{'fraction_non_alphanumeric': 0.015017667844522967, 'fraction_numerical': 0.0, 'mean_word_length': 6.08125, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
A Novel Nonlinear Ensemble Rainfall Forecasting Model Incorporating Linear and Nonlinear Regression

In this paper, we propose a novel nonlinear ensemble rainfall forecasting model integrating generalized linear regression with artificial neural networks (ANNs). In this model, using different linear regression extract linear characteristics of rainfall system. Then using different ANNs algorithms and different network architecture extract nonlinear characteristics of rainfall system. Thirdly, the principal component analysis (PCA) technology is adopted to extract ensemble members. Finally, the support vector machine regression (SVMR) is used for nonlinear ensemble model. Empirical results obtained reveal that the prediction by using the nonlinear ensemble model is generally better than those obtained using other models presented in this study in terms of the same evaluation measurements. Our findings reveal that the nonlinear ensemble model proposed here can be used as an alternative forecasting tool for a Meteorological application in achieving greater forecasting accuracy and improving prediction quality further.