An integrated intelligent technique for monthly rainfall spatial interpolation in the Northeast region of Thailand

Spatial interpolation is a method to create spatial continuous surface from observed data points. Spatial interpolation is important to water management and planning because it could provide estimation of rainfall at unobserved area. This paper proposes a methodology to analyze and establish an integrated intelligent spatial interpolation model for monthly rainfall data. The proposed methodology starts with determining the optimal number of sub-regions by means of standard deviation analysis and artificial neural networks. Once the optimal number of sub-regions is determined, a Mamdani fuzzy inference system is generated by fuzzy c-means and then optimized by genetic algorithm. Four case studies were used to evaluate the accuracy of the established models and compared with trend surface analysis and artificial neural networks. The experimental results demonstrated that the proposed methodology provided reasonable interpolation accuracy and the methodology gave human understandable fuzzy rules to human analysts

Interpretable fuzzy systems for monthly rainfall spatial interpolation and time series prediction

This thesis proposes methodologies to analyze and establish interpretable fuzzy systems for monthly rainfall spatial interpolation and time series prediction. A fuzzy system has been selected due to its capability of handling the uncertainty in the data and due to its interpretability characteristic. In the first part, this thesis proposes a methodology to analyze and establish interpretable fuzzy models for monthly rainfall spatial interpolation using global and local methods. In the global method, the proposed methodology begins with clustering analysis to de-termine the appropriate number of clusters, and fuzzy modeling and a genetic algorithm are then used to establish the fuzzy interpretation model. In the local method, the modu-lar technique has been applied to improve the accuracy of the global models while the interpretability capability of the model is maintained. In the second part, this thesis proposes a methodology to establish single and modular interpretable fuzzy models for monthly rainfall time series predictions. In the single model, the cooperative neuro-fuzzy technique and a genetic algorithm have been used. In the modular model, the modular technique has been applied to simplify the complexi-ty of the single model. The whole system is decomposed into twelve sub-modules ac-cording to the calendar months. The proposed modular model consists of two function-ally consecutive layers, the prediction layer and the aggregation layer. In the aggregation layer, Bayesian reasoning has been applied. The case study used in this thesis is located in the northeast region of Thailand. The proposed models were compared with commonly-used conventional and intelligent methods in the hydrological discipline. The experimental results showed that, in the quantitative aspect, the proposed models can provide good prediction accuracy and, in the qualitative aspect, the proposed models can also meet the criteria used for model in-terpretability assessment