Flood susceptibility mapping using multi-temporal SAR imagery and novel integration of nature-inspired algorithms into support vector regression

Flood has long been known as one of the most catastrophic natural hazards worldwide. Mapping flood-prone areas is an important part of flood disaster management. In this study, a flood susceptibility mapping framework was developed based on a novel integration of nature-inspired algorithms into support vector regression (SVR). To this end,...

Inferring the number of floors of building footprints in the Netherlands

Data on the number of floors is required for a variety of applications, ranging from energy demand estimation to flood response plans. Despite this, open data on the number of floors is currently not available at a nationwide level in the Netherlands. This means that it must be inferred from other available data. Automatic methods usually...

Predicting Truck Cycle Time in Earthworks Using a Machine Learning Approach

Inaccurate truck cycle time (TCT) prediction in earthworks impacts construction projects because more equipment and human resources must be added to complete the project. It also increases fuel consumption and emissions from the machinery. However, the current method gives inaccurate prediction because of subjectivity and human error. <br/>This...

Proxying Bond Credit Spreads with Machine Learning

The bond market is affected by the shortage of liquidity problem, which means that many bonds are not frequently traded. This implies that market data for these bonds are missing. This lack of data represent a problem for financial risk measures such as Value at Risk (VaR). This research provides the framework for the construction of a proxy...

Height Inference for all USA Building Footprints in the Absence of Height Data

In recent years, the demand for 3D spatial information and 3D city models has increased, as they support and allow many different applications, e.g. noise simulations, energy demand estimations, and shadow analysis. Constructing a city model with 3D buildings requires elevation data (such as LiDAR or Digital Terrain Models), but unfortunately,...

New Approach to Multisite Downscaling of Precipitation by Identifying Different Set of Atmospheric Predictor Variables

Estimating reliable projections of precipitation considering climate change scenarios is important for hydrological studies. General circulation models provide future climate simulations at large scale in terms of large-scale atmospheric variables (LSAVs). Those LSAVs can be downscaled to finer special resolution using several downscaling...

Prediction of Soluble Al2O3 in Calcined Kaolin Using Infrared Spectroscopy and Multivariate Calibration

In the production of calcined kaolin, the soluble Al2O3 content is used as a quality control criterion for some speciality applications. The increasing need for automated quality control systems in the industry has brought the necessity of developing techniques that provide (near) real-time data. Based on the understanding that the presence of...

Comparison of Machine Learning Models for Hazardous Gas Dispersion Prediction in Field Cases

Dispersion prediction plays a significant role in the management and emergency response to hazardous gas emissions and accidental leaks. Compared with conventional atmospheric dispersion models, machine leaning (ML) models have both high accuracy and efficiency in terms of prediction, especially in field cases. However, selection of model...

Support Structure Optimization: On the use of load estimations for time efficient optimization of monopile support structures of offshore wind turbines

Over the years, the installed capacity of offshore wind turbines is increasing rapidly. However, the Levelized Costs Of Energy (LCOE) is still higher than the LCOE of traditional energy production methods like nuclear power or energy from coals or gas. This research focuses on a further decrease of the LCOE, by minimizing the mass of a monopile...