Deadline Date: 28 February 2026
The growing intensity and frequency of extreme climate events, such as global temperature rise, heavy rainfall, and prolonged droughts, have led to an increase in geological disasters worldwide. Regions like Shanxi Province in China's Yellow River Basin are particularly vulnerable due to their unique geography and climate. Human activities, especially fossil fuel burning, have intensified global warming and contributed to the rising concentrations of atmospheric carbon dioxide. Mathematical models play a crucial role in understanding and predicting climate changes, from large-scale phenomena to urban heat island effects. However, despite advances in climate modeling, challenges remain in accurately predicting geological disasters, with many models limited by insufficient data, complex parameters, and technological constraints. To address these issues, interdisciplinary collaboration is needed to enhance model accuracy and improve data integration, simplification, and application in real-world decision-making. The aim of this Special Issue is to showcase multi-scale mathematical models and optimization techniques for climate change adaptation, urban planning, and disaster resilience. Contributions focusing on optimization methods, data integration, and simplification of model outcomes are highly encouraged, with the goal of advancing the predictive power of climate models for better disaster management and policy-making.
Suggested themes:
- Agent-Based Multi-Scale Modeling for Climate-Induced Migration Prediction.
- Stochastic Modeling of Spatiotemporal Climate Variability in Coastal Ecosystems.
- Partial Differential Equation-Based Simulation of Global Temperature Dynamics.
- Sensitivity Analysis for Multi-Objective Climate Adaptation Strategy Optimization.
- Deep Learning-Augmented Earth System Models for Predicting Greenhouse Gas Emissions.
- Integrating OpenFOAM Simulations with Remote Sensing for Urban Heat Island Analysis.
- Optimization of Renewable Energy Infrastructure in Smart Cities Using Genetic Algorithms.
- Geo-Spatial Big Data Analytics Using Google Earth Engine for Climate Impact Visualization.
- High-Performance Computing for Regional Climate Modeling Using COMSOL Multiphysics.
- Leveraging Apache Spark for Scalable Climate Data Processing in Predictive Analytics.
- Modeling Agricultural Yield Under Climate Stress Using Machine Learning-Enhanced Simulations.
- Predictive Simulation of Biodiversity Loss Due to Temperature and Habitat Shifts.
- Modeling the Socioeconomic Impact of Climate Change on Food Security in Sub-Saharan Africa.
- Ecosystem Service Degradation Modeling in Response to Long-Term Climate Stressors.
The growing intensity and frequency of extreme climate events, such as global temperature rise, heavy rainfall, and prolonged droughts, have led to an increase in geological disasters worldwide. Regions like Shanxi ... show more
Editorial board
