ABSTRACT
The study introduces the Ant Lion Recurrent Climate Estimation (ALRCE) model for precise climate forecasting, emphasizing critical parameters like rainfall, humidity, temperature, wind speed, and dew point. Its primary goal is to mitigate natural hazard risks stemming from sudden climate shifts. Data from Surat city, Gujarat, including these features, is meticulously processed to train the ALRCE model. It employs an ant lion fitness function for analysis, particularly predicting rainfall and drought rates. Implemented in MATLAB, it demonstrates superior precision, accuracy, and risk reduction, validated for future climatic predictions from 2025 to 2050. Comparative analysis with existing techniques shows ALRCE outperforms, achieving 96.5% accuracy, 98% precision, 0.18838 RMSE, and 0.63 correlation coefficient. The study enhances accuracy by 2% post-optimization, highlighting ALRCE’s potential to significantly improve climate prediction and mitigate risks associated with abrupt climate changes.
Disclosure statement
No potential conflict of interest was reported by the author(s).
Ethical approval
This article does not contain any studies with human participants or animals performed by any of the authors.
Consent to participate
All the authors involved have agreed to participate in this submitted article.
Consent to publish
All the authors involved in this manuscript give full consent for publication of this submitted article.
Authors contributions
All authors have equal contributions in this work.