Browsing NTNU Open by Title
Now showing items 72055-72074 of 100951
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Predicting Runoff for a Drinking Water Catchment in Southwest Greenland
(Master thesis, 2020)Efterhånden som smeltevandsafstrømningen fra Grønlands gletsjere og iskapper stiger, er der et øget behov for at kvantificere denne afstrømning, for at forbedre forståelsen og give et mere nøjagtigt estimat af bidraget til ... -
Predicting scour around offshore wind turbines using soft computing techniques - Comparing Genetic Programming with Existing Scour Prediction Methods.
(Master thesis, 2015)In this report a prediction method is developed for scour around monopiles. A soft computing technique called genetic programming (GP) is used to create a scour prediction formula that can compute scour in all offshore ... -
Predicting secondary surgery after operative fixation of olecranon fractures: a model using data from 800 patients
(Peer reviewed; Journal article, 2021)Background: High rates of secondary surgery after fixation of olecranon fractures have been reported. Identification of risk factors can aid surgeons to reduce complications leading to additional surgical procedures. ... -
Predicting Sheep Predation Events Through the Use of Machine Learning Models Trained on Tracking Necklace Data
(Master thesis, 2023)Hver sommer blir millioner av sauer sluppet på beite, og av disse blir tusener drept av rovdyr som leder til millioner av kroner i økonomiske tap. I det siste har det oppstått en trend med å utstyre sauene med halskjeder ... -
Predicting ship speeds in the Arctic using deep learning on historical AIS data
(Chapter, 2020)This study offers a perspective of applying fully connected neural networks (NN) to predict vessels’ speeds on a part of the Northern Sea Route and discusses its challenges. A fully connected neural network model was used ... -
Predicting Ship Turnaround Times with Machine Learning
(Master thesis, 2020)Målet med denne oppgaven er å undersøke hvorvidt maskinlæring ved hjelp av AIS, skips- og seilasdata kan brukes til å predikere hvor lang tid et skip vil bruke i havn. Undersøkelsen er gjort fra et havneperspektiv som betyr ... -
Predicting Shipping Freight Rate Movements Using Recurrent Neural Networks and AIS Data - On the tanker route between the Arabian Gulf and Singapore
(Master thesis, 2018)The purpose of this thesis is twofold. Firstly, we want to predict shipping freight rates in the liquid crude oil tanker market, applying a machine learning technique suitable for processing time-series (sequential) data, ... -
Predicting Silica Deposition from Superheated, Pressurized Steam Using Numerical Modeling of Nucleation, Agglomeration and Deposition
(Peer reviewed; Journal article, 2023)A model that can be used to quantify silica deposition from superheated depressurized steam is developed. Classical nucleation theory, agglomeration and deposition onto a wall are combined in a numerical model that calculates ... -
Predicting Snow Density
(Master thesis, 2013)Snow density is an important measure in hydrological applications. It is used to convert snow depth to the snow water equivalent (SWE). A model developed by Sturm et al. (2010) predicts the snow density by using snow depth, ... -
Predicting soil properties in the Canadian boreal forest with limited data: Comparison of spatial and non-spatial statistical approaches
(Journal article; Peer reviewed, 2017)Digital soil mapping (DSM) involves the use of georeferenced information and statistical models to map predictions and uncertainties related to soil properties. Many remote regions of the globe, such as boreal forest ... -
Predicting solar radiation using a parametric cloud model
(Chapter, 2020)In this paper, we evaluate a method to calculate hourly global solar radiation and improve the calculation of diffuse and vertical surface radiation on building facades by accounting for ground conditions based on publicly ... -
Predicting solvent degradation in absorption–based CO2 capture from industrial flue gases
(Peer reviewed; Journal article, 2023)This work aims to predict solvent degradation rates in absorption-based CO2 capture processes using a 30 wt% aqueous monoethanolamine (MEA) solvent. A degradation model for MEA is developed and used to predict solvent ... -
Predicting Stock Prices Using Technical Analysis and Machine Learning
(Master thesis, 2010)Historical stock prices are used to predict the direction of future stock prices. The developed stock price prediction model uses a novel two-layer reasoning approach that employs domain knowledge from technical analysis ... -
Predicting stock prices with Long Short-Term Memory based models using a combination of data sources
(Master thesis, 2020)Fokuset for denne oppgaven er prediksjon av aksjekurser ved hjelp av lett tilgjengelige informasjonskilder. De fire presenterte forskningsspørsmålene er relatert til å identifisere mønstre i de innsamlede dataene (1), ... -
Predicting stock prices with Long ShortTerm Memory based models using a combination of data sources
(Master thesis, 2020)Fokuset for denne oppgaven er prediksjon av aksjekurser ved hjelp av lett tilgjengelige informasjonskilder. De fire presenterte forskningsspørsmålene er relatert til å identifisere mønstre i de innsamlede dataene (1), ... -
Predicting stock returns using Google Trends
(Master thesis, 2020)Blant forskere som undersøker forholdet mellom Googles søkevolum og aksjeavkastning, finner noen at økt søkevolum spår høyere avkastning, mens andre trekker den motsatte konklusjonen. Vi undersøker dette forholdet ved å ... -
Predicting stock returns with Google searches: One size does not fit all
(Master thesis, 2019)Eksisterende litteratur har ikke funnet et entydig svar på om investoroppmerksomhet, i form av Google søkevolum, kan predikere aksjeavkastning. Tidligere forskning har sett på gjennomsnitseffekten på tvers av selskaper. ... -
Predicting stock returns with Google searches: One size does not fit all
(Master thesis, 2019)Eksisterende litteratur har ikke funnet et entydig svar på om investoroppmerksomhet, i form av Google søkevolum, kan predikere aksjeavkastning. Tidligere forskning har sett på gjennomsnitseffekten på tvers av selskaper. ... -
Predicting strain engineering strategies using iKS1317: a genome-scale metabolic model of Streptomyces coelicolor
(Journal article; Peer reviewed, 2018)Streptomyces coelicolor is a model organism for the Actinobacteria, a phylum known to produce an extensive range of different bioactive compounds that include antibiotics currently used in the clinic. Biosynthetic gene ... -
Predicting stress, strain and deformation fields in materials and structures with graph neural networks
(Journal article; Peer reviewed, 2022)