@article{HarirchianJadhavMohammadetal.,
  author    = {Harirchian, Ehsan and Jadhav, Kirti and Mohammad, Kifaytullah and Aghakouchaki Hosseini, Seyed Ehsan and Lahmer, Tom},
  title     = {A Comparative Study of MCDM Methods Integrated with Rapid Visual Seismic Vulnerability Assessment of Existing RC Structures},
  series = {Applied Sciences},
  volume    = {2020},
  journal   = {Applied Sciences},
  number    = {Volume 10, issue 18, article 6411},
  publisher = {MDPI},
  address   = {Basel},
  doi       = {10.3390/app10186411},
  url       = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20200918-42360},
  pages     = {24},
  abstract  = {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.},
  subject      = {Erdbebensicherheit},
  language  = {en}
}
@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{KarimimoshaverHajivalieiShokrietal.,
  author    = {Karimimoshaver, Mehrdad and Hajivaliei, Hatameh and Shokri, Manouchehr and Khalesro, Shakila and Aram, Farshid and Shamshirband, Shahaboddin},
  title     = {A Model for Locating Tall Buildings through a Visual Analysis Approach},
  series = {Applied Sciences},
  volume    = {2020},
  journal   = {Applied Sciences},
  number    = {Volume 10, issue 17, article 6072},
  publisher = {MDPI},
  address   = {Basel},
  doi       = {10.3390/app10176072},
  url       = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20210122-43350},
  pages     = {1 -- 25},
  abstract  = {Tall buildings have become an integral part of cities despite all their pros and cons. Some current tall buildings have several problems because of their unsuitable location; the problems include increasing density, imposing traffic on urban thoroughfares, blocking view corridors, etc. Some of these buildings have destroyed desirable views of the city. In this research, different criteria have been chosen, such as environment, access, social-economic, land-use, and physical context. These criteria and sub-criteria are prioritized and weighted by the analytic network process (ANP) based on experts' opinions, using Super Decisions V2.8 software. On the other hand, layers corresponding to sub-criteria were made in ArcGIS 10.3 simultaneously, then via a weighted overlay (map algebra), a locating plan was created. In the next step seven hypothetical tall buildings (20 stories), in the best part of the locating plan, were considered to evaluate how much of theses hypothetical buildings would be visible (fuzzy visibility) from the street and open spaces throughout the city. These processes have been modeled by MATLAB software, and the final fuzzy visibility plan was created by ArcGIS. Fuzzy visibility results can help city managers and planners to choose which location is suitable for a tall building and how much visibility may be appropriate. The proposed model can locate tall buildings based on technical and visual criteria in the future development of the city and it can be widely used in any city as long as the criteria and weights are localized.},
  subject      = {Geb{\"a}ude},
  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{HarirchianLahmerKumarietal.,
  author    = {Harirchian, Ehsan and Lahmer, Tom and Kumari, Vandana and Jadhav, Kirti},
  title     = {Application of Support Vector Machine Modeling for the Rapid Seismic Hazard Safety Evaluation of Existing Buildings},
  series = {Energies},
  volume    = {2020},
  journal   = {Energies},
  number    = {volume 13, issue 13, 3340},
  publisher = {MDPI},
  address   = {Basel},
  doi       = {10.3390/en13133340},
  url       = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20200707-41915},
  pages     = {15},
  abstract  = {The economic losses from earthquakes tend to hit the national economy considerably; therefore, models that are capable of estimating the vulnerability and losses of future earthquakes are highly consequential for emergency planners with the purpose of risk mitigation. This demands a mass prioritization filtering of structures to identify vulnerable buildings for retrofitting purposes. The application of advanced structural analysis on each building to study the earthquake response is impractical due to complex calculations, long computational time, and exorbitant cost. This exhibits the need for a fast, reliable, and rapid method, commonly known as Rapid Visual Screening (RVS). The method serves as a preliminary screening platform, using an optimum number of seismic parameters of the structure and predefined output damage states. In this study, the efficacy of the Machine Learning (ML) application in damage prediction through a Support Vector Machine (SVM) model as the damage classification technique has been investigated. The developed model was trained and examined based on damage data from the 1999 D{\"u}zce Earthquake in Turkey, where the building's data consists of 22 performance modifiers that have been implemented with supervised machine learning.},
  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{BandJanizadehChandraPaletal.,
  author    = {Band, Shahab S. and Janizadeh, Saeid and Chandra Pal, Subodh and Chowdhuri, Indrajit and Siabi, Zhaleh and Norouzi, Akbar and Melesse, Assefa M. and Shokri, Manouchehr and Mosavi, Amir Hosein},
  title     = {Comparative Analysis of Artificial Intelligence Models for Accurate Estimation of Groundwater Nitrate Concentration},
  series = {Sensors},
  volume    = {2020},
  journal   = {Sensors},
  number    = {Volume 20, issue 20, article 5763},
  publisher = {MDPI},
  address   = {Basel},
  doi       = {10.3390/s20205763},
  url       = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20210122-43364},
  pages     = {1 -- 23},
  abstract  = {Prediction of the groundwater nitrate concentration is of utmost importance for pollution control and water resource management. This research aims to model the spatial groundwater nitrate concentration in the Marvdasht watershed, Iran, based on several artificial intelligence methods of support vector machine (SVM), Cubist, random forest (RF), and Bayesian artificial neural network (Baysia-ANN) machine learning models. For this purpose, 11 independent variables affecting groundwater nitrate changes include elevation, slope, plan curvature, profile curvature, rainfall, piezometric depth, distance from the river, distance from residential, Sodium (Na), Potassium (K), and topographic wetness index (TWI) in the study area were prepared. Nitrate levels were also measured in 67 wells and used as a dependent variable for modeling. Data were divided into two categories of training (70\%) and testing (30\%) for modeling. The evaluation criteria coefficient of determination (R2), mean absolute error (MAE), root mean square error (RMSE), and Nash-Sutcliffe efficiency (NSE) were used to evaluate the performance of the models used. The results of modeling the susceptibility of groundwater nitrate concentration showed that the RF (R2 = 0.89, RMSE = 4.24, NSE = 0.87) model is better than the other Cubist (R2 = 0.87, RMSE = 5.18, NSE = 0.81), SVM (R2 = 0.74, RMSE = 6.07, NSE = 0.74), Bayesian-ANN (R2 = 0.79, RMSE = 5.91, NSE = 0.75) models. The results of groundwater nitrate concentration zoning in the study area showed that the northern parts of the case study have the highest amount of nitrate, which is higher in these agricultural areas than in other areas. The most important cause of nitrate pollution in these areas is agriculture activities and the use of groundwater to irrigate these crops and the wells close to agricultural areas, which has led to the indiscriminate use of chemical fertilizers by irrigation or rainwater of these fertilizers is washed and penetrates groundwater and pollutes the aquifer.},
  subject      = {Grundwasser},
  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{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{BrokowLogaNessler,
  author    = {Brokow-Loga, Anton and Neßler, Miriam},
  title     = {Eine Frage der Fl{\"a}chengerechtigkeit! Kommentar zu Lisa Vollmer und Boris Michel „Wohnen in der Klimakrise. Die Wohnungsfrage als {\"o}kologische Frage"},
  series = {s u b \ u r b a n. zeitschrift f{\"u}r kritische stadtforschung},
  volume    = {2020},
  journal   = {s u b \ u r b a n. zeitschrift f{\"u}r kritische stadtforschung},
  number    = {Band 8, Heft 1/2},
  publisher = {Sub\urban e.V.},
  address   = {Leipzig},
  doi       = {10.36900/suburban.v8i1/2.572},
  url       = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20210122-43333},
  pages     = {183 -- 192},
  abstract  = {Die derzeitige Wohnungskrise hat eine sozial-{\"o}kologische Kernproblematik. Dabei ist die sozial ungerechte und {\"o}kologisch problematische Verteilung von Wohnfl{\"a}che meist unsichtbar und wird weder in wissenschaftlichen noch in aktivistischen Kontexten ausreichend als Frage der Fl{\"a}chengerechtigkeit problematisiert. Denn Wohnraum und Fl{\"a}che in einer Stadt sind keine endlos verf{\"u}gbaren G{\"u}ter: Wenn einige Menschen auf viel Raum leben, bleibt f{\"u}r andere Menschen weniger Fl{\"a}che {\"u}brig. Und die Menschen, die am wenigstens f{\"u}r eine Verknappung von Wohnraum verantwortlich sind, leiden am meisten darunter. Dieser Artikel arbeitet zun{\"a}chst den Begriff der Wohnfl{\"a}chengerechtigkeit heraus, wobei auf die Ungleichverteilung von Wohnfl{\"a}che und deren gesellschaftliche Implikationen unter derzeitigen Wohnungsverteilungsmechanismen Bezug genommen wird. Anschließend wird der Verbrauch von (Wohn-)Fl{\"a}che aus {\"o}kologischer Perspektive problematisiert. Der Artikel diskutiert scheinbare und transformationsorientierte L{\"o}sungs- und Handlungsans{\"a}tze. Abschließend fordert er in der kritischen Stadtforschung und in aktivistischen Kontexten eine st{\"a}rkere Debatte um eine Wohnfl{\"a}chengerechtigkeit, deren Verwirklichung gleichermaßen eine soziale wie {\"o}kologische Dimension hat.},
  subject      = {Wohnen},
  language  = {de}
}