TY - JOUR A1 - Band, Shahab S. A1 - Janizadeh, Saeid A1 - Chandra Pal, Subodh A1 - Saha, Asish A1 - Chakrabortty, Rabbin A1 - Shokri, Manouchehr A1 - Mosavi, Amir Hosein T1 - Novel Ensemble Approach of Deep Learning Neural Network (DLNN) Model and Particle Swarm Optimization (PSO) Algorithm for Prediction of Gully Erosion Susceptibility JF - Sensors N2 - 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. KW - Geoinformatik KW - Maschinelles Lernen KW - gully erosion susceptibility KW - deep learning neural network KW - partical swarm optimization KW - OA-Publikationsfonds2020 Y1 - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20210122-43341 UR - https://www.mdpi.com/1424-8220/20/19/5609 VL - 2020 IS - Volume 20, issue 19, article 5609 SP - 1 EP - 27 PB - MDPI CY - Basel ER - TY - JOUR A1 - Harirchian, Ehsan A1 - Kumari, Vandana A1 - Jadhav, Kirti A1 - Raj Das, Rohan A1 - Rasulzade, Shahla A1 - Lahmer, Tom T1 - A Machine Learning Framework for Assessing Seismic Hazard Safety of Reinforced Concrete Buildings JF - Applied Sciences N2 - Although averting a seismic disturbance and its physical, social, and economic disruption is practically impossible, using the advancements in computational science and numerical modeling shall equip humanity to predict its severity, understand the outcomes, and equip for post-disaster management. Many buildings exist amidst the developed metropolitan areas, which are senile and still in service. These buildings were also designed before establishing national seismic codes or without the introduction of construction regulations. In that case, risk reduction is significant for developing alternatives and designing suitable models to enhance the existing structure’s performance. Such models will be able to classify risks and casualties related to possible earthquakes through emergency preparation. Thus, it is crucial to recognize structures that are susceptible to earthquake vibrations and need to be prioritized for retrofitting. However, each building’s behavior under seismic actions cannot be studied through performing structural analysis, as it might be unrealistic because of the rigorous computations, long period, and substantial expenditure. Therefore, it calls for a simple, reliable, and accurate process known as Rapid Visual Screening (RVS), which serves as a primary screening platform, including an optimum number of seismic parameters and predetermined performance damage conditions for structures. In this study, the damage classification technique was studied, and the efficacy of the Machine Learning (ML) method in damage prediction via a Support Vector Machine (SVM) model was explored. The ML model is trained and tested separately on damage data from four different earthquakes, namely Ecuador, Haiti, Nepal, and South Korea. Each dataset consists of varying numbers of input data and eight performance modifiers. Based on the study and the results, the ML model using SVM classifies the given input data into the belonging classes and accomplishes the performance on hazard safety evaluation of buildings. KW - Erdbeben KW - Vulnerability KW - Earthquake KW - damaged buildings KW - earthquake safety assessment KW - soft computing techniques KW - rapid visual screening KW - Machine Learning KW - vulnerability assessment KW - OA-Publikationsfonds2020 Y1 - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20201022-42744 UR - https://www.mdpi.com/2076-3417/10/20/7153 VL - 2020 IS - Volume 10, issue 20, article 7153 PB - MDPI CY - Basel ER - TY - JOUR A1 - Harirchian, Ehsan A1 - Lahmer, Tom A1 - Rasulzade, Shahla T1 - Earthquake Hazard Safety Assessment of Existing Buildings Using Optimized Multi-Layer Perceptron Neural Network JF - Energies N2 - 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 Düzce 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. KW - Erdbeben KW - Maschinelles Lernen KW - earthquake damage KW - seismic vulnerability KW - artificial neural network KW - OA-Publikationsfonds2020 Y1 - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20200504-41575 UR - https://www.mdpi.com/1996-1073/13/8/2060/htm VL - 2020 IS - Volume 13, Issue 8, 2060 PB - MDPI CY - Basel ER - TY - JOUR A1 - Sadeghzadeh, Milad A1 - Maddah, Heydar A1 - Ahmadi, Mohammad Hossein A1 - Khadang, Amirhosein A1 - Ghazvini, Mahyar A1 - Mosavi, Amir Hosein A1 - Nabipour, Narjes T1 - Prediction of Thermo-Physical Properties of TiO2-Al2O3/Water Nanoparticles by Using Artificial Neural Network JF - Nanomaterials N2 - In this paper, an artificial neural network is implemented for the sake of predicting the thermal conductivity ratio of TiO2-Al2O3/water nanofluid. TiO2-Al2O3/water in the role of an innovative type of nanofluid was synthesized by the sol–gel method. The results indicated that 1.5 vol.% of nanofluids enhanced the thermal conductivity by up to 25%. It was shown that the heat transfer coefficient was linearly augmented with increasing nanoparticle concentration, but its variation with temperature was nonlinear. It should be noted that the increase in concentration may cause the particles to agglomerate, and then the thermal conductivity is reduced. The increase in temperature also increases the thermal conductivity, due to an increase in the Brownian motion and collision of particles. In this research, for the sake of predicting the thermal conductivity of TiO2-Al2O3/water nanofluid based on volumetric concentration and temperature functions, an artificial neural network is implemented. In this way, for predicting thermal conductivity, SOM (self-organizing map) and BP-LM (Back Propagation-Levenberq-Marquardt) algorithms were used. Based on the results obtained, these algorithms can be considered as an exceptional tool for predicting thermal conductivity. Additionally, the correlation coefficient values were equal to 0.938 and 0.98 when implementing the SOM and BP-LM algorithms, respectively, which is highly acceptable. View Full-Text KW - Wärmeleitfähigkeit KW - Fluid KW - Neuronales Netz KW - Thermal conductivity KW - Nanofluid KW - Artificial neural network Y1 - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20200421-41308 UR - https://www.mdpi.com/2079-4991/10/4/697 VL - 2020 IS - Volume 10, Issue 4, 697 PB - MDPI CY - Basel ER - TY - JOUR A1 - Saadatfar, Hamid A1 - Khosravi, Samiyeh A1 - Hassannataj Joloudari, Javad A1 - Mosavi, Amir A1 - Shamshirband, Shahaboddin T1 - A New K-Nearest Neighbors Classifier for Big Data Based on Efficient Data Pruning JF - Mathematics N2 - 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. KW - Maschinelles Lernen KW - Machine learning KW - K-nearest neighbors KW - KNN KW - classifier KW - big data KW - clustering KW - cluster shape KW - cluster density KW - classification KW - reinforcement learning KW - data science KW - computation KW - artificial intelligence KW - OA-Publikationsfonds2020 Y1 - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20200225-40996 UR - https://www.mdpi.com/2227-7390/8/2/286 VL - 2020 IS - volume 8, issue 2, article 286 PB - MDPI ER - TY - JOUR A1 - Ahmadi, Mohammad Hossein A1 - Baghban, Alireza A1 - Sadeghzadeh, Milad A1 - Zamen, Mohammad A1 - Mosavi, Amir A1 - Shamshirband, Shahaboddin A1 - Kumar, Ravinder A1 - Mohammadi-Khanaposhtani, Mohammad T1 - Evaluation of electrical efficiency of photovoltaic thermal solar collector JF - Engineering Applications of Computational Fluid Mechanics N2 - In this study, machine learning methods of artificial neural networks (ANNs), least squares support vector machines (LSSVM), and neuro-fuzzy are used for advancing prediction models for thermal performance of a photovoltaic-thermal solar collector (PV/T). In the proposed models, the inlet temperature, flow rate, heat, solar radiation, and the sun heat have been considered as the input variables. Data set has been extracted through experimental measurements from a novel solar collector system. Different analyses are performed to examine the credibility of the introduced models and evaluate their performances. The proposed LSSVM model outperformed the ANFIS and ANNs models. LSSVM model is reported suitable when the laboratory measurements are costly and time-consuming, or achieving such values requires sophisticated interpretations. KW - Fotovoltaik KW - Erneuerbare Energien KW - Solar KW - Deep learning KW - Machine learning KW - Renewable energy KW - neural networks (NNs) KW - adaptive neuro-fuzzy inference system (ANFIS) KW - least square support vector machine (LSSVM) KW - photovoltaic-thermal (PV/T) KW - hybrid machine learning model KW - OA-Publikationsfonds2020 Y1 - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20200304-41049 UR - https://www.tandfonline.com/doi/full/10.1080/19942060.2020.1734094 VL - 2020 IS - volume 14, issue 1 SP - 545 EP - 565 PB - Taylor & Francis ER - TY - JOUR A1 - Shamshirband, Shahaboddin A1 - Babanezhad, Meisam A1 - Mosavi, Amir A1 - Nabipour, Narjes A1 - Hajnal, Eva A1 - Nadai, Laszlo A1 - Chau, Kwok-Wing T1 - Prediction of flow characteristics in the bubble column reactor by the artificial pheromone-based communication of biological ants JF - Engineering Applications of Computational Fluid Mechanics N2 - 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. KW - Maschinelles Lernen KW - Machine learning KW - Bubble column reactor KW - ant colony optimization algorithm (ACO) KW - flow pattern KW - computational fluid dynamics (CFD) KW - big data KW - OA-Publikationsfonds2020 Y1 - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20200227-41013 UR - https://www.tandfonline.com/doi/full/10.1080/19942060.2020.1715842 VL - 2020 IS - volume 14, issue 1 SP - 367 EP - 378 PB - Taylor & Francis ER - TY - JOUR A1 - Harirchian, Ehsan A1 - Jadhav, Kirti A1 - Mohammad, Kifaytullah A1 - Aghakouchaki Hosseini, Seyed Ehsan A1 - Lahmer, Tom T1 - A Comparative Study of MCDM Methods Integrated with Rapid Visual Seismic Vulnerability Assessment of Existing RC Structures JF - Applied Sciences N2 - Recently, the demand for residence and usage of urban infrastructure has been increased, thereby resulting in the elevation of risk levels of human lives over natural calamities. The occupancy demand has rapidly increased the construction rate, whereas the inadequate design of structures prone to more vulnerability. Buildings constructed before the development of seismic codes have an additional susceptibility to earthquake vibrations. The structural collapse causes an economic loss as well as setbacks for human lives. An application of different theoretical methods to analyze the structural behavior is expensive and time-consuming. Therefore, introducing a rapid vulnerability assessment method to check structural performances is necessary for future developments. The process, as mentioned earlier, is known as Rapid Visual Screening (RVS). This technique has been generated to identify, inventory, and screen structures that are potentially hazardous. Sometimes, poor construction quality does not provide some of the required parameters; in this case, the RVS process turns into a tedious scenario. Hence, to tackle such a situation, multiple-criteria decision-making (MCDM) methods for the seismic vulnerability assessment opens a new gateway. The different parameters required by RVS can be taken in MCDM. MCDM evaluates multiple conflicting criteria in decision making in several fields. This paper has aimed to bridge the gap between RVS and MCDM. Furthermore, to define the correlation between these techniques, implementation of the methodologies from Indian, Turkish, and Federal Emergency Management Agency (FEMA) codes has been done. The effects of seismic vulnerability of structures have been observed and compared. KW - Erdbebensicherheit KW - damaged buildings KW - earthquake safety assessment KW - soft computing techniques KW - rapid visual screening KW - seismic risk estimation KW - Multi-criteria decision making KW - vulnerability assessment KW - OA-Publikationsfonds2020 Y1 - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20200918-42360 UR - https://www.mdpi.com/2076-3417/10/18/6411/htm VL - 2020 IS - Volume 10, issue 18, article 6411 PB - MDPI CY - Basel ER - TY - JOUR A1 - Mosavi, Amir A1 - Shamshirband, Shahaboddin A1 - Esmaeilbeiki, Fatemeh A1 - Zarehaghi, Davoud A1 - Neyshabouri, Mohammadreza A1 - Samadianfard, Saeed A1 - Ghorbani, Mohammad Ali A1 - Nabipour, Narjes A1 - Chau, Kwok-Wing T1 - Comparative analysis of hybrid models of firefly optimization algorithm with support vector machines and multilayer perceptron for predicting soil temperature at different depths JF - Engineering Applications of Computational Fluid Mechanics N2 - 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. KW - Bodentemperatur KW - Algorithmus KW - Maschinelles Lernen KW - Neuronales Netz KW - firefly optimization algorithm KW - soil temperature KW - artificial neural networks KW - hybrid machine learning KW - OA-Publikationsfonds2019 Y1 - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20200911-42347 UR - https://www.tandfonline.com/doi/full/10.1080/19942060.2020.1788644 VL - 2020 IS - Volume 14, Issue 1 SP - 939 EP - 953 ER - TY - JOUR A1 - Amirinasab, Mehdi A1 - Shamshirband, Shahaboddin A1 - Chronopoulos, Anthony Theodore A1 - Mosavi, Amir A1 - Nabipour, Narjes T1 - Energy‐Efficient Method for Wireless Sensor Networks Low‐Power Radio Operation in Internet of Things JF - electronics N2 - The radio operation in wireless sensor networks (WSN) in Internet of Things (IoT)applications is the most common source for power consumption. Consequently, recognizing and controlling the factors affecting radio operation can be valuable for managing the node power consumption. Among essential factors affecting radio operation, the time spent for checking the radio is of utmost importance for monitoring power consumption. It can lead to false WakeUp or idle listening in radio duty cycles and ContikiMAC. ContikiMAC is a low‐power radio duty‐cycle protocol in Contiki OS used in WakeUp mode, as a clear channel assessment (CCA) for checking radio status periodically. This paper presents a detailed analysis of radio WakeUp time factors of ContikiMAC. Furthermore, we propose a lightweight CCA (LW‐CCA) as an extension to ContikiMAC to reduce the Radio Duty‐Cycles in false WakeUps and idle listening though using dynamic received signal strength indicator (RSSI) status check time. The simulation results in the Cooja simulator show that LW‐CCA reduces about 8% energy consumption in nodes while maintaining up to 99% of the packet delivery rate (PDR). KW - Internet der Dinge KW - Internet of things KW - wireless sensor networks KW - ContikiMAC KW - energy efficiency KW - duty-cycles KW - clear channel assessments KW - fog computing KW - smart sensors KW - signal processing KW - received signal strength indicator KW - OA-Publikationsfonds2020 KW - RSSI Y1 - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20200213-40954 UR - https://www.mdpi.com/2079-9292/9/2/320 VL - 2020 IS - volume 9, issue 2, 320 PB - MDPI ER -