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"Economic forecasting Saudi Arabia."
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The economy of Saudi Arabia in the 21st century : prospects and realities
Discusses Saudi Arabia's efforts to overhaul its economy and the numerous prospects and challenges it faces in doing so. As one of the world's leading oil producers, the outcomes of the most ambitious wave of reforms Saudi Arabia has ever undertaken will also provide valuable lessons for other oil-dependent and resource-based economies.
Book
Urban Residential Water Demand Prediction Based on Artificial Neural Networks and Time Series Models
Water demand prediction is essential in any short or long-term management plans. For short-term prediction of water demand, climatic factors play an important role since they have direct influence on water consumption. In this paper, prediction of future daily water demand for Al-Khobar city in the Kingdom of Saudi Arabia is investigated. For this purpose, the combined technique of Artificial Neural Networks (ANNs) and time series models was constructed based on the available daily water consumption and climatic data. The paper covers the following: forecast daily water demand for Al-Khobar city, compare the performance of the ANNs [General Regression Neural Network (GRNN) model] technique to time series models in predicting water consumption, and study the ability of the combined technique (GRNN and time series) to forecast water consumption compared to the time series technique alone. Results indicate that combining time series models with ANNs model will give better prediction compared to the use of ANNs or time series models alone.
Journal Article
Burden, and trends of breast cancer along with attributable risk factors in Gulf Cooperation Council countries from 1990 to 2019 and its projections
Breast cancer (BC) is a growing global public health concern, affecting millions of women worldwide. Gulf Cooperation Council (GCC) countries are no exception to this trend. Mortality rates in GCC nations are still high despite improvements in BC treatment. This article examines the changing picture of BC incidence, prevalence, and mortality in the GCC region from 1990 to 2019 and predictions up to 2030.
Using data from the Global Burden of Disease study, we analyzed BC incidence, prevalence, and mortality rates per 100,000 individuals across different age groups and countries.
The study reveals a significant rise in Age-Standardized Incidence Rates (ASIR) for breast cancer among females in Saudi Arabia from 1990 to 2019, with Oman experiencing the highest increase and Kuwait the highest decrease. Bahrain also saw a significant increase in male Age-standardized death rate (ASDR), despite all other countries experiencing a decrease. Also, the data demonstrated a statistically significant positive correlation between ASIR and Human Development Index (HDI), evident across all countries. Metabolic risk and tobacco use were identified as primary contributors. A ten-year BC prediction predicts a significant increase in female cases, with Saudi Arabia expected to experience the highest rise.
This study underscores the urgent need for improved BC awareness, early detection through screening programs, enhanced access to quality healthcare services, and the addressing of sociocultural barriers in the GCC countries.
Journal Article
Artificial neural network-driven approaches to improved forecasting of disability care expenditures in an aging Kingdom of Saudi Arabia population
The total number of older persons globally (those aged 60 years and above) was 202 million in 1950; this total multiplied to attain 901 million and is predicted to triple again in 2100. The growth percentage of the elderly population is quickly improving, and the value of their care shall pose a challenging problem in the future. Notably, the number of older persons in the Kingdom of Saudi Arabia (KSA) is fast growing, from 5% of the entire population in 2015 to a predicted 20.9% by 2050. The main problem is the KSA’s management of the rising problem of age-related Non-Communicable Diseases (NCD). With the escalating dimensions of the population of older persons and increased prevalence of NCD causes of risk, the occurrence of NCDs in KSA will rise, resulting in a proportional increase in the requirement for medical assistance. In this paper, an Artificial Neural Network-Based Approaches for Improved Forecasting of Disability Care Expenditures in an Aging Kingdom of Saudi Arabia Population (ANNFDCE-AKSAP) method is proposed. The main objective of the ANNFDCE-AKSAP method is to create an accurate and scalable forecasting system capable of addressing the Kingdom’s evolving disability care needs. Initially, the ANNFDCE-AKSAP technique utilizes a min-max normalization-based data preprocessing model to ensure consistent scaling across variables. Furthermore, the bidirectional variational autoencoder with the self-attention module (BiVAE‐SAM) model forecasts disability care expenses. Finally, the enriched coati osprey algorithm (ECOA)-based hyperparameter selection process is performed to optimize the prediction results of the BiVAE‐SAM method. A wide range of simulations is accomplished to demonstrate the enhanced performance of the ANNFDCE-AKSAP technique, and the results are inspected using several measures. The comparison study of the ANNFDCE-AKSAP technique illustrated the lowest MSE, 0.0128, and MAE, 0.0942, compared to all other methods.
Journal Article
Cutting through the Hype: Understanding the Economic Potential of Saudi Arabia-Israel Diplomatic Relations
This article will assess the extent of future trade relations between Israel and Saudi Arabia after normalization, and attempt to identify key sectors likely to benefit from the diplomatic shift. Using a robust methodology, we aim to project potential trade volumes between the two countries and discuss the challenges inherent to the new economic landscape. The core of this analysis uses 2020 trade figures, a pivotal year marked by the signing of the \"Abraham Accords\", to project future trends and estimate potential trade figures between KSA and Israel in the coming years. The strength of our research lies not in its precise trade volume calculations but in the highlighted sectors poised for growth and an understanding of factors that influence economic cooperation. Initially focused on Saudi Arabia—the more sought-after party in this context—the article details both the current measures taken to align KSA politically and diplomatically with Israel and previous steps taken to reach a peace agreement. We then discuss relevant regional and geopolitical developments. Moving on to our economic analysis, we highlight factors that could shape or alter the scope of transactions between the two countries. Finally, we introduce our method for evaluating the economic potential between Saudi Arabia and Israel, by comparing it with World Trade Organization data from various countries already trading with Saudi Arabia in similar fields.
Journal Article
Predicting Close Price in Emerging Saudi Stock Exchange: Time Series Models
The forecasting of stock prices is an important area of research because of the benefits it provides for individuals, corporations, and governments. The purpose of this study is to investigate the application of a key of study to the prediction of the adjusted closing price of a particular firm. Estimating a stock’s volatility is one of the more difficult tasks that traders must undertake. Investors are able to mitigate the risks associated with their portfolios and investments to a greater extent when stock prices can be accurately predicted. Prices of stocks do not move in a linear fashion. We propose artificial intelligence (AI) for multilayer perceptron (MLP) and long short-term memory (LSTM) models to predict fluctuations on the Saudi Stock Exchange (Tadawul). This paper focuses on the future forecasting of the stock exchange in the communication, energy, financial, and industrial sectors. The historical records from Tadawul were used as a basis for data collection for these sectors, in time periods from 2018 to 2020. For the purpose of predicting the future values of various stock market sectors, the AI algorithms were applied over a period of 60 days. They demonstrated highly effective performance when simulated using input data, which was carried out to validate the proposed model. In addition, the correlation coefficient (R) of the LSTM and MLP models for predicting the stock market in four sectors in the Saudi Stock Exchange (Tadawul) was >0.9950, which indicates that the outcomes were in good agreement with the predicted values. The outcomes of the forecasts were provided for each method based on four different measures. Among all the algorithms utilized in this work, LSTM demonstrated the most accurate findings and had the best capacity for model fitting.
Journal Article
Machine Learning and Artificial Intelligence for a Sustainable Tourism: A Case Study on Saudi Arabia
This work conducts a rigorous examination of the economic influence of tourism in Saudi Arabia, with a particular focus on predicting tourist spending patterns and classifying spending behaviors during the COVID-19 pandemic period and its implications for sustainable development. Utilizing authentic datasets obtained from the Saudi Tourism Authority for the years 2015 to 2021, the research employs a variety of machine learning (ML) algorithms, including Decision Trees, Random Forests, K-Neighbors Classifiers, Gaussian Naive Bayes, and Support Vector Classifiers, all meticulously fine-tuned to optimize model performance. Additionally, the ARIMA model is expertly adjusted to forecast the economic landscape of tourism from 2022 to 2030, providing a robust predictive framework for future trends. The research framework is comprehensive, encompassing diligent data collection and purification, exploratory data analysis (EDA), and extensive calibration of ML algorithms through hyperparameter tuning. This thorough process tailors the predictive models to the unique dynamics of Saudi Arabia’s tourism industry, resulting in robust forecasts and insights. The findings reveal the growth trajectory of the tourism sector, highlighted by nearly 965,073 thousand tourist visits and 7,335,538 thousand overnights, with an aggregate tourist expenditure of SAR 2,246,491 million. These figures, coupled with an average expenditure of SAR 89,443 per trip and SAR 9198 per night, form a solid statistical basis for the employed predictive models. Furthermore, this research expands on how ML and AI innovations contribute to sustainable tourism practices, addressing key aspects such as resource management, economic resilience, and environmental stewardship. By integrating predictive analytics and AI-driven operational efficiencies, the study provides strategic insights for future planning and decision-making, aiming to support stakeholders in developing resilient and sustainable strategies for the tourism sector. This approach not only enhances the capacity for navigating economic complexities in a post-pandemic context, but also reinforces Saudi Arabia’s position as a premier tourism destination, with a strong emphasis on sustainability leading into 2030 and beyond.
Journal Article
Effect of easing lockdown and restriction measures on COVID-19 epidemic projection: A case study of Saudi Arabia
Objectives In this study we compared two predictions of COVID-19 cases in the Kingdom Saudi Arabia (KSA) using pre-and post-relaxation of lockdown period data to provide an insight regarding rational exit strategies. We also applied these projections to understand economic costs versus health benefit of lockdown measures. Methods We analyzed open access data on COVID-19 cases from March 6 to January 16, 2021 in the KSA. To understand the epidemic projection during the pre- and post-lockdown period, we used two types of modeling: the SIR model, and the time series model. We also estimated the costs and benefits of lockdown- QALY gained compared to the costs of lockdown considering the payment threshold of the Government. Results Prediction using lockdown period data suggested that the epidemic might slow down significantly after 109 thousand cases and end on October 6, 2020. However, analysis with latest data after easing lockdown measures suggested that epidemic might be close to an end on October 28, 2021 with 358 thousand cases. The peak has also been shifted from May 18, 2020 to Jun 24, 2020. While earlier model predicted a steady growth in mid-June, the revised model with latest data predicted it in mid-August. In addition, we estimated that 4986 lives would have been saved if lockdown continued but the cost per life saved would be more than $378 thousand, which is way above not only the KSA threshold, but also the threshold of any other highly advanced economies such as the UK and the USA. Conclusions Our results suggest that relaxation of lockdown measures negatively impacts the epidemic. However, considering the negative impact of prolong lockdown measures on health and economy, countries must decide on the best timing and strategy to exit from such measures to safely return to normal life with minimum loss of lives and economy considering its economic and health systems' capacity. Instead of focusing only on health, a balanced approach taking economy under consideration is recommended.
Journal Article
Rainfall Prediction Rate in Saudi Arabia Using Improved Machine Learning Techniques
Every farmer requires access to rainfall prediction (RP) to continue their exploration of harvest yield. The proper use of water assets, the successful collection of water, and the successful pre-growth of water construction all depend on an accurate assessment of rainfall. The prediction of heavy rain and the provision of information regarding natural catastrophes are two of the most challenging factors in this regard. In the twentieth century, RP was the most methodically and technically complicated issue worldwide. Weather prediction may be used to calculate and analyse the behaviour of weather with unique features and to determine rainfall patterns at an exact locale. To this end, a variety of methodologies have been used to determine the rainfall intensity in Saudi Arabia. The classification methods of data mining (DM) approaches that estimate rainfall both numerically and categorically can be used to achieve RP. This study, which used DM approaches, achieved greater accuracy in RP than conventional statistical methods. This study was conducted to test the efficacy of several machine learning (ML) approaches for forecasting rainfall, utilising southern Saudi Arabia’s historical weather data obtained from the live database that comprises various meteorological data variables. Accurate crop yield predictions are crucial and would undoubtedly assist farmers. While engineers have developed analysis systems whose performance relies on several connected factors, these methods are seldom used despite their potential for precise crop yield forecasts. For this reason, agricultural forecasting should make use of these methods. The impact of drought on crop yield can be difficult to forecast and there is a need for careful preparation regarding crop choice, planting window, harvest motive, and storage space. In this study, the relevant characteristics required to predict precipitation were identified and the ML approach utilised is an innovative classification method that can be used determine whether the predicted rainfall will be regular or heavy. The outcomes of several different methodologies, including accuracy, error, recall, F-measure, RMSE, and MAE, are used to evaluate the performance metrics. Based on this evaluation, it is determined that DT provides the highest level of accuracy. The accuracy of the Function Fitting Artificial Neural Network classifier (FFANN) is 96.1%, which is higher than that of any of the other classifiers currently used in the rainfall database.
Journal Article
Flight delay prediction: Evaluating machine learning algorithms for enhanced accuracy
Flight delays pose substantial operational and economic challenges for airlines, directly affecting scheduling efficiency, resource allocation, and passenger satisfaction. Accurate prediction of arrival delays is therefore critical for optimizing airline operations and enhancing customer experience. This study systematically evaluates the predictive performance of six machine learning classifiers-Decision Tree, Random Forest, Support Vector Classifier (SVC), Logistic Regression, K-Nearest Neighbors (KNN), and Naive Bayes-on a comprehensive flight dataset, with particular attention to the challenges posed by class imbalance. To mitigate skewed class distributions, resampling techniques including Random Oversampling, Synthetic Minority Oversampling Technique (SMOTE), and Adaptive Synthetic Sampling (ADASYN) were applied to the training data. Model performance was rigorously assessed using stratified 10-fold cross-validation and further validated on a hold-out test set, employing multiple evaluation metrics: Accuracy, F1-score, Matthews Correlation Coefficient (MCC), and ROC-AUC. The results demonstrate that Random Forest combined with Random Oversampling and Decision Tree combined with SMOTE both achieved the highest predictive performance (accuracy 0.90, F1-score 0.90, MCC 0.73, ROC-AUC 0.87. Notably, simpler models such as Naive Bayes exhibited competitive results under balanced conditions, underscoring the continued relevance of probabilistic classifiers in certain operational contexts. These findings highlight the critical role of resampling strategies and rigorous cross-validation in developing reliable, high-performing predictive models for imbalanced flight delay datasets, offering actionable insights for both airline operations and data-driven decision-making.
Journal Article