Rainfall Prediction in the Northeast Region of Thailand using Cooperative Neuro-Fuzzy Technique
Accurate rainfall forecasting is a crucial task for reservoir operation and flood prevention because it can provide an extension of lead-time for flow forecasting. This study proposes two rainfall time series prediction models, the Single Fuzzy Inference System and the Modular Fuzzy Inference System, which use the concept of cooperative neuro-fuzzy technique. This case study is located in the northeast region of Thailand and the proposed models are evaluated by four monthly rainfall time series data. The experimental results showed that the proposed models could be a good alternative method to provide both accurate results and human-understandable prediction mechanism. Furthermore, this study found that when the number of training data was small, the proposed model provided better prediction accuracy than artificial neural networks
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