Cities flooding is a major hydrological risk in rapidly urbanizing regions, intensified by climate change, extreme rainfall, and inadequate drainage systems. Traditional hydrological models require extensive calibration and high-resolution data, which are often unavailable for many cities. To address this issue, this study proposes a flood prediction framework that integrates Geographic Information Systems (GIS) with Machine Learning (ML). Multi-source geospatial data such as digital elevation models (DEM), land use–land cover (LULC), soil properties, drainage density, rainfall intensity, and historical flood records are used to derive flood conditioning factors in a GIS environment. These factors are then applied as inputs for supervised ML algorithms including Artificial Neural Networks (ANN), Random Forest (RF), Support Vector Machine (SVM), and Gradient Boosting. Model performance is evaluated using RMSE, MAE, R², and ROC–AUC metrics. Results indicate that hybrid GIS-ML models outperform traditional approaches by effectively capturing non-linear relationships between hydrometeorological and urban factors. The framework provides a reliable decision-support tool for urban planners and disaster management authorities to identify flood-prone areas, improve drainage planning, and enhance early warning systems.
Ahmed Hassan, Fatima Noor· International Journal of Mod...· 0 citations
Business Intelligence (BI) systems have become essential tools for strategic financial management by transforming large volumes of financial data into actionable insights for planning, forecasting, budgeting, investment analysis, risk management, and decision-making. Unlike traditional financial management methods that relied on historical reporting and manual analysis, BI technologies automate data collection, integrate information from multiple sources, and provide real-time analytical capabilities. Key BI components such as data warehousing, OLAP, dashboards, reporting tools, data mining, and predictive analytics help organizations monitor performance, identify trends, assess risks, and develop evidence-based financial strategies. BI adoption across industries, including banking, manufacturing, healthcare, and telecommunications, has improved decision quality, reporting efficiency, forecasting accuracy, financial transparency, and organizational competitiveness. This study presents a systematic review of BI systems in strategic financial management before 2019, examining their evolution, architecture, analytical methods, and impact on financial performance. The findings indicate that BI frameworks significantly enhance financial planning, governance, and strategic decision-making despite challenges related to data integration, organizational resistance, and implementation costs. Overall, BI technologies serve as a foundation for modern data-driven financial decision support systems.
Ahmed Hassan, Fatima Noor· International Journal of Com...· 0 citations