This article has been reviewed according to Science X's editorial process and policies. Editors have highlighted the following attributes while ensuring the content's credibility: A hybrid artificial intelligence (AI) system modeled on grasshopper behavior could help allocate medical resources, transport and power during an urban emergency, according to research in the International Journal of Environmental Technology and Management. Tests of the new hybrid model on historical emergency data sets show it to be highly effective.
The team's LSTM-GOA system combines a long short-term memory neural network and the so-called grasshopper optimization algorithm (GOA). LSTM is a machine-learning tool that can identify patterns in data as they change over time. GOA is an optimization technique that searches for better solutions to complex problems based on how a swarm of grasshoppers forages and feeds.
In this hybrid approach, GOA is used to tune the behavior of the LSTM so that it makes better scheduling decisions to find the most appropriate solution. The researchers compared the model with conventional rule-based approaches and other optimization methods. The team improved prediction accuracy for medical resource demand by almost 70%.
The system can respond to otherwise unpredictable spikes in demand following natural disasters, public health incidents and major traffic accidents. Moreover, it can work with noisy or incomplete data sets. The work points the way to the broad use of predictive AI in managing resources during an emergency.
The team explains that city authorities could use historical data to anticipate pressures across several public services and adjust allocations accordingly. This would be more effective than relying on fixed rules or responding to changing demands after the fact. Xiaodan Cai et al, Intelligent decision system for urban emergency management based on combined deep learning and optimisation algorithm, International Journal of Environmental Technology and Management (2026).
DOI: 10.1504/ijetm.2026.155736 MA in English, copy editor since 2021 with experience in higher education and health content. Dedicated to trustworthy science news. Full profile → Master's in physics with research experience.
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