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- OA-Publikationsfonds2020 (29) (remove)
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.
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.
Neuartige Sanitärsysteme zielen auf eine ressourcenorientierte Verwertung von Abwasser ab. Erreicht werden soll dies durch die separate Erfassung von Abwasserteilströmen. In den Fachöffentlichkeiten der Wasserwirtschaft und Raumplanung werden neuartige Sanitärsysteme als ein geeigneter Ansatz für die zukünftige
Sicherung der Abwasserentsorgung in ländlichen Räumen betrachtet. Die Praxistauglichkeit dieser Systeme wurde zwar in Forschungsprojekten nachgewiesen, bisher erschweren jedoch für Abwasserentsorger vielfältige Risiken die Einführung einer ressourcenorientierten Abwasserbewirtschaftung. Ausgehend von einer Untersuchung der Kontexte bei der Umsetzung eines neuartigen Sanitärsystems im ländlichen Raum Thüringens wird in diesem Beitrag der Frage nachgegangen, wie auf Landesebene mit dem abwasserwirtschaftlichen Instrumentarium die Einführung von ressourcenorientierten Systemansätzen unterstützt werden kann. Zentrale Elemente des Beitrags sind die Darstellung der wesentlichen Transformationsrisiken in Bezug auf die Einführung innovativer Lösungsansätze, eine Erläuterung der spezifischen abwasserwirtschaftlichen Instrumente sowie die Darlegung von Steuerungsansätzen,mit denen die Einführung von neuartigen Sanitärsystemen gefördert werden kann. Im Ergebnis wird die Realisierbarkeit von neuartigen Sanitärsystemen 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.
A Machine Learning Framework for Assessing Seismic Hazard Safety of Reinforced Concrete Buildings
(2020)
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.
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.
Die im Jahr 2020 in Deutschland praktizierte Siedlungs- und Wohnungspolitik erhält in Anbetracht ihrer Auswirkungen auf die soziale und ökologische Lage einen bitteren Beigeschmack. Arm und Reich triften weiter auseinander und einer zielgerichteten ökologischen Transformation der Art und Weise, wie Stadtentwicklung und Wohnungspolitik gestaltet werden,stehen noch immer historisch und systemisch bedingte Pfadabhängigkeiten im Weg. Diese werden nur durch eine integrierte Betrachtung sozialer und ökonomischer Aspekte sichtbar und deuten auf eine der ursprünglichen Fragen linker Gesellschaftsforschung hin: Die Auseinandersetzung mit dem Verhältnis von Eigentum und Gerechtigkeit.
Im Ergebnis stehen drei wesentliche Befunde: Der Diskurs zum Schutz des Klimas und der Biodiversität berührt direkt die Parameter Dichte, Nutzungsmischung und Flächeninanspruchnahme; zweitens steigt letztere relativ mit erhöhtem, individuell verfügbaren Kapital und insbesondere im selbstgenutztem Eigentum gegenüber Mietwohnungen; und drittens wächst der Eigentumsanteil mit fortschreitender Finanzialisierung des Wohnungsmarktes, sodass das Risiko sozialer und ökologischer Krisen sich verschärft.
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.
Städte ohne Wachstum - eine bislang kaum vorstellbare Vision. Doch Klimawandel, Ressourcenverschwendung, wachsende soziale Ungleichheiten und viele andere Zukunftsgefahren stellen das bisherige Allheilmittel Wachstum grundsätzlich infrage. Wie wollen wir heute und morgen zusammenleben? Wie gestalten wir ein gutes Leben für alle in der Stadt? Während in einzelnen Nischen diese Fragen bereits ansatzweise beantwortet werden, fehlt es noch immer an umfassenden Entwürfen und Transformationsansätzen, 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 verknüpfen. Die Beiträge diskutieren städtische Wachstumskrisen, transformative Planung und Konflikte um Gestaltungsmacht. Nicht zuletzt wird dabei auch die Frage nach der Rolle von Stadtutopien neu gestellt. Dadurch soll eine längst fällige Debatte darüber angestoßen werden, wie sich notwendige städtische Wenden durch eine sozialökologische Neuorientierung vor Ort verwirklichen lassen.
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.
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.