Integrating dynamic modeling and decision support systems for sustainable urban planning in a changing climate

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.

KEYWORDS:

• Climate
• Recurrent Neural Network
• Ant Lion optimization
• future climate prediction
• rainfall rate
• drought rate

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This article does not contain any studies with human participants or animals performed by any of the authors.

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