Current research studies offer an investigation of machine learning methods used for forecasting rainfall in urban metropolitan cities. Time series data, distinguished by their temporal complexities, are exploited using a unique data segmentation approach, providing discrete training, validation, and testing sets. Two unique models are created: Model-1, which is based on daily data, and Model-2, which is based on weekly data. A variety of performance criteria are used to rigorously analyze these models. CatBoost, XGBoost, Lasso, Ridge, Linear Regression, and LGBM are among the algorithms under consideration. This research study provides insights into their predictive abilities, revealing significant trends across the training, validation, and testing phases. The results show that ensemble-based algorithms, particularly CatBoost and XGBoost, outperform in both models. CatBoost emerged as the model of choice throughout all assessment stages, including training, validation, and testing. The MAE was 0.00077, the RMSE was 0.0010, the RMSPE was 0.49, and the R² was 0.99, confirming CatBoost’s unrivaled ability to identify deep temporal intricacies within daily rainfall patterns. Both models had an R² of 0.99, indicating their remarkable ability to predict weekly rainfall trends. Significant results for XGBoost included an MAE of 0.02 and an RMSE of 0.10, indicating their ability to handle longer time intervals. The predictive performance of Lasso, Ridge, and Linear Regression varies. Scatter plots demonstrate the robustness of CatBoost and XGBoost by demonstrating their capacity to sustain consistently low prediction errors across the dataset. This study emphasizes the potential to transform urban meteorology and planning, improve decision-making through precise rainfall forecasts, and contribute to disaster preparedness measures.

This study presents a cutting-edge framework for assessing earthquake vulnerability and risk in residential areas of Al-Seeb, Muscat Governorate (Sultanate of Oman). Drawing upon a rich dataset encompassing seismic, geotechnical, structural, environmental, and socioeconomic parameters, thematic vulnerability maps were developed using a GIS-based analytic hierarchy process (GIS-AHP). These were systematically integrated to produce comprehensive risk matrices. It has been noted that the Rusayl industrial estate emerged as a critical hotspot due to the clustering of industrial facilities and fuel stations near densely populated zones, amplifying hazard proximity. Physical vulnerability patterns largely mirrored geotechnical characteristics but were further shaped by urban density and infrastructure quality. A risk index was derived for each grid cell, enabling the formulation of a detailed microzonation map that categorizes Al-Seeb into five distinct risk zones. Central urban areas like Al-Hail North and segments of Old Al-Khoud plunged within the highest vulnerability category, while parts of Al-Mawaleh North exhibited a blend of high and very high risk, primarily due to aging infrastructure and insufficient retrofitting. Interestingly, Al-Maabilah showed signs of structural resilience, with 60% of buildings experiencing minor damage and only 20% sustaining major impacts. The robust database generated offers critical insights for policymakers, planners, and stakeholders, serving as a strategic tool for informed decision-making. The study’s outcomes align well with Oman Vision 2040 and contribute directly to the goals of sustainable and disaster-resilient urban development outlined in the United Nation’s Sustainable Development Goal (UN-SDG) 11. By embedding these insights into national urban planning and construction policies, the study supports risk-informed development and financial risk reduction for any futuristic projects in the Sultanate of Oman. In response to the shortcomings and delays of previous research, the present study is the first of its kind in the Arabian region, significantly contributing to the establishment of a sustainable and resilient Omani society.

This study investigates the use of Date-Palm Seeds (DS) as a sustainable alternative to coarse aggregates (CA) in concrete, based on the significant date production in Al Madinah, which exceeds 340,000 tons each year. Leveraging Madinah’s abundant date production, the research aims to recycle DS, a prominent agricultural byproduct, to create environmentally friendly concrete in alignment with Saudi Arabia’s Vision 2030, which emphasizes sustainability and resource efficiency. Concrete mixtures were prepared with DS replacing CA at ratios of 0%, 25%, 60%, and 100%-DS, and their mechanical properties were evaluated. Laboratory tests, including water absorption, specific gravity, and compressive strength, were conducted after 7 and 28 days of curing using standard procedures to assess the effects on compressive strength, density, and workability. The results indicate that partial replacement of CA with DPS significantly influences compressive strength and density, suggesting potential for lightweight concrete applications. At 25% and 60%-DS replacements, the 28-day compressive strengths were 21.51 MPa (47.5% reduction) and 13.93 MPa (66.0% reduction), respectively, while 100%-DS replacement proved unsuitable, as samples failed to bear any load. The findings highlight that optimizing DPS properties and limiting replacement ratios to below 25%-DS could yield satisfactory results. Although Date-Palm Seeds (DS) offers advantages such as reduced density and environmental sustainability, modifications to its properties, including improved bonding and reduced water absorption, are necessary for its use in structural applications. This research contributes to sustainable construction practices by integrating agricultural waste into concrete production, promoting environmental and economic benefits.

Excavation in Sabkha soils, found in coastal regions of Saudi Arabia, presents complex challenges in construction due to their unique geotechnical characteristics. These soils are known for their high compressibility, collapsibility, and variable sediment deposition, rendition them unsuitable for excavation without adequate support systems. As urbanization and industrial activities continue to expand into these areas, the demand for effective excavation support systems becomes gradually critical. This study investigates the feasibility of employing Deep Soil Mixing (DSM) as an excavation support system in Sabkha soils. Utilizing finite element analysis through PLAXIS 3D software, the behaviour of DSM walls in Sabkha soil under various conditions is rigorously simulated. The findings of this research establish that DSM walls can serve as an efficient excavation support system in Sabkha soils, provided that crucial factors, particularly excavation depth, are considered during the design phase. The study demonstrates that properly designed DSM walls, in combination with appropriate anchoring methods, can substantially reduce horizontal displacement and bending moments, ensuring the stability of excavations in Sabkha soils. The research highlights the need for further investigations into the cost-effectiveness of DSM walls in comparison to other excavation support options to evaluate the economic feasibility of using DSM in Sabkha soil applications.