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Comparing machine learning, deep learning, and reinforcement learning performance in Culex pipiens predictive

Wei Yin¹, Sanad H Ragab², Michael G Tyshenko³

¹School of Mathematical and Statistical Sciences, The University of Texas Rio Grande Valley, Edinburg, Texas, United States of America.

|November 13, 2025

Summary

This summary is machine-generated.

Reinforcement learning (RL) methods effectively predict the geographic distribution of the common house mosquito (Culex pipiens), even with limited data. Altitude and precipitation are key factors in predicting mosquito presence.

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Area of Science:

Ecology
Machine Learning
Epidemiology

Background:

Machine learning (ML) and deep learning (DL) are established for species presence prediction.
Reinforcement learning (RL) is novel for species distribution modeling.
Culex pipiens is a global vector for West Nile Virus (WNV).

Purpose of the Study:

Compare ML/DL classifiers with RL methods for predicting Culex pipiens distribution.
Evaluate the efficacy of different algorithms using historical presence data in the USA.

Main Methods:

Logistic regression
Random forest classifier
Deep neural networks
RL algorithms: Q-learning, Deep Q-Network (DQN), REINFORCE, Actor-Critic

Main Results:

All tested methods showed comparable performance in predicting species distribution.
RL methods (DQN, REINFORCE) were effective with fewer features.
Altitude and annual precipitation were the most significant bioclimatic predictors.

Conclusions:

RL methods offer a promising alternative for species distribution modeling, especially in data-limited or dynamic scenarios.
Bioclimatic variables like altitude and precipitation are crucial for understanding Culex pipiens distribution.