COVID-19 is a disease-causing coronavirus strain that emerged in December 2019 that led to an ongoing global pandemic. The ability to anticipate the pandemic’s path is critical. This is important in order to determine how to combat and track its spread. COVID-19 data is an example of time-series data where several methods can be applied for forecasting. Although various time-series forecasting models are available, it is difficult to draw broad theoretical conclusions regarding their relative merits. This paper presents an empirical evaluation of several time-series models for forecasting COVID-19 cases, recoveries, and deaths in Saudi Arabia. In particular, seven forecasting models were trained using autoregressive integrated moving average, TBATS, exponential smoothing, cubic spline, simple exponential smoothing Holt, and HoltWinters. The models were built using publicly available daily data of COVID-19 during the period of 24 March 2020 to 5 April 2021 reported in Saudi Arabia. The experimental results indicate that the ARIMA model had a smaller prediction error in forecasting confirmed cases, which is consistent with results reported in the literature, while cubic spline showed better predictions for recoveries and deaths. As more data become available, a fluctuation in the forecasting-accuracy metrics was observed, possibly due to abrupt changes in the data.

The wind is one of the most important sources of renewable energy. However, it is associated with many challenges, with one of the most notable being determining suitable locations for wind power farms based on different evaluation criteria. In this study, we investigated the suitability of wind farm sites in the Red Sea off the coast of Saudi Arabia using the analytical hierarchy process (AHP) and a Geographic Information System (GIS). We assessed the suitability of offshore locations for wind energy projects, differentiating between fixed and floating turbines, and identified a 4180 km² area as less suitable, whereas the 33,094 km², 20,618 km², and 11,077 km² areas were deemed suitable, very suitable, and extremely suitable, respectively. These findings highlight the differences in suitability levels based on specific geographical features. Moreover, the extremely suitable location, which has the largest area of 3032 km², has the capacity to generate an annual energy output of 56,965,410 MWh/year.

This study aimed to compare and evaluate three satellite-based precipitation estimation products: Integrated Multi-satellitE Retrievals for Global Precipitation Measurement Early Run (IMERG-Early Run), Climate Prediction Center MORPHing technique Real Time (CMORPH-RT), and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Dynamic Infrared Rain rate Now (PDIR-Now) to identify the optimal integration strategies to improve the extreme rainfall estimation during Super Typhoon Yagi (September, 2024) in Hanoi, Vietnam, using validation data from 25 ground stations. In-depth analysis of three extreme rainfall series during Typhoon Yagi (6–9 September 2024), examining 93 extreme rainfall events at the 95th percentile precipitation threshold (R95p = 21.78 mm/h), combined with statistics at lower percentile thresholds (R1p, R5p, R10p, and R90p) and upper percentile threshold (R99p), revealed IMERG-Early best captured the peak rainfall, CMORPH-RT achieved highest total rainfall accuracy, while PDIR-Now offered the best spatial analysis. However, limitations included time lags, inability to detect rainfall events above R99p (41.69 mm/hour), and low detection rates (8–12%) in areas first impacted by the typhoon. This study identified that integration strategies combining different satellite products based on their strengths at specific time scales showed potential for improved rainfall estimation: PDIR-Now with IMERG-Early (1–3 h) and IMERG-Early with CMORPH-RT (6–12 h). These integration approaches accounted for each product’s unique capabilities in capturing different aspects of extreme rainfall during super typhoon events.

The Rub’ al Khali desert (or Empty Quarter) is the largest and perhaps most significant sand sea in the world. Located on the southern Arabian Peninsula, the dune field has remained largely unexplored owing to the harsh clime and difficult terrain. This study takes advantage of geospatial technology (interpolations, supervised classification, minimum focal statistic) to extract information from the data contained in global Digital Elevation Model (DEM)s, satellite imagery. The main objective here is to identify and map different dune forms within the sand sea, estimate the volume of sand and explore probable sources of sand. The analysis of dune color strongly suggests that the sand is not completely reworked and intermixed. If this is true, a spatial variability map of the mineral composition of the sand could be very revealing. The red sand is quite pronounced, the largest volume of sand (~36%) is associated with the yellow color class. Yellow sand covers most of the western part of the dunes field and seems to be a transitional color between red and white sand in the eastern part of the dune field. This suggests that the yellow sand might be derived from both local and regional sources, or it might be less oxidized, reworked, or have a different composition that represents a combination of red and white sand.

Understanding the movement, direction, and shape of sand dunes can contribute to reducing their impact on infrastructure and the environment. The Rub’ al Khali desert has a distribution of dune types. This study aims to identify and map the different types of dunes within the Rub’ al Khali using a texture analysis method based on a digital elevation model (DEM). Statistical texture analysis methods (variance, skewness, and kurtosis) show three different textures of sand dune shapes, according to the geography of the dunes, using data contained in global DEMs. The analysis presented in this study focused on the use of DEMs to investigate the varied dune morphology within the Rub’ al Khali. The GMTED2010 and EarthEnv_DEM90 digital elevation models were compared. Spatial variability in dune height, spatial variability in dune texture, and profile graphs were created to examine dune surfaces in cross-section. The results provided six different dune types within the sand sea: giant compound linear dunes, simple linear dunes, simple transverse dunes, compound crescentic dunes (megabarchans), huge star dunes, and many transitional forms that defy classification. The results showed that the compound linear dune and simple linear dune were the dominant dune types, covering 41.61% and 31.7% of the total study area, respectively. The maps of variance, using either 10 × 10 and 30 × 30 focal blocks, produced a fairly sharp distinction in dune texture. It is hoped that future research in aeolian geomorphology will greatly benefit from these results, which could easily be expanded with the use of more sophisticated pattern recognition software, which clearly shows the value of using such an approach.