About the Journal

Overview

Forecasting Engineering is a peer-reviewed international journal dedicated to advancing engineering practices through cutting-edge forecasting methodologies and predictive modeling. By integrating statistical techniques, artificial intelligence, and machine learning, the journal provides a platform for researchers, academics, and industry professionals to contribute to the development of robust, data-driven engineering solutions.

In an era where technological advancements drive rapid changes in engineering systems, accurate forecasting has become essential for decision-making, risk assessment, and performance optimization. Forecasting Engineering aims to bridge the gap between forecasting techniques and practical engineering applications, promoting innovative research that enhances the reliability, sustainability, and efficiency of engineering processes.

 

Scope and Focus

The journal covers a broad spectrum of forecasting methodologies and their applications in engineering, including but not limited to:

Time Series Analysis and Predictive Modeling

• Statistical models and computational approaches for trend analysis
• Forecasting techniques applied to complex engineering systems
• Hybrid models integrating traditional statistical methods with AI

Machine Learning and AI-Driven Forecasting

• Deep learning architectures for predictive analytics in engineering
• AI-enhanced models for anomaly detection and failure prediction
• Application of reinforcement learning for engineering optimization

Reliability and Risk Prediction in Engineering Projects

• Probabilistic risk assessment and uncertainty modeling
• Predictive maintenance and failure forecasting in industrial systems
• Quantitative risk models for infrastructure and energy systems

Optimization and Decision-Making Under Uncertainty

• Stochastic modeling for engineering design and operation
• Multi-objective optimization techniques in complex systems
• Forecasting-driven decision support tools for resource allocation

Applications of Forecasting in Key Engineering Domains

• Energy Systems: Demand forecasting, renewable energy integration, and power grid optimization
• Manufacturing: Process optimization, quality control, and predictive maintenance
• Construction and Civil Engineering: Structural health monitoring, project risk forecasting, and smart infrastructure
• Transportation: Traffic flow prediction, logistics optimization, and intelligent transport systems

Climate Impact Prediction on Infrastructure and Systems

• Climate risk forecasting for infrastructure resilience
• Environmental forecasting for sustainable engineering solutions
• Long-term climate impact assessment on energy and transportation systems

This interdisciplinary approach ensures that Forecasting Engineering remains at the forefront of scientific inquiry, providing novel insights into predictive modeling, risk management, and system optimization across multiple engineering disciplines.