Artificial Intelligence Modelling of Rainfall Effects on Amphibian Reproductive Success

Abstract

Introduction: Reproductive success of amphibians depends greatly on the variability of rainfall because rainfall influences the onset of breeding, water availability, breeding site sustainability, and larval growth. The increase in climate variability requires a predictive model that can handle complicated and non-linear relations between rainfall and reproductive response. This research will develop an AI-based modelling framework to measure and predict the influence of rainfall on the reproductive success of amphibians.

Materials & Methods: Observations on rainfall data and amphibian reproductive data go through the process of data cleaning, missing values management, normalization, and feature processing. The parameters taken into consideration as major predicting features include rainfall amount, rainfall frequency, rainfall intensity, and seasonal rainfall patterns while reproductive predictors include breeding and reproductive outputs. Some of the machine learning models that can be considered for prediction include Random Forest, Artificial Neural Network, Gradient Boosting, and Support Vector Regression models. Performance of the models is done using R², RMSE, and MAE.

Results: The comparative modelling shows that the relationship between rainfall and reproduction can be well described through the use of artificial intelligence models. Gradient Boosting yields the highest predictive capacity, yielding an R² of 0.87, RMSE of 0.26 and MAE of 0.19, while Random Forest gives an R² of 0.84. The results imply that rainfall quantity and timing play a vital role in the relationship between rainfall and reproduction. Reproductive reactions could differ in low, optimal and high rainfall environments. The results further suggest that reproductive responses may vary under insufficient, optimal, and excessive rainfall conditions, highlighting the nonlinear nature of rainfall effects.

Conclusion: The suggested framework proves to be an effective means of using machine learning algorithms for predicting the reproductive success of amphibians in case of varying precipitation. It can help to find out which factors of precipitation are important enough to monitor ecology, breed habitats, and make climate-related conservation efforts. Integrating additional environmental variables and longer-term observations could further improve model reliability and ecological applicability