Extreme rainfall events and cloudbursts pose a severe threat to human life, infrastructure, agriculture and environmental sustainability particularly in regions of complex terrain such as northern Pakistan. Traditional Numerical Weather Prediction (NWP) systems struggle to resolve localized short-duration convective events because of their coarse spatial resolution and high computational latency leaving vulnerable populations with little lead time to respond. This study presents a full- stack machine learning -based Early Warning System (EWS) for predicting rainfall levels and detecting potential cloudburst events. To overcome the scarcity of high resolution localized meteorological records a physically informed data simulator was engineered to generate more than 145000 daily observations spanning nine years across 49 distinct locations covering temperature, humidity, pressure and wind parameters. Four predictive models Random Forest, Support Vector Machine (SVM), Logistic Regression and a hybrid Convolutional Neural Network with Bidirectional Long Short-Term Memory (CNN-LSTM) were trained and benchmarked on both rainfall magnitude regression and cloudburst classification. The CNN-LSTM model delivered the best performance with 99.1% accuracy, 98.5% precision, 97.2% recall, a 97.8% F1 score and an AUC of 0.99 while maintaining a low false negative rate. The trained intelligence was deployed through a Python Flask REST API for real time inference and a Risk Rating Score (RRS) algorithm was introduced to translate raw model outputs into four actionable color-coded alert levels (Green, Yellow, Orange, Red). An interactive React dashboard lets disaster management officials enter live weather metrics and instantly receive visual warnings. The proposed system provides a practical link between complex meteorological artificial intelligence and user-friendly emergency response platform synoptic macro scale forecasting.
Basharat Ahmad Hassan, Nadeem Khan, Muhammad Zubair et al.· International Journal of Inn...· 0 citations
Saudi Arabia’s industrial economy is quietly underpinned by energy pipelines. But rapid gas-network expansion, decarbonisation commitments, hotter operating environments and the need to extract more value from long-lived assets are redefining reliability. In this review, advanced pipeline technologies and lifecycle management are discussed as potential ways to improve the reliability of the energy infrastructure in the Kingdom while supporting Vision 2030, the Saudi Green Initiative and circular-carbon targets. We used a structured review methodology to synthesise literature and industry evidence published between 2020 and 2025, focusing on leak detection, corrosion prediction, non-metallic materials, digital twins, risk-based inspection, drone/robotic surveillance, and life-cycle carbon accounting. The review shows that reliability is most improved when technologies are not adopted as isolated tools but are embedded in decisions on design, construction, commissioning, operation, maintenance and renewal. Distributed fiber-optic sensing provides continuous monitoring along extended corridors; SCADA and IoT platforms enhance situational awareness; machine-learning models aid in corrosion and failure prediction; and digital twins integrate design data, inspection histories, hydraulic simulations, and maintenance planning. These capabilities are particularly relevant in Saudi Arabia, as new gas pipelines, hydrogen ambitions, remote desert routes, and industrial-zone demand call for high availability and faster fault localisation. This paper presents a lifecycle reliability framework and a technology-integration matrix for pipeline operators in Saudi Arabia. It concludes that the reliability of Saudi energy infrastructure will depend on four enablers: data governance, localised technical capacity, risk-based investment prioritisation and lifecycle performance metrics that balance safety, availability, emissions and cost.
Nadeem Khan· Global academic journal of e...· 0 citations
