Neural Network Embedded Monte Carlo Approach for Water Quality Modeling under Input Information Uncertainty

摘要

This paper proposes a neural network embedded Monte Carlo (NNMC) approach to account for uncertainty tin water quality modeling. The framework of the proposed method has three major parts: a numerical water quality model, a neural network technique, and Monte Carlo simulation. The numerical model is used to generate desirable output for training and testing sets, and the neural network is Used as a universal functional mapping tool to approximate the input-output response of the numerical model.