Groundwater is a vital non-renewable resource that is being over exploited at an alarming rate. In Saudi Arabia, the majority of groundwater is used for agricultural activities. As such, the mapping of irrigated lands is a crucial step for managing available water resources. Even though traditional in-field mapping is effective, it is expensive, physically demanding, and spatially restricted. The use of remote sensing combined with advanced computational approaches provide a potential solution to this scale problem. However, when attempted at large scales, traditional computing tends to have significant processing and storage limitations. To address the scalability challenge, this project explores open-source cloud-based resources to map and quantify center-pivot irrigation fields on a national scale. This is achieved by first applying a land cover classification using Random Forest which is a machine learning approach, and then implementing a circle detection algorithm. While the analysis represents a preliminary exploration of these emerging cloud-based techniques, there is clear potential for broad application to many problems in the Earth and environmental sciences.

Discovering the crucial factors that contribute to goal success in sports analytics, this thesis aimsto utilize Random Forest classification to predict the outcome of shots and pre-shot events in powerplay situations. Through three experiments, the study evaluated the use of shots, shots with pre-shotevents, and shots with pre-shot events over sections. The first experiment used only shots, while thesecond experiment focused on shots with pre-shot events, where both compared it with shots over anexpected goal value of 0.08 or higher. The third experiment examined shots with pre-shot events acrossdifferent sections. Our findings demonstrated that the models in our experiments achieved accuracyscores ranging from 78% to 96% and F1 scores between 0% and 24%. Notably, the models in experiment3 demonstrated lower recall scores. The feature importance analysis revealed that pre-shotevents played a significant role in the predictive models of the second and third experiments, indicatingtheir substantial impact on the outcomes. A noteworthy conclusion arising from the discussion isthe recommendation for future research to conduct a more comprehensive exploration into the impactof pre-shot events, given their demonstrated significance in predicting goals. Such an investigation isdeemed necessary and justified.

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Monitoring carbon stock has emerged as a critical environmental problem among several worldwide organizations and collaborations in the context of global warming and climate change. This study seeks to provide a remote sensing solution based on three types of data, to explore the feasibility and reliability of estimating aboveground biomass (AGB) in order to improve the efficiency of monitoring carbon stock. The study attempted to investigate the potential of using Google Earth Engine (GEE), and the combinations of different datasets from Sentinel-1 (SAR), Sentinel-2 multispectral imagery, and LiDAR data to estimate AGB, by using the random forest algorithm (RF). Two models were proposed: the first one (Model 1) detected the AGB temporal changes from 2016 to 2021 in Southern Sweden; while the second one (Model 2) focused on Hultsfred municipality and studied the influence of different variables including the canopy height. Besides, six experimental groups of variables were tested to determine the performance of using different types of remote sensing data. We validated these two models with the observed AGB, and the findings showed that the combination of SAR polarization, multisprectral bands, vegetation indices able to estimate AGB for Model 1. In addition, Model 2 showed that further using the canopy height data can further improve the estimation.  We also found out that the spectral bands from Sentinel-2 contributed the most to AGB estimation for Model 1 in terms of: bands B3 (Green), B4 (Red), B5 (Red edge), B11 (SWIR), B12 (SWIR); and, vegetation indices of RVI, DVI, and EVI. On the other hand, for Model 2, B1(Ultra blue), B4 (Red), EVI, SAVI, and the canopy height are the most crucial variables for estimating AGB. Besides, the radar backscatter values using VV and VH modes from Sentienl-1 were both important for Models 1 and 2. For Model 1, the experimental group with the best accuracy was the group that used all variable combinations from Sentinel-1 and 2, and its   was 0.33~0.74. For Model 2, the group that used all the variables, in addition to the canopy height performed the best, where its   is 0.91. These therefore showed the benefit of integrating different remote sensing data sources.  In conclusion, this study showed the potential of using RF and GEE to estimate AGB in Southern Sweden. Furthermore, this study also shows the possibility of handling large dataset for a large scale area, at the resolution of 10 m, and producing time series AGB maps from 2016 to 2021. This can help enhance our understanding of AGB temporal changes and carbon stock detection in Southern Sweden, that can provide valuable insights for forest management and carbon monitoring.

The agricultural industry is constantly evolving with automation as one of the current main focuses. This thesis involves the automation of a seedbed tine harrow, specifically the control of the tillage depth. The tillage depth is instrumental to farming as it determines the quality of the tilth, how well clods are broken up, and how well the soil aggregates are sorted. Poor control of the tillage depth could result in a bad harvest for the farmer. To control the tillage depth, several pulse radar sensors are installed on the harrow. The sensors measure the distance from the tines of the harrow to the ground. This distance is used in a control-loop that controls the hydraulic actuators that lifts and pushes down the frame of the harrow. Because of the rough working conditions of the tine harrow, the pulse radar sensors are in danger of being damaged or disturbed. A sensor not working as intended will lead to poor control of the tillage depth or even an unstable control system. The purpose of this thesis is to develop diagnosis systems to detect and generate an alarm if the output of a sensor is faulty. Four different systems are developed, three machine learning approaches and one model based approach. To be able to test and train models without having to go out on a field with a real harrow, a test rig is available. The test rig emulates a harrow driving on a field and the tests are designed to imitate plausible sensor errors. The models trained on and tuned to the test rig data are validated with data gathered from a real tine harrow.  The validation data from the harrow reveal that the main difference between the field data and test rig data are the vibrations and the sensor heights. The test rig produces negligible amounts of vibrations whereas the vibrations on a real harrow are immense. These differences affect the performances of the models and some tuning have to be done to the models to accommodate for the vibrations. The performance of the model based approach is good and no larger adjustments have to be made to it. The machine learning models created from the test rig data do not work in the field and new models are trained using field data. The new models are accurate and show great potential; albeit, it would be necessary to collect a lot more data for further training. Specifically, training the machine learning models on varying heights. In conclusion, the test rig data is similar to the field data but the vibrations in the system is missing and the heights differ. The missing vibrations results in that the models do not work as intended on field data. The conventional diagnostics approach works, but the generated alarms are binary meaning that the alarm only reveal if the signal is good or bad and does not provide any nuance. The machine learning models does provide nuance, meaning that the model can detect errors, what is causing the error, and warn if an error is about to occur. However, the machine learning models need a lot of data to train on to make this happen.

Background. Due to an increasing number of credit card defaulters, companies arenow taking greater precautions when approving credit applications. When a customermeets certain requirements, credit card firms typically use their experience todecide whether to grant them a credit card. Additionally, a few machine learningmethods have been applied to support the final decision. Objectives. The aim of this thesis is to compare the accuracy of logistic regressionclassifier, random forest classifier, and support vector classifier with the ensemblebagging classifier for predicting credit card approval. Methods. This thesis follows a method called general experimentation to determinethe most accurate classification technique for predicting credit card approval. Thedataset is taken from Kaggle, which contains information about credit card applications.The selected algorithms are trained with training data and validate themusing validation data then evaluate their performance on the testing data by usingmetrics such as accuracy, precision, recall, F1 score, and ROC curve. Now ensemblelearning bagging technique is applied to combine the predictions of these multiplemodels using majority voting to create an ensemble model. Finally, the performanceof the ensemble model was evaluated on the testing data and compared its accuracyto that of the individual models to identify the most accurate classification techniquefor predicting credit card approval. Results. Among the four selected machine learning algorithms, the random forestclassifier performed better with an accuracy of 88.41% on the testing dataset.The second-best algorithm is the ensemble bagging classifier, with an accuracy of84.78%. Hence, the random forest classifier is the most accurate algorithm for predictingcredit card approval. Conclusions. After evaluating various classifiers, including logistic regression classifier,random forest classifier, support vector classifier, and ensemble bagging, it wasobserved that the random forest classifier outperformed the other models in termsof predicting accuracy. This indicates that the random forest classifier was better atpredicting credit card approval.

A wide range of contemporary technologies leveraging ubiquitous mobile phones have addressed the challenge of transportation mode recognition, which involves identifying how users move about, such as walking, cycling, driving a car, or taking a bus. This problem has found applications in various areas, including smart city transportation, carbon footprint calculation, and context-aware mobile assistants. Previous research has primarily focused on recognizing mobility modes using GPS and motion sensor data from smartphones. However, these approaches often necessitate the installation of specialized mobile applications on users’ devices to collect sensor data, resulting in power inefficiency and privacy concerns. In this study, we tackle these issues by presenting a user-independent system capable of distinguishing four forms of locomotion—walking, bus, car, and train—solely based on mobile data (4G) from smartphones. Our system was developed using data collected in three diverse locations (Mekkah, Jeddah, KAUST) in the Kingdom of Saudi Arabia. The underlying concept is to correlate phone speed with features extracted from Channel State Information (CSI), which includes information about Physical Cell ID, received signal strength, and other relevant data. The feature extraction process involves utilizing sliding windows over both the time and frequency domains. By employing statistical classification and boosting techniques, we achieved remarkable F-scores of 85%, 95%, 88%, and 70% for the car, bus, walking, and train modes, respectively. Moreover, we conducted an analysis of the handover rate in a one-tier network and compared the analytical results with real data. This investigation provided novel insights into the influence of transportation modes on handover rate, revealing the correlation between different modes of mobility and network connectivity. This work sets the stage for the development of more efficient and privacy-friendly solutions in transportation mode recognition and network optimization.

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Periodontitis is a gum disease that is the result of infections and inflammation of the gums and the bone which surround and support the teeth. In the most severe cases, teeth may loosen or fall out. Using the ensemble machine learning method Random Forest we predict the risk of developing Periodontitis with three different models and also investigate which predictors are the most useful for the predictions. Each model is based on the same data with both systemic and local predictors but the predictors included in each model differ. The model's performances are evaluated based on different types of accuracy measures and the predictors are evaluated using Variable Importance plots and measures. The model with the most predictors included performed best, as a result of it using the most local predictors which seemed to have a higher importance in predicting, compared to the systemic predictors.

This thesis investigates if house price prediction models perform better when adding macroe- conomic features to a data set with only house-specific features. Previous research has shown that tree-based models perform well when predicting house prices, especially the algorithms random forest and XGBoost. It is common to rely entirely on house-specific features when training these models. However, studies show that macroeconomic variables such as interest rate, inflation, and GDP affect house prices. Therefore it makes sense to include them in these models and study if they outperform the more traditional models with only house-specific features. The thesis also investigates which algorithm, out of random forest and XGBoost is better at predicting house prices. The results show that the mean absolute error is lower for the XGBoost and random forest models trained on data with macroeconomic features. Furthermore, XGBoost outperformed random forest regardless of the set of features. In Con- clusion, the suggestion is to include macroeconomic features and use the XGBoost algorithm when predicting house prices.