Predicting Future Mosquito Larval Habitats Using Time Series Climate Forecasting and Deep Learning

TL;DR

This paper combines climate time series forecasting with deep learning to predict future mosquito larval habitats, highlighting potential regional expansions due to climate change.

Contribution

It introduces an integrated approach using ecological data, climate projections, and neural networks to forecast mosquito habitat expansion under climate change scenarios.

Findings

High-elevation regions may become more susceptible to mosquito infestations.

The model successfully predicts future larvae abundance based on ecological and climate data.

Results indicate regional ecosystem changes influence mosquito habitat expansion.

Abstract

Mosquito habitat ranges are projected to expand due to climate change. This investigation aims to identify future mosquito habitats by analyzing preferred ecological conditions of mosquito larvae. After assembling a data set with atmospheric records and larvae observations, a neural network is trained to predict larvae counts from ecological inputs. Time series forecasting is conducted on these variables and climate projections are passed into the initial deep learning model to generate location-specific larvae abundance predictions. The results support the notion of regional ecosystem-driven changes in mosquito spread, with high-elevation regions in particular experiencing an increase in susceptibility to mosquito infestation.