Blar i Institutt for økonomi og it på utgivelsesdato
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Improving Accuracy Estimation of Forest Aboveground Biomass Based on Incorporation of ALOS-2 PALSAR-2 and Sentinel-2A Imagery and Machine Learning: A Case Study of the Hyrcanian Forest Area (Iran)
(Journal article; Peer reviewed, 2018)The main objective of this research is to investigate the potential combination of Sentinel-2A and ALOS-2 PALSAR-2 (Advanced Land Observing Satellite -2 Phased Array type L-band Synthetic Aperture Radar-2) imagery for ... -
The riding trail as geotourism attraction: Evidence from Iceland
(Journal article; Peer reviewed, 2018)The geological aspects of tourism are much more extensive than just places to be viewed and/or experienced. The terrain traveled is also a geological phenomenon and an attraction in itself. For a hiker or a rider the type ... -
Novel Hybrid Swarm Optimized Multilayer Neural Network for Spatial Prediction of Flash Flood at Tropical Area Using Sentinel-1 SAR Imagery and Geospatial data
(Journal article; Peer reviewed, 2018)Flash floods are widely recognized as one of the most devastating natural hazards in the world, therefore prediction of flash flood-prone areas is crucial for public safety and emergency management. This research proposes ... -
Enhancing Prediction Performance of Landslide Susceptibility Model Using Hybrid Machine Learning Approach of Bagging Ensemble and Logistic Model Tree
(Journal article; Peer reviewed, 2018)The objective of this research is introduce a new machine learning ensemble approach that is a hybridization of Bagging ensemble (BE) and Logistic Model Trees (LMTree), named as BE-LMtree, for improving performance of ... -
Landslide Susceptibility Assessment at Mila basin (Algeria): A Comparative Assessment of Prediction Capability of Advanced Machine Learning Methods
(Journal article; Peer reviewed, 2018)Landslide risk prevention requires the delineation of landslide-prone areas as accurately as possible. Therefore, selecting a method or a technique that is capable of providing the highest landslide prediction capability ... -
Software Startup Engineering: A Systematic Mapping Study
(Journal article; Peer reviewed, 2018)Abstract Software startups have long been a significant driver in economic growth and innovation. The on-going failure of the major number of startups calls for a better understanding of state-of-the-practice of startup ... -
Evaluating GIS-Based Multiple Statistical Models and Data Mining for Earthquake and Rainfall-Induced Landslide Susceptibility Using the LiDAR DEM
(Peer reviewed; Journal article, 2019)Landslides are typically triggered by earthquakes or rainfall occasionally a rainfall event followed by an earthquake or vice versa. Yet, most of the works presented in the past decade have been largely focused at the ... -
Machine-Learning-Based Classification Approaches toward Recognizing Slope Stability Failure
(Peer reviewed; Journal article, 2019)In this paper, the authors investigated the applicability of combining machine-learning-based models toward slope stability assessment. To do this, several well-known machine-learning-based methods, namely multiple linear ... -
Spatial Modeling of Snow Avalanche Using Machine Learning Models and Geo-Environmental Factors: Comparison of Effectiveness in Two Mountain Regions
(Peer reviewed; Journal article, 2019)Although snow avalanches are among the most destructive natural disasters, and result in losses of life and economic damages in mountainous regions, far too little attention has been paid to the prediction of the snow ... -
Predicting Heating Load in Energy-Efficient Buildings Through Machine Learning Techniques
(Peer reviewed; Journal article, 2019)The heating load calculation is the first step of the iterative heating, ventilation, and air conditioning (HVAC) design procedure. In this study, we employed six machine learning techniques, namely multi-layer perceptron ... -
Prediction of Pullout Behavior of Belled Piles through Various Machine Learning Modelling Techniques
(Journal article; Peer reviewed, 2019)The main goal of this study is to estimate the pullout forces by developing various modelling technique like feedforward neural network (FFNN), radial basis functions neural networks (RBNN), general regression neural network ... -
Development of Two Novel Hybrid Prediction Models Estimating Ultimate Bearing Capacity of the Shallow Circular Footing
(Peer reviewed; Journal article, 2019)In the present work, we employed artificial neural network (ANN) that is optimized with two hybrid models, namely imperialist competition algorithm (ICA) as well as particle swarm optimization (PSO) in the case of the ... -
Do Software Firms Collaborate or Compete? A Model of Coopetition in Community-initiated OSS Projects
(Journal article; Peer reviewed, 2019)Background: An increasing number of commercial firms are participating in Open Source Software (OSS) projects to reduce their development cost and increase technical innovativeness. When collaborating with other firms whose ... -
A New Approach of Hybrid Bee Colony Optimized Neural Computing for Estimation of Soil Compression Coefficient for Housing Construction Project
(Peer reviewed; Journal article, 2019)In the design phase of housing projects, predicting the settlement of soil layers beneath the buildings requires the estimation of the coefficient of soil compression. This study proposes a low-cost, fast, and reliable ... -
Time-varying impact of snow depth on tourism in selected regions
(Peer reviewed; Journal article, 2019)This study uses a time-varying model that provides new evidence on the changing relationship between domestic overnight stays of selected winter sport destinations and natural snow conditions. A Kalman filter method combined ... -
Identifying Security Risks of Digital Transformation - An Engineering Perspective
(Chapter, 2019)Technological advancements continue to disrupt how organizations compete and create value in almost every industry and society. The recent digital transformation movement has expanded the reliance of companies and organizations ... -
Wildfire Probability Mapping: Bivariate vs. Multivariate Statistics
(Peer reviewed; Journal article, 2019)Wildfires are one of the most common natural hazards worldwide. Here, we compared the capability of bivariate and multivariate models for the prediction of spatially explicit wildfire probability across a fire-prone landscape ... -
Hybrid Machine Learning Approaches for Landslide Susceptibility Modeling
(Peer reviewed; Journal article, 2019)This paper presents novel hybrid machine learning models, namely Adaptive Neuro Fuzzy Inference System optimized by Particle Swarm Optimization (PSOANFIS), Artificial Neural Networks optimized by Particle Swarm Optimization ... -
The Feasibility of Three Prediction Techniques of the Artificial Neural Network, Adaptive Neuro-Fuzzy Inference System, and Hybrid Particle Swarm Optimization for Assessing the Safety Factor of Cohesive Slopes
(Journal article; Peer reviewed, 2019)In this paper, a neuro particle-based optimization of the artificial neural network (ANN) is investigated for slope stability calculation. The results are also compared to another artificial intelligence technique of a ... -
Remote sensing approaches for monitoring mangrove species, structure and biomass: opportunities and challenges
(Journal article; Peer reviewed, 2019)The mangrove ecosystem plays a vital role in the global carbon cycle, by reducing greenhouse gas emissions and mitigating the impacts of climate change. However, mangroves have been lost worldwide, resulting in substantial ...