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ANSER: an Adaptive-Neuron Artificial Neural Network System for Estimating Rainfall Using Satellite Data
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2007
Year
Artificial IntelligenceEngineeringMachine LearningNeural Networks (Machine Learning)Ai FoundationLearning AlgorithmWeather ForecastingNan ModelsRecurrent Neural NetworkSocial SciencesWater Quality ForecastingNumerical Weather PredictionData ScienceDrought ForecastingHydrological ModelingHydrometeorologyMeteorologyGeographyNeural Networks (Computational Neuroscience)Computer ScienceHydrologyNan ModelDeep Neural NetworksDroughtRemote Sensing
We propose a new neural network model – Neuron-Adaptive artificial neural Network (NAN) – is developed. A learning algorithm is derived to tune both the neuron activation function free parameters and the connection weights between neurons. We proceed to prove that a NAN can approximate any piecewise continuous function to any desired accuracy, then relate the approximation properties of NAN models to some special mathematical functions. A neuron-Adaptive artificial Neural network System for Estimating Rainfall (ANSER) which uses NAN as its basic reasoning network is described. Empirical results show that the NAN model performs about 1.8% better than artificial Neural Network Groups, and around% better than classical Artificial Neural Networks when using a rainfall estimate experimental database. The empirical results also show that by using the NAN model, ANSER plus can (i) automatically compute rainfall amounts ten times faster; and (ii) reduce average errors of rainfall estimates for the total precipitation event to less than 10 per cent.