@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} } @article{OuaerHosseiniAmaretal., author = {Ouaer, Hocine and Hosseini, Amir Hossein and Amar, Menad Nait and Ben Seghier, Mohamed El Amine and Ghriga, Mohammed Abdelfetah and Nabipour, Narjes and Andersen, P{\aa}l {\O}steb{\o} and Mosavi, Amir and Shamshirband, Shahaboddin}, title = {Rigorous Connectionist Models to Predict Carbon Dioxide Solubility in Various Ionic Liquids}, series = {Applied Sciences}, volume = {2020}, journal = {Applied Sciences}, number = {Volume 10, Issue 1, 304}, publisher = {MDPI}, doi = {https://doi.org/10.3390/app10010304}, url = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20200107-40558}, pages = {18}, abstract = {Estimating the solubility of carbon dioxide in ionic liquids, using reliable models, is of paramount importance from both environmental and economic points of view. In this regard, the current research aims at evaluating the performance of two data-driven techniques, namely multilayer perceptron (MLP) and gene expression programming (GEP), for predicting the solubility of carbon dioxide (CO2) in ionic liquids (ILs) as the function of pressure, temperature, and four thermodynamical parameters of the ionic liquid. To develop the above techniques, 744 experimental data points derived from the literature including 13 ILs were used (80\% of the points for training and 20\% for validation). Two backpropagation-based methods, namely Levenberg-Marquardt (LM) and Bayesian Regularization (BR), were applied to optimize the MLP algorithm. Various statistical and graphical assessments were applied to check the credibility of the developed techniques. The results were then compared with those calculated using Peng-Robinson (PR) or Soave-Redlich-Kwong (SRK) equations of state (EoS). The highest coefficient of determination (R2 = 0.9965) and the lowest root mean square error (RMSE = 0.0116) were recorded for the MLP-LMA model on the full dataset (with a negligible difference to the MLP-BR model). The comparison of results from this model with the vastly applied thermodynamic equation of state models revealed slightly better performance, but the EoS approaches also performed well with R2 from 0.984 up to 0.996. Lastly, the newly established correlation based on the GEP model exhibited very satisfactory results with overall values of R2 = 0.9896 and RMSE = 0.0201.}, subject = {Maschinelles Lernen}, language = {en} } @article{StaubachMachacekSkowroneketal.2020, author = {Staubach, Patrick and Machacek, Jan and Skowronek, Josefine and Wichtmann, Torsten}, title = {Vibratory pile driving in water-saturated sand: Back-analysis of model tests using a hydro-mechanically coupled CEL method}, series = {Soils and Foundations}, volume = {2021}, journal = {Soils and Foundations}, number = {Volume 61, Issue 1}, publisher = {Elsevier, Science Direct}, address = {Amsterdam}, doi = {10.1016/j.sandf.2020.11.005}, url = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20210203-43571}, pages = {144 -- 159}, year = {2020}, abstract = {The development of a hydro-mechanically coupled Coupled-Eulerian-Lagrangian (CEL) method and its application to the back-analysisof vibratory pile driving model tests in water-saturated sand is presented. The predicted pile penetration using this approachis in good agreement with the results of the model tests as well as with fully Lagrangian simulations. In terms of pore water pressure, however, the results of the CEL simulation show a slightly worse accordance with the model tests compared to the Lagrangian simulation. Some shortcomings of the hydro-mechanically coupled CEL method in case of frictional contact problems and pore fluids with high bulk modulus are discussed. Lastly, the CEL method is applied to the simulation of vibratory driving of open-profile piles under partially drained conditions to study installation-induced changes in the soil state. It is concluded that the proposed method is capable of realistically reproducing the most important mechanisms in the soil during the driving process despite its addressed shortcomings.}, subject = {Plastische Deformation}, language = {en} } @article{WolfLondong, author = {Wolf, Mario and Londong, J{\"o}rg}, title = {Transformation der Siedlungswasserwirtschaft - Steuerungsmechanismen im Diskurs ressourcenorientierter Systemans{\"a}tze am Beispiel von Th{\"u}ringen}, series = {Raumforschung und Raumordnung}, volume = {2020}, journal = {Raumforschung und Raumordnung}, number = {Band 78, Heft 4}, publisher = {Sciendo}, doi = {10.2478/rara-2020-0012}, url = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20201022-42731}, pages = {397 -- 411}, abstract = {Neuartige Sanit{\"a}rsysteme zielen auf eine ressourcenorientierte Verwertung von Abwasser ab. Erreicht werden soll dies durch die separate Erfassung von Abwasserteilstr{\"o}men. In den Fach{\"o}ffentlichkeiten der Wasserwirtschaft und Raumplanung werden neuartige Sanit{\"a}rsysteme als ein geeigneter Ansatz f{\"u}r die zuk{\"u}nftige Sicherung der Abwasserentsorgung in l{\"a}ndlichen R{\"a}umen betrachtet. Die Praxistauglichkeit dieser Systeme wurde zwar in Forschungsprojekten nachgewiesen, bisher erschweren jedoch f{\"u}r Abwasserentsorger vielf{\"a}ltige Risiken die Einf{\"u}hrung einer ressourcenorientierten Abwasserbewirtschaftung. Ausgehend von einer Untersuchung der Kontexte bei der Umsetzung eines neuartigen Sanit{\"a}rsystems im l{\"a}ndlichen Raum Th{\"u}ringens wird in diesem Beitrag der Frage nachgegangen, wie auf Landesebene mit dem abwasserwirtschaftlichen Instrumentarium die Einf{\"u}hrung von ressourcenorientierten Systemans{\"a}tzen unterst{\"u}tzt werden kann. Zentrale Elemente des Beitrags sind die Darstellung der wesentlichen Transformationsrisiken in Bezug auf die Einf{\"u}hrung innovativer L{\"o}sungsans{\"a}tze, eine Erl{\"a}uterung der spezifischen abwasserwirtschaftlichen Instrumente sowie die Darlegung von Steuerungsans{\"a}tzen,mit denen die Einf{\"u}hrung von neuartigen Sanit{\"a}rsystemen gef{\"o}rdert werden kann. Im Ergebnis wird die Realisierbarkeit von neuartigen Sanit{\"a}rsystemen durch den strategischen Einsatz des Instrumentariums deutlich, gleichwohl die Wasserwirtschaft durch die Erweiterung der bisherigen Systemgrenzen auf die Kooperation mit anderen Bereichen der Daseinsvorsorge angewiesen ist.}, subject = {Raumordnung}, language = {de} } @article{HarirchianKumariJadhavetal., author = {Harirchian, Ehsan and Kumari, Vandana and Jadhav, Kirti and Raj Das, Rohan and Rasulzade, Shahla and Lahmer, Tom}, title = {A Machine Learning Framework for Assessing Seismic Hazard Safety of Reinforced Concrete Buildings}, series = {Applied Sciences}, volume = {2020}, journal = {Applied Sciences}, number = {Volume 10, issue 20, article 7153}, publisher = {MDPI}, address = {Basel}, doi = {10.3390/app10207153}, url = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20201022-42744}, pages = {18}, abstract = {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.}, subject = {Erdbeben}, language = {en} } @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{Haefner, author = {H{\"a}fner, Lukas}, title = {Common Ground. Kommentar zu Lisa Vollmer und Boris Michel „Wohnen in der Klimakrise. Die Wohnungsfrage als {\"o}kologische Frage"}, series = {sub\urban. zeitschrift f{\"u}r kritische stadtforschung}, volume = {2020}, journal = {sub\urban. zeitschrift f{\"u}r kritische stadtforschung}, number = {Band 8, Heft 1/2}, publisher = {Sub\urban e.V.}, address = {Leipzig}, doi = {10.36900/suburban.v8i1/2.565}, url = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20200507-41655}, pages = {177 -- 182}, abstract = {Die im Jahr 2020 in Deutschland praktizierte Siedlungs- und Wohnungspolitik erh{\"a}lt in Anbetracht ihrer Auswirkungen auf die soziale und {\"o}kologische Lage einen bitteren Beigeschmack. Arm und Reich triften weiter auseinander und einer zielgerichteten {\"o}kologischen Transformation der Art und Weise, wie Stadtentwicklung und Wohnungspolitik gestaltet werden,stehen noch immer historisch und systemisch bedingte Pfadabh{\"a}ngigkeiten im Weg. Diese werden nur durch eine integrierte Betrachtung sozialer und {\"o}konomischer Aspekte sichtbar und deuten auf eine der urspr{\"u}nglichen Fragen linker Gesellschaftsforschung hin: Die Auseinandersetzung mit dem Verh{\"a}ltnis von Eigentum und Gerechtigkeit. Im Ergebnis stehen drei wesentliche Befunde: Der Diskurs zum Schutz des Klimas und der Biodiversit{\"a}t ber{\"u}hrt direkt die Parameter Dichte, Nutzungsmischung und Fl{\"a}cheninanspruchnahme; zweitens steigt letztere relativ mit erh{\"o}htem, individuell verf{\"u}gbaren Kapital und insbesondere im selbstgenutztem Eigentum gegen{\"u}ber Mietwohnungen; und drittens w{\"a}chst der Eigentumsanteil mit fortschreitender Finanzialisierung des Wohnungsmarktes, sodass das Risiko sozialer und {\"o}kologischer Krisen sich versch{\"a}rft.}, subject = {Umweltgerechtigkeit}, language = {de} } @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} } @book{OPUS4-4106, title = {Postwachstumsstadt. Konturen einer solidarischen Stadtpolitik}, editor = {Brokow-Loga, Anton and Eckardt, Frank}, publisher = {oekom verlag}, address = {M{\"u}nchen}, isbn = {978-3-96238-696-2}, doi = {10.14512/9783962386962}, url = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20200311-41061}, publisher = {Bauhaus-Universit{\"a}t Weimar}, pages = {344}, abstract = {St{\"a}dte ohne Wachstum - eine bislang kaum vorstellbare Vision. Doch Klimawandel, Ressourcenverschwendung, wachsende soziale Ungleichheiten und viele andere Zukunftsgefahren stellen das bisherige Allheilmittel Wachstum grunds{\"a}tzlich infrage. Wie wollen wir heute und morgen zusammenleben? Wie gestalten wir ein gutes Leben f{\"u}r alle in der Stadt? W{\"a}hrend in einzelnen Nischen diese Fragen bereits ansatzweise beantwortet werden, fehlt es noch immer an umfassenden Entw{\"u}rfen und Transformationsans{\"a}tzen, die eine fundamental andere, solidarische Stadt konturieren. Diesen Versuch wagt das Projekt Postwachstumsstadt. In diesem Buch werden konzeptionelle und pragmatische Aspekte aus verschiedenen Bereichen der Stadtpolitik zusammengebracht, die neue Pfade aufzeigen und verkn{\"u}pfen. Die Beitr{\"a}ge diskutieren st{\"a}dtische Wachstumskrisen, transformative Planung und Konflikte um Gestaltungsmacht. Nicht zuletzt wird dabei auch die Frage nach der Rolle von Stadtutopien neu gestellt. Dadurch soll eine l{\"a}ngst f{\"a}llige Debatte dar{\"u}ber angestoßen werden, wie sich notwendige st{\"a}dtische Wenden durch eine sozial{\"o}kologische Neuorientierung vor Ort verwirklichen lassen.}, subject = {Architektur}, language = {de} } @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} }