Manuscript Topics

Introduction

In an era defined by rapid climate change and the increasing prevalence of extreme weather events, the challenge of accurately assessing landslide susceptibility has never been more critical. Advancements in machine learning have already begun to transform hazard prediction, while remote sensing technologies continue to refine our spatial insights. This special issue aims to merge these powerful tools with cutting-edge research into risk analysis and resilience strategies. Moreover, we seek to explore the emerging potential of big language models to process vast environmental datasets and provide nuanced interpretations, thereby opening new pathways for decision-making in geohazard management.    
This special issue strives to serve as a confluence of innovative research, bridging state-of-the-art machine learning and remote sensing techniques with critical climate change insights and the transformative potential of big language models. By focusing on robust risk analysis and resilience strategies, this collection will pave the way for more effective landslide susceptibility assessments and proactive environmental management. I look forward to collaborating with the authors and the editorial team of AIMS Environmental Science to highlight groundbreaking advancements in this field.

Scope and Objectives

This special issue is dedicated to fostering innovative research at the intersection of environment, earth science and computational techniques, with a special focus on:
Integrating Climate Change Data: Encouraging studies that incorporate climate projections, extreme weather patterns, and hydrological shifts into landslide predictive models, reinforcing adaptive strategies and resilience planning.
Enhancing Remote Sensing Applications: Highlighting research that leverages satellite imagery, LiDAR, and other geospatial data sources in tandem with machine learning algorithms to improve spatial resolution in hazard mapping.
Advancing Risk Assessment and Resilience Frameworks: Soliciting contributions that formalize robust risk quantification methodologies and resilience-building strategies, translating predictive insights into actionable recommendations for public safety and infrastructure planning.
Exploring Big Language Models: Pioneering the exploration of big language models, which can analyze and synthesize complex geospatial and climatic datasets, to complement traditional machine learning approaches. This exploration may yield new methodologies for interpreting environmental risks and guiding practical interventions.
Methodological Innovation: Welcoming novel algorithms—from deep learning and ensemble methods to Bayesian and interpretable models—that enhance the accuracy and reliability of landslide susceptibility assessments across diverse environmental contexts.

Topics of Interest

Submissions may encompass, but are not limited to, the following topics:
Climate Change Dynamics and Environmental Impact: Research integrating climate change scenarios, hydrological processes, and extreme weather events into geohazard models.
Remote Sensing and Geospatial Data Fusion: Innovative techniques that involve the use of remote sensing technologies (e.g., satellite data, LiDAR) in conjunction with machine learning to delineate landslide-prone areas with greater precision.
Risk Analysis and Resilience Strategies: Studies that propose frameworks for uncertainty quantification, sensitivity analysis, and the development of resilient infrastructures and communities in the wake of geohazards.
Big Language Models in This Field: Exploratory approaches utilizing big language models to extract, process, and integrate diverse environmental datasets, presenting novel insights for landslide susceptibility assessment and risk management.
Interdisciplinary Case Studies: Empirical examples from different geographic regions that demonstrate the practical application of these methodologies, linking scientific innovation with actionable landslide risk mitigation strategies.

Instruction for Authors    
http://www.aimspress.com/aimses/news/solo-detail/instructionsforauthors    
Please submit your manuscript to online submission system    
https://aimspress.jams.pub/