = {Erdbeben}, language = {en} } @article{SaadatfarKhosraviHassannatajJoloudarietal., author = {Saadatfar, Hamid and Khosravi, Samiyeh and Hassannataj Joloudari, Javad and Mosavi, Amir and Shamshirband, Shahaboddin}, title = {A New K-Nearest Neighbors Classifier for Big Data Based on Efficient Data Pruning}, series = {Mathematics}, volume = {2020}, journal = {Mathematics}, number = {volume 8, issue 2, article 286}, publisher = {MDPI}, doi = {10.3390/math8020286}, url = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20200225-40996}, pages = {12}, abstract = {The K-nearest neighbors (KNN) machine learning algorithm is a well-known non-parametric classification method. However, like other traditional data mining methods, applying it on big data comes with computational challenges. Indeed, KNN determines the class of a new sample based on the class of its nearest neighbors; however, identifying the neighbors in a large amount of data imposes a large computational cost so that it is no longer applicable by a single computing machine. One of the proposed techniques to make classification methods applicable on large datasets is pruning. LC-KNN is an improved KNN method which first clusters the data into some smaller partitions using the K-means clustering method; and then applies the KNN for each new sample on the partition which its center is the nearest one. However, because the clusters have different shapes and densities, selection of the appropriate cluster is a challenge. In this paper, an approach has been proposed to improve the pruning phase of the LC-KNN method by taking into account these factors. The proposed approach helps to choose a more appropriate cluster of data for looking for the neighbors, thus, increasing the classification accuracy. The performance of the proposed approach is evaluated on different real datasets. The experimental results show the effectiveness of the proposed approach and its higher classification accuracy and lower time cost in comparison to other recent relevant methods.}, subject = {Maschinelles Lernen}, language = {en} } @article{ShamshirbandBabanezhadMosavietal., author = {Shamshirband, Shahaboddin and Babanezhad, Meisam and Mosavi, Amir and Nabipour, Narjes and Hajnal, Eva and Nadai, Laszlo and Chau, Kwok-Wing}, title = {Prediction of flow characteristics in the bubble column reactor by the artificial pheromone-based communication of biological ants}, series = {Engineering Applications of Computational Fluid Mechanics}, volume = {2020}, journal = {Engineering Applications of Computational Fluid Mechanics}, number = {volume 14, issue 1}, publisher = {Taylor \& Francis}, doi = {10.1080/19942060.2020.1715842}, url = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20200227-41013}, pages = {367 -- 378}, abstract = {A novel combination of the ant colony optimization algorithm (ACO)and computational fluid dynamics (CFD) data is proposed for modeling the multiphase chemical reactors. The proposed intelligent model presents a probabilistic computational strategy for predicting various levels of three-dimensional bubble column reactor (BCR) flow. The results prove an enhanced communication between ant colony prediction and CFD data in different sections of the BCR.}, subject = {Maschinelles Lernen}, language = {en} } @article{MosaviShamshirbandEsmaeilbeikietal., author = {Mosavi, Amir and Shamshirband, Shahaboddin and Esmaeilbeiki, Fatemeh and Zarehaghi, Davoud and Neyshabouri, Mohammadreza and Samadianfard, Saeed and Ghorbani, Mohammad Ali and Nabipour, Narjes and Chau, Kwok-Wing}, title = {Comparative analysis of hybrid models of firefly optimization algorithm with support vector machines and multilayer perceptron for predicting soil temperature at different depths}, series = {Engineering Applications of Computational Fluid Mechanics}, volume = {2020}, journal = {Engineering Applications of Computational Fluid Mechanics}, number = {Volume 14, Issue 1}, doi = {10.1080/19942060.2020.1788644}, url = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20200911-42347}, pages = {939 -- 953}, abstract = {This research aims to model soil temperature (ST) using machine learning models of multilayer perceptron (MLP) algorithm and support vector machine (SVM) in hybrid form with the Firefly optimization algorithm, i.e. MLP-FFA and SVM-FFA. In the current study, measured ST and meteorological parameters of Tabriz and Ahar weather stations in a period of 2013-2015 are used for training and testing of the studied models with one and two days as a delay. To ascertain conclusive results for validation of the proposed hybrid models, the error metrics are benchmarked in an independent testing period. Moreover, Taylor diagrams utilized for that purpose. Obtained results showed that, in a case of one day delay, except in predicting ST at 5 cm below the soil surface (ST5cm) at Tabriz station, MLP-FFA produced superior results compared with MLP, SVM, and SVM-FFA models. However, for two days delay, MLP-FFA indicated increased accuracy in predicting ST5cm and ST 20cm of Tabriz station and ST10cm of Ahar station in comparison with SVM-FFA. Additionally, for all of the prescribed models, the performance of the MLP-FFA and SVM-FFA hybrid models in the testing phase was found to be meaningfully superior to the classical MLP and SVM models.}, subject = {Bodentemperatur}, language = {en} } @article{HassannatajJoloudariHassannatajJoloudariSaadatfaretal., author = {Hassannataj Joloudari, Javad and Hassannataj Joloudari, Edris and Saadatfar, Hamid and GhasemiGol, Mohammad and Razavi, Seyyed Mohammad and Mosavi, Amir and Nabipour, Narjes and Shamshirband, Shahaboddin and Nadai, Laszlo}, title = {Coronary Artery Disease Diagnosis: Ranking the Significant Features Using a Random Trees Model}, series = {International Journal of Environmental Research and Public Health, IJERPH}, volume = {2020}, journal = {International Journal of Environmental Research and Public Health, IJERPH}, number = {Volume 17, Issue 3, 731}, publisher = {MDPI}, doi = {10.3390/ijerph17030731}, url = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20200213-40819}, pages = {24}, abstract = {Heart disease is one of the most common diseases in middle-aged citizens. Among the vast number of heart diseases, coronary artery disease (CAD) is considered as a common cardiovascular disease with a high death rate. The most popular tool for diagnosing CAD is the use of medical imaging, e.g., angiography. However, angiography is known for being costly and also associated with a number of side effects. Hence, the purpose of this study is to increase the accuracy of coronary heart disease diagnosis through selecting significant predictive features in order of their ranking. In this study, we propose an integrated method using machine learning. The machine learning methods of random trees (RTs), decision tree of C5.0, support vector machine (SVM), and decision tree of Chi-squared automatic interaction detection (CHAID) are used in this study. The proposed method shows promising results and the study confirms that the RTs model outperforms other models.}, subject = {Maschinelles Lernen}, language = {en} } @article{KargarSamadianfardParsaetal., author = {Kargar, Katayoun and Samadianfard, Saeed and Parsa, Javad and Nabipour, Narjes and Shamshirband, Shahaboddin and Mosavi, Amir and Chau, Kwok-Wing}, title = {Estimating longitudinal dispersion coefficient in natural streams using empirical models and machine learning algorithms}, series = {Engineering Applications of Computational Fluid Mechanics}, volume = {2020}, journal = {Engineering Applications of Computational Fluid Mechanics}, number = {Volume 14, No. 1}, publisher = {Taylor \& Francis}, doi = {10.1080/19942060.2020.1712260}, url = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20200128-40775}, pages = {311 -- 322}, abstract = {The longitudinal dispersion coefficient (LDC) plays an important role in modeling the transport of pollutants and sediment in natural rivers. As a result of transportation processes, the concentration of pollutants changes along the river. Various studies have been conducted to provide simple equations for estimating LDC. In this study, machine learning methods, namely support vector regression, Gaussian process regression, M5 model tree (M5P) and random forest, and multiple linear regression were examined in predicting the LDC in natural streams. Data sets from 60 rivers around the world with different hydraulic and geometric features were gathered to develop models for LDC estimation. Statistical criteria, including correlation coefficient (CC), root mean squared error (RMSE) and mean absolute error (MAE), were used to scrutinize the models. The LDC values estimated by these models were compared with the corresponding results of common empirical models. The Taylor chart was used to evaluate the models and the results showed that among the machine learning models, M5P had superior performance, with CC of 0.823, RMSE of 454.9 and MAE of 380.9. The model of Sahay and Dutta, with CC of 0.795, RMSE of 460.7 and MAE of 306.1, gave more precise results than the other empirical models. The main advantage of M5P models is their ability to provide practical formulae. In conclusion, the results proved that the developed M5P model with simple formulations was superior to other machine learning models and empirical models; therefore, it can be used as a proper tool for estimating the LDC in rivers.}, subject = {Maschinelles Lernen}, language = {en} } @article{DehghaniSalehiMosavietal., author = {Dehghani, Majid and Salehi, Somayeh and Mosavi, Amir and Nabipour, Narjes and Shamshirband, Shahaboddin and Ghamisi, Pedram}, title = {Spatial Analysis of Seasonal Precipitation over Iran: Co-Variation with Climate Indices}, series = {ISPRS, International Journal of Geo-Information}, volume = {2020}, journal = {ISPRS, International Journal of Geo-Information}, number = {Volume 9, Issue 2, 73}, publisher = {MDPI}, doi = {10.3390/ijgi9020073}, url = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20200128-40740}, pages = {23}, abstract = {Temporary changes in precipitation may lead to sustained and severe drought or massive floods in different parts of the world. Knowing the variation in precipitation can effectively help the water resources decision-makers in water resources management. Large-scale circulation drivers have a considerable impact on precipitation in different parts of the world. In this research, the impact of El Ni{\~n}o-Southern Oscillation (ENSO), Pacific Decadal Oscillation (PDO), and North Atlantic Oscillation (NAO) on seasonal precipitation over Iran was investigated. For this purpose, 103 synoptic stations with at least 30 years of data were utilized. The Spearman correlation coefficient between the indices in the previous 12 months with seasonal precipitation was calculated, and the meaningful correlations were extracted. Then, the month in which each of these indices has the highest correlation with seasonal precipitation was determined. Finally, the overall amount of increase or decrease in seasonal precipitation due to each of these indices was calculated. Results indicate the Southern Oscillation Index (SOI), NAO, and PDO have the most impact on seasonal precipitation, respectively. Additionally, these indices have the highest impact on the precipitation in winter, autumn, spring, and summer, respectively. SOI has a diverse impact on winter precipitation compared to the PDO and NAO, while in the other seasons, each index has its special impact on seasonal precipitation. Generally, all indices in different phases may decrease the seasonal precipitation up to 100\%. However, the seasonal precipitation may increase more than 100\% in different seasons due to the impact of these indices. The results of this study can be used effectively in water resources management and especially in dam operation.}, subject = {Maschinelles Lernen}, language = {en} } @article{NabipourDehghaniMosavietal., author = {Nabipour, Narjes and Dehghani, Majid and Mosavi, Amir and Shamshirband, Shahaboddin}, title = {Short-Term Hydrological Drought Forecasting Based on Different Nature-Inspired Optimization Algorithms Hybridized With Artificial Neural Networks}, series = {IEEE Access}, volume = {2020}, journal = {IEEE Access}, number = {volume 8}, publisher = {IEEE}, doi = {10.1109/ACCESS.2020.2964584}, url = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20200213-40796}, pages = {15210 -- 15222}, abstract = {Hydrological drought forecasting plays a substantial role in water resources management. Hydrological drought highly affects the water allocation and hydropower generation. In this research, short term hydrological drought forecasted based on the hybridized of novel nature-inspired optimization algorithms and Artificial Neural Networks (ANN). For this purpose, the Standardized Hydrological Drought Index (SHDI) and the Standardized Precipitation Index (SPI) were calculated in one, three, and six aggregated months. Then, three states where proposed for SHDI forecasting, and 36 input-output combinations were extracted based on the cross-correlation analysis. In the next step, newly proposed optimization algorithms, including Grasshopper Optimization Algorithm (GOA), Salp Swarm algorithm (SSA), Biogeography-based optimization (BBO), and Particle Swarm Optimization (PSO) hybridized with the ANN were utilized for SHDI forecasting and the results compared to the conventional ANN. Results indicated that the hybridized model outperformed compared to the conventional ANN. PSO performed better than the other optimization algorithms. The best models forecasted SHDI1 with R2 = 0.68 and RMSE = 0.58, SHDI3 with R 2 = 0.81 and RMSE = 0.45 and SHDI6 with R 2 = 0.82 and RMSE = 0.40.}, subject = {Maschinelles Lernen}, language = {en} } @article{MengNomanQasemShokrietal., author = {Meng, Yinghui and Noman Qasem, Sultan and Shokri, Manouchehr and Shamshirband, Shahaboddin}, title = {Dimension Reduction of Machine Learning-Based Forecasting Models Employing Principal Component Analysis}, series = {Mathematics}, volume = {2020}, journal = {Mathematics}, number = {volume 8, issue 8, article 1233}, publisher = {MDPI}, address = {Basel}, doi = {10.3390/math8081233}, url = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20200811-42125}, pages = {15}, abstract = {In this research, an attempt was made to reduce the dimension of wavelet-ANFIS/ANN (artificial neural network/adaptive neuro-fuzzy inference system) models toward reliable forecasts as well as to decrease computational cost. In this regard, the principal component analysis was performed on the input time series decomposed by a discrete wavelet transform to feed the ANN/ANFIS models. The models were applied for dissolved oxygen (DO) forecasting in rivers which is an important variable affecting aquatic life and water quality. The current values of DO, water surface temperature, salinity, and turbidity have been considered as the input variable to forecast DO in a three-time step further. The results of the study revealed that PCA can be employed as a powerful tool for dimension reduction of input variables and also to detect inter-correlation of input variables. Results of the PCA-wavelet-ANN models are compared with those obtained from wavelet-ANN models while the earlier one has the advantage of less computational time than the later models. Dealing with ANFIS models, PCA is more beneficial to avoid wavelet-ANFIS models creating too many rules which deteriorate the efficiency of the ANFIS models. Moreover, manipulating the wavelet-ANFIS models utilizing PCA leads to a significant decreasing in computational time. Finally, it was found that the PCA-wavelet-ANN/ANFIS models can provide reliable forecasts of dissolved oxygen as an important water quality indicator in rivers.}, subject = {Maschinelles Lernen}, language = {en} } @article{HarirchianLahmerBuddhirajuetal., author = {Harirchian, Ehsan and Lahmer, Tom and Buddhiraju, Sreekanth and Mohammad, Kifaytullah and Mosavi, Amir}, title = {Earthquake Safety Assessment of Buildings through Rapid Visual Screening}, series = {Buildings}, volume = {2020}, journal = {Buildings}, number = {Volume 10, Issue 3}, publisher = {MDPI}, doi = {10.3390/buildings10030051}, url = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20200331-41153}, pages = {15}, abstract = {Earthquake is among the most devastating natural disasters causing severe economical, environmental, and social destruction. Earthquake safety assessment and building hazard monitoring can highly contribute to urban sustainability through identification and insight into optimum materials and structures. While the vulnerability of structures mainly depends on the structural resistance, the safety assessment of buildings can be highly challenging. In this paper, we consider the Rapid Visual Screening (RVS) method, which is a qualitative procedure for estimating structural scores for buildings suitable for medium- to high-seismic cases. This paper presents an overview of the common RVS methods, i.e., FEMA P-154, IITK-GGSDMA, and EMPI. To examine the accuracy and validation, a practical comparison is performed between their assessment and observed damage of reinforced concrete buildings from a street survey in the Bing{\"o}l region, Turkey, after the 1 May 2003 earthquake. The results demonstrate that the application of RVS methods for preliminary damage estimation is a vital tool. Furthermore, the comparative analysis showed that FEMA P-154 creates an assessment that overestimates damage states and is not economically viable, while EMPI and IITK-GGSDMA provide more accurate and practical estimation, respectively.}, subject = {Maschinelles Lernen}, language = {en} }