# The Evolution of Modeling Approaches: From Statistical Models to Deep Learning for Locust and Grasshopper Forecasting

## TL;DR

This paper reviews how deep learning models, especially GRUs, can improve forecasting of locust and grasshopper outbreaks to support better pest management in grassland ecosystems.

## Contribution

The paper adapts locust prediction models for grasshopper forecasting and proposes integrating XAI and transfer learning to address deep learning limitations in ecological forecasting.

## Key findings

- Deep learning models outperform traditional methods in capturing complex ecological dynamics.
- GRUs show promise in data-limited regions but face challenges like interpretability and generalizability.
- Grasshopper outbreaks in grassland ecosystems are under-researched compared to locusts.

## Abstract

Locust outbreaks, driven by factors such as weather, vegetation, and soil conditions, pose a significant threat to agriculture and ecosystems. Since grasshoppers in grassland ecosystems exhibit similar behaviors to locusts, we adapt locust prediction models to forecast grasshopper outbreaks. This study reviews various prediction methods, including traditional statistical models, machine learning (ML), and deep learning (DL), highlighting the advantages of DL in ecological forecasting tasks as demonstrated by the existing literature. While Gated Recurrent Units (GRUs) are promising, especially in data-limited regions, challenges such as data scarcity, the specific limitations of grassland ecosystems, and model interpretability remain. This study suggests combining GRUs with advanced technologies to improve the accuracy and transparency of locust and grasshopper predictions, enhancing pest management in grassland regions.

Locust outbreaks cause a significant threat to global food security and ecosystem stability, with particularly severe consequences in grassland regions, where grasshoppers also exert considerable ecological pressure. In comparison to grasshoppers, locusts typically occur at much larger spatial scales, as their strong migratory ability and collective movement behavior lead to greater spatial connectivity and autocorrelation. The forecasting of both locust and grasshopper outbreaks remains a formidable scientific challenge, primarily due to the complex, nonlinear spatiotemporal interactions among environmental drivers such as weather, vegetation, and soil conditions. This review compares the evolution of prediction methodologies for locust and grasshopper outbreaks, focusing on the application of deep learning (DL) methods to ecological forecasting tasks. It traces the development from traditional statistical models to classical machine learning, and ultimately to DL, assessing the strengths and limitations of key DL architectures—including Deep Neural Networks (DNNs), Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM) networks, and Gated Recurrent Units (GRUs)—in modeling the intricate dynamics of locust populations. While most studies have concentrated on locust outbreaks, this review emphasizes the adaptation of these models to grassland ecosystems, such as those in Inner Mongolia, where grasshopper outbreaks exhibit similarities to locust plagues but have been largely overlooked in DL research. Despite the potential of DL, challenges such as data scarcity, limited model generalizability across regions, and the “black box” issue of low interpretability remain. To address these issues, we propose future research directions that integrate Explainable AI (XAI), transfer learning, and generative models like GANs to development more robust, transparent, and ecologically grounded forecasting tools. By promoting the use of efficient architectures like GRUs within customized frameworks, this review aims to guide the development of effective early warning systems for sustainable locust management in vulnerable grassland ecosystems.