A New Multi-Objective Genetic Programming Model for Meteorological Drought Forecasting

Abstract

Drought forecasting is a vital task for sustainable development and water resource management. Emerging machine learning techniques could be used to develop precise drought forecasting models. However, they need to be explicit and simple enough to secure their implementation in practice. This article introduces a novel explicit model, called multi-objective multi-gene genetic programming (MOMGGP), for meteorological drought forecasting that addresses both the accuracy and simplicity of the model applied. The proposed model considers two objective functions: (i) root mean square error and (ii) expressional complexity during its evolution. While the former is used to increase the model accuracy at the training phase, the latter is assigned to decrease the model complexity and achieve parsimony conditions. The model evolution and verification procedure were demonstrated using the standardized precipitation index obtained for Burdur City, Turkey. The comparison with benchmark genetic programming (GP) and multi-gene genetic programming (MGGP) models showed that MOMGGP provides the same forecasting accuracy with more parsimony conditions. Thus, it is suggested to utilize the model for practical meteorological drought forecasting.

A Synergistic Optimization Algorithm with Attribute and Instance Weighting Approach for Effective Drought Prediction in Tamil Nadu

“…In a separate study conducted by Reihanifar [23], a multi-objective genetic algorithm was utilized to optimize both the root mean square error and expressional complexity. This approach aimed to achieve higher accuracy while reducing the complexity of the model.…”