@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{MousaviSteinkeJuniorTeixeiraetal., author = {Mousavi, Seyed Nasrollah and Steinke J{\´u}nior, Renato and Teixeira, Eder Daniel and Bocchiola, Daniele and Nabipour, Narjes and Mosavi, Amir and Shamshirband, Shahaboddin}, title = {Predictive Modeling the Free Hydraulic Jumps Pressure through Advanced Statistical Methods}, series = {Mathematics}, volume = {2020}, journal = {Mathematics}, number = {Volume 8, Issue 3, 323}, publisher = {MDPI}, address = {Basel}, doi = {10.3390/math8030323}, url = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20200402-41140}, pages = {16}, abstract = {Pressure fluctuations beneath hydraulic jumps potentially endanger the stability of stilling basins. This paper deals with the mathematical modeling of the results of laboratory-scale experiments to estimate the extreme pressures. Experiments were carried out on a smooth stilling basin underneath free hydraulic jumps downstream of an Ogee spillway. From the probability distribution of measured instantaneous pressures, pressures with different probabilities could be determined. It was verified that maximum pressure fluctuations, and the negative pressures, are located at the positions near the spillway toe. Also, minimum pressure fluctuations are located at the downstream of hydraulic jumps. It was possible to assess the cumulative curves of pressure data related to the characteristic points along the basin, and different Froude numbers. To benchmark the results, the dimensionless forms of statistical parameters include mean pressures (P*m), the standard deviations of pressure fluctuations (σ*X), pressures with different non-exceedance probabilities (P*k\%), and the statistical coefficient of the probability distribution (Nk\%) were assessed. It was found that an existing method can be used to interpret the present data, and pressure distribution in similar conditions, by using a new second-order fractional relationships for σ*X, and Nk\%. The values of the Nk\% coefficient indicated a single mean value for each probability.}, 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{ShabaniSamadianfardSattarietal., author = {Shabani, Sevda and Samadianfard, Saeed and Sattari, Mohammad Taghi and Mosavi, Amir and Shamshirband, Shahaboddin and Kmet, Tibor and V{\´a}rkonyi-K{\´o}czy, Annam{\´a}ria R.}, title = {Modeling Pan Evaporation Using Gaussian Process Regression K-Nearest Neighbors Random Forest and Support Vector Machines; Comparative Analysis}, series = {Atmosphere}, volume = {2020}, journal = {Atmosphere}, number = {Volume 11, Issue 1, 66}, doi = {10.3390/atmos11010066}, url = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20200110-40561}, pages = {17}, abstract = {Evaporation is a very important process; it is one of the most critical factors in agricultural, hydrological, and meteorological studies. Due to the interactions of multiple climatic factors, evaporation is considered as a complex and nonlinear phenomenon to model. Thus, machine learning methods have gained popularity in this realm. In the present study, four machine learning methods of Gaussian Process Regression (GPR), K-Nearest Neighbors (KNN), Random Forest (RF) and Support Vector Regression (SVR) were used to predict the pan evaporation (PE). Meteorological data including PE, temperature (T), relative humidity (RH), wind speed (W), and sunny hours (S) collected from 2011 through 2017. The accuracy of the studied methods was determined using the statistical indices of Root Mean Squared Error (RMSE), correlation coefficient (R) and Mean Absolute Error (MAE). Furthermore, the Taylor charts utilized for evaluating the accuracy of the mentioned models. The results of this study showed that at Gonbad-e Kavus, Gorgan and Bandar Torkman stations, GPR with RMSE of 1.521 mm/day, 1.244 mm/day, and 1.254 mm/day, KNN with RMSE of 1.991 mm/day, 1.775 mm/day, and 1.577 mm/day, RF with RMSE of 1.614 mm/day, 1.337 mm/day, and 1.316 mm/day, and SVR with RMSE of 1.55 mm/day, 1.262 mm/day, and 1.275 mm/day had more appropriate performances in estimating PE values. It was found that GPR for Gonbad-e Kavus Station with input parameters of T, W and S and GPR for Gorgan and Bandar Torkmen stations with input parameters of T, RH, W and S had the most accurate predictions and were proposed for precise estimation of PE. The findings of the current study indicated that the PE values may be accurately estimated with few easily measured meteorological parameters.}, subject = {Maschinelles Lernen}, language = {en} } @article{SaqlaiGhaniKhanetal., author = {Saqlai, Syed Muhammad and Ghani, Anwar and Khan, Imran and Ahmed Khan Ghayyur, Shahbaz and Shamshirband, Shahaboddin and Nabipour, Narjes and Shokri, Manouchehr}, title = {Image Analysis Using Human Body Geometry and Size Proportion Science for Action Classification}, series = {Applied Sciences}, volume = {2020}, journal = {Applied Sciences}, number = {volume 10, issue 16, article 5453}, publisher = {MDPI}, address = {Basel}, doi = {10.3390/app10165453}, url = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20200904-42322}, pages = {24}, abstract = {Gestures are one of the basic modes of human communication and are usually used to represent different actions. Automatic recognition of these actions forms the basis for solving more complex problems like human behavior analysis, video surveillance, event detection, and sign language recognition, etc. Action recognition from images is a challenging task as the key information like temporal data, object trajectory, and optical flow are not available in still images. While measuring the size of different regions of the human body i.e., step size, arms span, length of the arm, forearm, and hand, etc., provides valuable clues for identification of the human actions. In this article, a framework for classification of the human actions is presented where humans are detected and localized through faster region-convolutional neural networks followed by morphological image processing techniques. Furthermore, geometric features from human blob are extracted and incorporated into the classification rules for the six human actions i.e., standing, walking, single-hand side wave, single-hand top wave, both hands side wave, and both hands top wave. The performance of the proposed technique has been evaluated using precision, recall, omission error, and commission error. The proposed technique has been comparatively analyzed in terms of overall accuracy with existing approaches showing that it performs well in contrast to its counterparts.}, subject = {Bildanalyse}, language = {en} } @article{NabipourMosaviBaghbanetal., author = {Nabipour, Narjes and Mosavi, Amir and Baghban, Alireza and Shamshirband, Shahaboddin and Felde, Imre}, title = {Extreme Learning Machine-Based Model for Solubility Estimation of Hydrocarbon Gases in Electrolyte Solutions}, series = {Processes}, volume = {2020}, journal = {Processes}, number = {Volume 8, Issue 1, 92}, publisher = {MDPI}, doi = {10.3390/pr8010092}, url = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20200113-40624}, pages = {12}, abstract = {Calculating hydrocarbon components solubility of natural gases is known as one of the important issues for operational works in petroleum and chemical engineering. In this work, a novel solubility estimation tool has been proposed for hydrocarbon gases—including methane, ethane, propane, and butane—in aqueous electrolyte solutions based on extreme learning machine (ELM) algorithm. Comparing the ELM outputs with a comprehensive real databank which has 1175 solubility points yielded R-squared values of 0.985 and 0.987 for training and testing phases respectively. Furthermore, the visual comparison of estimated and actual hydrocarbon solubility led to confirm the ability of proposed solubility model. Additionally, sensitivity analysis has been employed on the input variables of model to identify their impacts on hydrocarbon solubility. Such a comprehensive and reliable study can help engineers and scientists to successfully determine the important thermodynamic properties, which are key factors in optimizing and designing different industrial units such as refineries and petrochemical plants.}, subject = {Maschinelles Lernen}, language = {en} } @article{AhmadiBaghbanSadeghzadehetal., author = {Ahmadi, Mohammad Hossein and Baghban, Alireza and Sadeghzadeh, Milad and Zamen, Mohammad and Mosavi, Amir and Shamshirband, Shahaboddin and Kumar, Ravinder and Mohammadi-Khanaposhtani, Mohammad}, title = {Evaluation of electrical efficiency of photovoltaic thermal solar collector}, 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 = {10.1080/19942060.2020.1734094}, url = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20200304-41049}, pages = {545 -- 565}, abstract = {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.}, subject = {Fotovoltaik}, 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{AmirinasabShamshirbandChronopoulosetal., author = {Amirinasab, Mehdi and Shamshirband, Shahaboddin and Chronopoulos, Anthony Theodore and Mosavi, Amir and Nabipour, Narjes}, title = {Energy-Efficient Method for Wireless Sensor Networks Low-Power Radio Operation in Internet of Things}, series = {electronics}, volume = {2020}, journal = {electronics}, number = {volume 9, issue 2, 320}, publisher = {MDPI}, doi = {10.3390/electronics9020320}, url = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20200213-40954}, pages = {20}, abstract = {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).}, subject = {Internet der Dinge}, language = {en} }