Now showing items 27432-27451 of 49060

    • m-Banking Quality and Bank Reputation

      Mirjana Pejić Bach; Berislava Starešinić; Mislav Ante Omazić; Ana Aleksić; Sanja Seljan (MDPI AG, 2020-05-01)
      m-Banking is developed to support the clients in using various banking services, by using their mobile phones, thus allowing them to overcome the barriers in terms of time and location. Clients are increasingly using m-banking, so for some of them, this is the most used way of communication with the bank and doing banking transactions. Therefore, high-quality m-banking services significantly impact trust towards the bank, and it can influence bank reputation. Given the influence of m-banking, as well as the importance of its perceived quality, the paper aims to investigate the elements of m-banking quality, and to analyze the relation between m-banking quality and bank reputation. We investigate several dimensions of m-banking (safety, simplicity, and variety of m-banking services), and their impact on perceived m-banking quality. Besides, we examine the effect of perceived m-banking quality to bank reputation. For the analysis of these relationships, we use structural equation modeling, based on the survey results on a sample of clients of major banks in Croatia. Results of empirical research indicate that safety, simplicity, and a variety of m-banking services have a significant impact on the perceived m-banking quality, which, in turn, has a positive impact on the bank’s reputation.
    • M-Government Cooperation for Sustainable Development in China: A Transaction Cost and Resource-Based View

      Xuesong Li; Yunlong Ding; Yuxuan Li (MDPI AG, 2019-03-01)
      Mobile government (m-Government) is highly valued by many countries and governments worldwide for its important technical, economic, and political benefits. A development trend worthy of attention in China is that various public mobile services are provided through the cooperation between governments and Internet enterprises. The m-Government cooperation, as component of the public service system, has both a benefit safeguard function by mitigating transaction hazards and a value creation function by sharing advantageous resources. Previous studies have not explained both functions for m-Government cooperation. This study addresses this research gap. We establish a theoretical model by developing hypotheses from integrating model of Transaction Costs Theory (TCT) and Resource-based Theory (RBT). The OLS and Poisson regression method are used to test the proposed model by using cross-sectional data collected from 284 cities in China. Results show that strategy alliance, technology-specific knowhow, and financial security positively influence m-Government cooperation, asset specificity negatively influences the m-Government cooperation, and environmental certainty has no significant impact on m-Government cooperation. From the perspectives of technology, policy, and culture, the article puts forward suggestions on how to better promote m-Government cooperation in China, including promoting the government’s digital capabilities, improving the citizen’ privacy protection system and cultivating a public-private cooperative culture of mutual trust.
    • M-PESA and Financial Inclusion in Kenya: Of Paying Comes Saving?

      Leo Van Hove; Antoine Dubus (MDPI AG, 2019-01-01)
      Mobile financial services such as M-PESA in Kenya are said to promote inclusion. Yet only 7.6 per cent of the Kenyans in the 2013 Financial Inclusion Insights dataset have ever used an M-PESA account to save for a future purchase. This paper uses a novel, three-step probit analysis to identify the socio-demographic characteristics of, successively, respondents who do not have access to a SIM card, have access to a SIM but do not have an M-PESA account, and, finally, have an account but do not save on it. We find that those who are excluded in the early stages are predominantly poor, non-educated, and female. For the final stage, we find that those who are in a position to save on their phone—the phone owners, the better educated—are less likely to do so. These results go against the traditional optimistic discourse on mobile savings as a prime path to financial inclusion. As such, our findings corroborate qualitative research that indicates that Kenyans have other needs, and want their money to circulate and ‘work’.
    • Ma Kahana ka ‘Ike: Lessons for Community-Based Fisheries Management

      Monica Montgomery; Mehana Vaughan (MDPI AG, 2018-10-01)
      Indigenous and place-based communities worldwide have self-organized to develop effective local-level institutions to conserve biocultural diversity. How communities maintain and adapt these institutions over time offers lessons for fostering more balanced human–environment relationships—an increasingly critical need as centralized governance systems struggle to manage declining fisheries. In this study, we focus on one long-enduring case of local level fisheries management, in Kahana, on the most populated Hawaiian island of O‘ahu. We used a mixed-methods approach including in-depth interviews, archival research, and participation in community gatherings to understand how relationships with place and local governance have endured despite changes in land and sea tenure, and what lessons this case offers for other communities engaged in restoring local-level governance. We detail the changing role of konohiki (head fishermen) in modern times (1850–1965) when they were managing local fisheries, not just for local subsistence but for larger commercial harvests. We also highlight ways in which families are reclaiming their role as caretakers following decades of state mismanagement. Considerations for fisheries co-management emerging from this research include the importance of (1) understanding historical contexts for enhancing institutional fit, (2) enduring community leadership, (3) balancing rights and responsibilities, and (4) fostering community ability to manage coastal resources through both formal and informal processes.
    • Macadamia Husk Compost Improved Physical and Chemical Properties of a Sandy Loam Soil

      Dembe Maselesele; John B.O. Ogola; Romeo N. Murovhi (MDPI AG, 2021-06-01)
      Poor soil fertility caused mainly by low and declining soil organic carbon is one of the major constraints limiting crop productivity in tropical and subtropical regions of South Africa. We evaluated the effect of macadamia husk compost (MHC) on selected chemical and physical properties of a sandy loam soil in NE South Africa in two successive seasons. The treatments, laid out in randomised, complete block design and replicated four times, were: (i) zero control, (ii) inorganic fertilizer (100:60:60 NPK Kg ha<sup>−1</sup>), (iii) MHC at 15 t ha<sup>−1</sup>, and (iv) MHC at 30 t ha<sup>−1</sup>. Soil bulk density; water holding capacity; soil pH; electrical conductivity (EC); organic carbon; total N; and available P, K, Ca, Mg, Al, Zn, and Cu were determined at 0–15 cm soil depth. Macadamia husk compost application decreased bulk density and increased water holding capacity. MHC and inorganic fertilizer increased soil pH, organic carbon, total N, C:N ratio, available P, exchangeable cations, and micronutrients but the effect was more pronounced under MHC treatments in both seasons. The positive effect of MHC on soil physicochemical properties was associated with an increase in soil organic carbon due to MHC application; hence, MHC may offer a sustainable option of increasing soil productivity, particularly in areas characterised by low SOC.
    • Macau Squares: Discerning the Triadic Sign Model of Built-Heritage

      Mark Hansley Yang Chua (MDPI AG, 2021-06-01)
      Despite an objectivist vision by many heritage conservation bodies, the extant literature mostly dwells on the value of heritage as something subjective and arbitrary. Semiotically treating built-heritage as a Peircian triadic sign, instead of a dyadic sign, could reconcile this apparent dichotomy. Some squares of Macau]’s Historic Centre are taken as case study. Using a Coasian perspective, this paper argues how the meaning-delimiting consequences of a triadic semiotic framework allow for a lower transaction cost in valuation and eventually a more sustainable conservation. This has been confirmed by an expert decision in designating the relatively new squares as heritage protected areas.
    • Machine Learning Aided Design and Prediction of Environmentally Friendly Rubberised Concrete

      Xu Huang; Jiaqi Zhang; Jessada Sresakoolchai; Sakdirat Kaewunruen (MDPI AG, 2021-02-01)
      <b> </b>Not only can waste rubber enhance the properties of concrete (e.g., its dynamic damping and abrasion resistance capacity), its rational utilisation can also dramatically reduce environmental pollution and carbon footprint globally. This study is the world’s first to develop a novel machine learning-aided design and prediction of environmentally friendly concrete using waste rubber, which can drive sustainable development of infrastructure systems towards net-zero emission, which saves time and cost. In this study, artificial neuron networks (ANN) have been established to determine the design relationship between various concrete mix composites and their multiple mechanical properties simultaneously. Interestingly, it is found that almost all previous studies on the ANNs could only predict one kind of mechanical property. To enable multiple mechanical property predictions, ANN models with various architectural algorithms, hidden neurons and layers are built and tailored for benchmarking in this study. Comprehensively, all three hundred and fifty-three experimental data sets of rubberised concrete available in the open literature have been collected. In this study, the mechanical properties in focus consist of the compressive strength at day 7 (CS7), the compressive strength at day 28 (CS28), the flexural strength (FS), the tensile strength (TS) and the elastic modulus (EM). The optimal ANN architecture has been identified by customising and benchmarking the algorithms (Levenberg–Marquardt (LM), Bayesian Regularisation (BR) and Scaled Conjugate Gradient (SCG)), hidden layers (1–2) and hidden neurons (1–30). The performance of the optimal ANN architecture has been assessed by employing the mean squared error (MSE) and the coefficient of determination (R2). In addition, the prediction accuracy of the optimal ANN model has ben compared with that of the multiple linear regression (MLR).
    • Machine Learning and Algorithmic Pair Trading in Futures Markets

      Seungho Baek; Mina Glambosky; Seok Hee Oh; Jeong Lee (MDPI AG, 2020-08-01)
      This<b> </b>study applies machine learning methods to develop a sustainable pairs trading market-neutral investment strategy across multiple futures markets. Cointegrated pairs with similar price trends are identified, and a hedge ratio is determined using an Error Correction Model (ECM) framework and support vector machine algorithm based upon the two-step Engle–Granger method. The study shows that normal backwardation and contango do not consistently characterize futures markets, and an algorithmic pairs trading strategy is effective, given the unique predominant price trends of each futures market. Across multiple futures markets, the pairs trading strategy results in larger risk-adjusted returns and lower exposure to market risk, relative to an appropriate benchmark. Backtesting is employed and results show that the pairs trading strategy may hedge against unexpected negative systemic events, specifically the COVID-19 pandemic, remaining profitable over the period examined.
    • Machine Learning and Deep Learning in Energy Systems: A Review

      Mohammad Mahdi Forootan; Iman Larki; Rahim Zahedi; Abolfazl Ahmadi (MDPI AG, 2022-04-01)
      With population increases and a vital need for energy, energy systems play an important and decisive role in all of the sectors of society. To accelerate the process and improve the methods of responding to this increase in energy demand, the use of models and algorithms based on artificial intelligence has become common and mandatory. In the present study, a comprehensive and detailed study has been conducted on the methods and applications of Machine Learning (ML) and Deep Learning (DL), which are the newest and most practical models based on Artificial Intelligence (AI) for use in energy systems. It should be noted that due to the development of DL algorithms, which are usually more accurate and less error, the use of these algorithms increases the ability of the model to solve complex problems in this field. In this article, we have tried to examine DL algorithms that are very powerful in problem solving but have received less attention in other studies, such as RNN, ANFIS, RBN, DBN, WNN, and so on. This research uses knowledge discovery in research databases to understand ML and DL applications in energy systems’ current status and future. Subsequently, the critical areas and research gaps are identified. In addition, this study covers the most common and efficient applications used in this field; optimization, forecasting, fault detection, and other applications of energy systems are investigated. Attempts have also been made to cover most of the algorithms and their evaluation metrics, including not only algorithms that are more important, but also newer ones that have received less attention.
    • Machine Learning Application in Reservoir Water Level Forecasting for Sustainable Hydropower Generation Strategy

      Michelle Sapitang; Wanie M. Ridwan; Khairul Faizal Kushiar; Ali Najah Ahmed; Ahmed El-Shafie (MDPI AG, 2020-07-01)
      The aim of this study is to accurately forecast the changes in water level of a reservoir located in Malaysia with two different scenarios; Scenario 1 (SC1) includes rainfall and water level as input and Scenario 2 (SC2) includes rainfall, water level, and sent out. Different time horizons (one day ahead to seven days) will be investigated to check the accuracy of the proposed models. In this study, four supervised machine learning algorithms for both scenarios were proposed such as Boosted Decision Tree Regression (BDTR), Decision Forest Regression (DFR), Bayesian Linear Regression (BLR) and Neural Network Regression (NNR). Eighty percent of the total data were used for training the datasets while 20% for the dataset used for testing. The models’ performance is evaluated using five statistical indexes; the Correlation Coefficient (R<sup>2</sup>), Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Relative Absolute Error (RAE), and Relative Squared Error (RSE). The findings showed that among the four proposed models, the BLR model outperformed other models with R<sup>2</sup> 0.998952 (1-day ahead) for SC1 and BDTR for SC2 with R<sup>2</sup> 0.99992 (1-day ahead). With regards to the uncertainty analysis, 95PPU and d-factors were adopted to measure the uncertainties of the best models (BLR and BDTR). The results showed the value of 95PPU for both models in both scenarios (SC1 and SC2) fall into the range between 80% to 100%. As for the d-factor, all values in SC1 and SC2 fall below one.
    • Machine Learning Application to Eco-Friendly Concrete Design for Decarbonisation

      Abigail Lavercombe; Xu Huang; Sakdirat Kaewunruen (MDPI AG, 2021-12-01)
      Cement replacement materials can not only benefit the workability of the concrete but can also improve its compressive strength. Reducing the cement content of concrete can also lower CO<sub>2</sub> emissions to mitigate the impact of the construction industry on the environment and improve energy consumption. This paper aims to predict the compressive strength (CS) and embodied carbon (EC) of cement replacement concrete using machine learning (ML) algorithms, i.e., deep neural network (DNN), support vector regression (SVR), gradient boosting regression (GBR), random forest (RF), k-nearest neighbors (kNN), and decision tree regression (DTR). Not only is producing an optimal ML model helpful for predicting accurate results, but it also saves time, energy, and costs, compared to conducting experiments. Firstly, 367 pieces of experimental datasets from the open literature were collected, in which cement was replaced with any of the cementitious materials. Secondly, the datasets were imported into the ML models, whose parameters were tuned by the grid search algorithm (GSA). Then, the prediction performance, the coefficient of determination (R<sup>2</sup>), the prediction accuracy, and the root mean square error (RMSE) were employed to indicate the prediction ability of the ML models. The results demonstrate that the GBR models perform the best prediction of the CS and EC. The R<sup>2</sup> of the GBR models for predicting the CS and EC are 0.946 and 0.999, respectively. Thus, it can be concluded that the GBR models have promising abilities for design assistance in cement replacement concrete. Finally, a sensitivity analysis (SA) was conducted in this paper to analyse the effects of the inputs on the CS and EC of the cement replacement concrete. Pulverised fuel ash (PFA), blast-furnace slag (GGBS), Expanded perlite (EP), and Silica fume (SF) were noticed to affect the CS and EC of cement replacement concrete significantly.
    • Machine Learning Approach Using MLP and SVM Algorithms for the Fault Prediction of a Centrifugal Pump in the Oil and Gas Industry

      Pier Francesco Orrù; Andrea Zoccheddu; Lorenzo Sassu; Carmine Mattia; Riccardo Cozza; Simone Arena (MDPI AG, 2020-06-01)
      The demand for cost-effective, reliable and safe machinery operation requires accurate fault detection and classification to achieve an efficient maintenance strategy and increase performance. Furthermore, in strategic sectors such as the oil and gas industry, fault prediction plays a key role to extend component lifetime and reduce unplanned equipment thus preventing costly breakdowns and plant shutdowns. This paper presents the preliminary development of a simple and easy to implement machine learning (ML) model for early fault prediction of a centrifugal pump in the oil and gas industry. The data analysis is based on real-life historical data from process and equipment sensors mounted on the selected machinery. The raw sensor data, mainly from temperature, pressure and vibrations probes, are denoised, pre-processed and successively coded to train the model. To validate the learning capabilities of the ML model, two different algorithms—the Support Vector Machine (SVM) and the Multilayer Perceptron (MLP)—are implemented in KNIME platform. Based on these algorithms, potential faults are successfully recognized and classified ensuring good prediction accuracy. Indeed, results from this preliminary work show that the model allows us to properly detect the trends of system deviations from normal operation behavior and generate fault prediction alerts as a maintenance decision support system for operatives, aiming at avoiding possible incoming failures.
    • Machine Learning Based Short-Term Travel Time Prediction: Numerical Results and Comparative Analyses

      Bo Qiu; Wei (David) Fan (MDPI AG, 2021-07-01)
      Due to the increasing traffic volume in metropolitan areas, short-term travel time prediction (TTP) can be an important and useful tool for both travelers and traffic management. Accurate and reliable short-term travel time prediction can greatly help vehicle routing and congestion mitigation. One of the most challenging tasks in TTP is developing and selecting the most appropriate prediction algorithm using the available data. In this study, the travel time data was provided and collected from the Regional Integrated Transportation Information System (RITIS). Then, the travel times were predicted for short horizons (ranging from 15 to 60 min) on the selected freeway corridors by applying four different machine learning algorithms, which are Decision Trees (DT), Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Long Short-Term Memory neural network (LSTM). Many spatial and temporal characteristics that may affect travel time were used when developing the models. The performance of prediction accuracy and reliability are compared. Numerical results suggest that RF can achieve a better prediction performance result than any of the other methods not only in accuracy but also with stability.
    • Machine Learning Based Vehicle to Grid Strategy for Improving the Energy Performance of Public Buildings

      Connor Scott; Mominul Ahsan; Alhussein Albarbar (MDPI AG, 2021-04-01)
      Carbon neutral buildings are dependent on effective energy management systems and harvesting energy from unpredictable renewable sources. One strategy is to utilise the capacity from electric vehicles, while renewables are not available according to demand. Vehicle to grid (V2G) technology can only be expanded if there is funding and realisation that it works, so investment must be in place first, with charging stations and with the electric vehicles to begin with. The installer of the charging stations will achieve the financial benefit or have an incentive and vice versa for the owners of the electric vehicles. The paper presents an effective V2G strategy that was developed and implemented for an operational university campus. A machine learning algorithm has also been derived to predict energy consumption and energy costs for the investigated building. The accuracy of the developed algorithm in predicting energy consumption was found to be between 94% and 96%, with an average of less than 5% error in costs predictions. The achieved results show that energy consumption savings are in the range of 35%, with the potentials to achieve about 65% if the strategy was applied at all times. This has demonstrated the effectiveness of the machine learning algorithm in carbon print reductions.
    • Machine Learning for Conservation Planning in a Changing Climate

      Ana Cristina Mosebo Fernandes; Rebeca Quintero Gonzalez; Marie Ann Lenihan-Clarke; Ezra Francis Leslie Trotter; Jamal Jokar Arsanjani (MDPI AG, 2020-09-01)
      Wildlife species’ habitats throughout North America are subject to direct and indirect consequences of climate change. Vulnerability assessments for the Intermountain West regard wildlife and vegetation and their disturbance as two key resource areas in terms of ecosystems when considering climate change issues. Despite the adaptability potential of certain wildlife, increased temperature estimates of 1.67–2 °C by 2050 increase the likelihood and severity of droughts, floods, heatwaves and wildfires in Utah. As a consequence, resilient flora and fauna could be displaced. The aim of this study was to locate areas of habitat for an exemplary species, i.e., sage-grouse, based on current climate conditions and pinpoint areas of future habitat based on climate projections. The locations of wildlife were collected from Volunteered Geographic Information (VGI) observations in addition to normal temperature and precipitation, vegetation cover and other ecosystem-related data. Four machine learning algorithms were then used to locate the current sites of wildlife habitats and predict suitable future sites where wildlife would likely relocate to, dependent on the effects of climate change and based on a timeframe of scientifically backed temperature-increase estimates. Our findings show that Random Forest outperforms other competing models, with an accuracy of 0.897, and a sensitivity and specificity of 0.917 and 0.885, respectively, and has great potential in Species Distribution Modeling (SDM), which can provide useful insights into habitat predictions. Based on this model, our predictions show that sage-grouse habitats in Utah will continue to decrease over the coming years due to climate change, producing a highly fragmented habitat and causing a loss of close to 70% of their current habitat. Priority Areas of Conservation (PACs) and protected areas might be deemed insufficient to halt this habitat loss, and more effort should be put into maintaining connectivity between patches to ensure the movement and genetic diversity within the sage-grouse population. The underlying data-driven methodical approach of this study could be useful for environmentalists, researchers, decision-makers, and policymakers, among others.
    • Machine Learning for Determining Interactions between Air Pollutants and Environmental Parameters in Three Cities of Iran

      Abdullah Kaviani Rad; Redmond R. Shamshiri; Armin Naghipour; Seraj-Odeen Razmi; Mohsen Shariati; Foroogh Golkar; Siva K. Balasundram (MDPI AG, 2022-06-01)
      Air pollution, as one of the most significant environmental challenges, has adversely affected the global economy, human health, and ecosystems. Consequently, comprehensive research is being conducted to provide solutions to air quality management. Recently, it has been demonstrated that environmental parameters, including temperature, relative humidity, wind speed, air pressure, and vegetation, interact with air pollutants, such as particulate matter (PM), NO2, SO2, O3, and CO, contributing to frameworks for forecasting air quality. The objective of the present study is to explore these interactions in three Iranian metropolises of Tehran, Tabriz, and Shiraz from 2015 to 2019 and develop a machine learning-based model to predict daily air pollution. Three distinct assessment criteria were used to assess the proposed XGBoost model, including R squared (R2), Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE). Preliminary results showed that although air pollutants were significantly associated with meteorological factors and vegetation, the formulated model had low accuracy in predicting (R2PM2.5 = 0.36, R2PM10 = 0.27, R2NO2 = 0.46, R2SO2 = 0.41, R2O3 = 0.52, and R2CO = 0.38). Accordingly, future studies should consider more variables, including emission data from manufactories and traffic, as well as sunlight and wind direction. It is also suggested that strategies be applied to minimize the lack of observational data by considering second-and third-order interactions between parameters, increasing the number of simultaneous air pollution and meteorological monitoring stations, as well as hybrid machine learning models based on proximal and satellite data.
    • Machine Learning for Optimization of Energy and Plastic Consumption in the Production of Thermoplastic Parts in SME

      Martina Willenbacher; Jonas Scholten; Volker Wohlgemuth (MDPI AG, 2021-06-01)
      In manufacturing companies, especially in SMEs, the optimization of processes in terms of resource consumption, waste minimization, and pollutant emissions is becoming increasingly important. Another important driver is digitalization and the associated increase in the volume of data. These data, from a multitude of devices and systems, offer enormous potential, which increases the need for intelligent, dynamic analysis models even in smaller companies. This article presents the results of an investigation into whether and to what extent machine learning processes can contribute to optimizing energy consumption and reducing incorrectly produced plastic parts in plastic processing SMEs. For this purpose, the machine data were recorded in a plastics-producing company for the automotive industry and analyzed with regard to the material and energy flows. Machine learning methods were used to train these data in order to uncover optimization potential. Another problem that was addressed in the project was the analysis of manufacturing processes characterized by strong non-linearities and time-invariant behavior with Big Data methods and self-learning controls. Machine learning is suitable for this if sufficient training data are available. Due to the high material throughput in the production of the SMEs’ plastic parts, these requirements for the development of suitable learning methods were met. In response to the increasing importance of current information technologies in industrial production processes, the project aimed to use these technologies for sustainable digitalization in order to reduce the industry’s environmental impact and increase efficiency.
    • Machine Learning for Pan Evaporation Modeling in Different Agroclimatic Zones of the Slovak Republic (Macro-Regions)

      Beáta Novotná; Ľuboš Jurík; Ján Čimo; Jozef Palkovič; Branislav Chvíla; Vladimír Kišš (MDPI AG, 2022-03-01)
      Global climate change is likely to influence evapotranspiration (ET); as a result, many ET calculation methods may not give accurate results under different climatic conditions. The main objective of this study is to verify the suitability of machine learning (ML) models as calculation methods for pan evaporation modeling on the macro-regional scale. The most significant PE changes in the different agroclimatic zones of the Slovak Republic were compared, and their considerable impacts were analyzed. On the basis of the agroclimatic zones, 35 meteorological stations distributed across Slovakia were classified into six macro-regions. For each of the meteorological stations, 11 variables were applied during the vegetation period in the years from 2010 to 2020 with a daily time step. The performance of eight different ML models—the neural network (NN) model, the autoneural network (AN) model, the decision tree (DT) model, the Dmine regression (DR) model, the DM neural network (DM NN) model, the gradient boosting (GB) model, the least angle regression (LARS) model, and the ensemble model (EM)—was employed to predict PE. It was found that the different models had diverse prediction accuracies in various geographical locations. In this study, the results of the values predicted by the individual models are compared.
    • Machine Learning for the Estimation of Diameter Increment in Mixed and Uneven-Aged Forests

      Abotaleb Salehnasab; Mahmoud Bayat; Manouchehr Namiranian; Bagher Khaleghi; Mahmoud Omid; Hafiz Umair Masood Awan; Nadir Al-Ansari; Abolfazl Jaafari (MDPI AG, 2022-03-01)
      Estimating the diameter increment of forests is one of the most important relationships in forest management and planning. The aim of this study was to provide insight into the application of two machine learning methods, i.e., the multilayer perceptron artificial neural network (MLP) and adaptive neuro-fuzzy inference system (ANFIS), for developing diameter increment models for the Hyrcanian forests. For this purpose, the diameters at breast height (DBH) of seven tree species were recorded during two inventory periods. The trees were divided into four broad species groups, including beech (Fagus orientalis), chestnut-leaved oak (Quercus castaneifolia), hornbeam (Carpinus betulus), and other species. For each group, a separate model was developed. The k-fold strategy was used to evaluate these models. The Pearson correlation coefficient (r), coefficient of determination (R2), root mean square error (RMSE), Akaike information criterion (AIC), and Bayesian information criterion (BIC) were utilized to evaluate the models. RMSE and R2 of the MLP and ANFIS models were estimated for the four groups of beech ((1.61 and 0.23) and (1.57 and 0.26)), hornbeam ((1.42 and 0.13) and (1.49 and 0.10)), chestnut-leaved oak ((1.55 and 0.28) and (1.47 and 0.39)), and other species ((1.44 and 0.32) and (1.5 and 0.24)), respectively. Despite the low coefficient of determination, the correlation test in both techniques was significant at a 0.01 level for all four groups. In this study, we also determined optimal network parameters such as number of nodes of one or multiple hidden layers and the type of membership functions for modeling the diameter increment in the Hyrcanian forests. Comparison of the results of the two techniques showed that for the groups of beech and chestnut-leaved oak, the ANFIS technique performed better and that the modeling techniques have a deep relationship with the nature of the tree species.
    • Machine Learning for the Improvement of Deep Renovation Building Projects Using As-Built BIM Models

      Sofía Mulero-Palencia; Sonia Álvarez-Díaz; Manuel Andrés-Chicote (MDPI AG, 2021-06-01)
      In recent years, new technologies, such as Artificial Intelligence, are emerging to improve decision making based on learning. Their use applied to the Architectural, Engineering and Construction (AEC) sector, together with the increased use of Building Information Modeling (BIM) methodology in all phases of a building’s life cycle, is opening up a wide range of opportunities in the sector. At the same time, the need to reduce CO<inline-formula><math xmlns="" display="inline"><semantics><msub><mrow></mrow><mn>2</mn></msub></semantics></math></inline-formula> emissions in cities is focusing on the energy renovation of existing buildings, thus tackling one of the main causes of these emissions. This paper shows the potentials, constraints and viable solutions of the use of Machine Learning/Artificial Intelligence approaches at the design stage of deep renovation building projects using As-Built BIM models as input to improve the decision-making process towards the uptake of energy efficiency measures. First, existing databases on buildings pathologies have been studied. Second, a Machine Learning based algorithm has been designed as a prototype diagnosis tool. It determines the critical areas to be solved through deep renovation projects by analysing BIM data according to the Industry Foundation Classes (IFC4) standard and proposing the most convenient renovation alternative (based on a catalogue of Energy Conservation Measures). Finally, the proposed diagnosis tool has been applied to a reference test building for different locations. The comparison shows how significant differences appear in the results depending on the situation of the building and the regulatory requirements to which it must be subjected.