ANFIS and RBFNN Efficacy and Timescale Dependence in SPEI-Based Drought Prediction using Meteorological Inputs

Drought is a slow-onset natural disaster that has far-reaching effects on agriculture, water security, and socio-economic systems, especially in climate-vulnerable countries such as Malaysia. It is imperative to predict droughts for prompt mitigation efforts. In this paper, the influence of temporal scale on drought modelling has been put into discussion by analyzing a comparison between two machine learning (ML) models: Adaptive Neuro-Fuzzy Inference System (ANFIS); Radial Basis Function Neural Network (RBFNN) based on Standardized Precipitation Evapotranspiration Index (SPEI) as depicting drought. The SPEI of four timescale categories (SPEI-3, SPEI-6, SPEI-9, and SPEI-12) were calculated weekly and monthly (two different temporal scales) from a 15-year (5,844 observations) set of meteorological records (including precipitation, minimum and maximum temperature, humidity, and mean sea level pressure). Model performance was assessed using the Mean Absolute Error (MAE), Pearson correlation coefficient ( ), and Nash-Sutcliffe efficiency (NSE). It is shown that RBFNN surpassed ANFIS at short-, medium-, and long-term timescales in terms of MAE values irrespective of temporal scale, with weekly having the highest accuracy for longer time intervals (especially SPEI-12). It was observed that, in terms of dealing with complex non-linear relationships as well as temporal granularity, RBFNN outperformed ANFIS where ANFIS showed poor performance because of its rule base expansion and input dimensionality. This research provides evidence that integrating RBFNN with weekly temporal scale data and long-term drought indices would be a more robust apparatus for predicting severe drought in Malaysia. These results also highlight the relevance of properly choosing the temporal granularity to develop data-driven forecasting systems for hydrometeorology applications.