Assessment of Machine Learning Techniques for Monthly Flow Prediction

Abstract

Monthly flow predictions provide an essential basis for efficient decision-making regarding water resource allocation. In this paper, the performance of different popular data-driven models for monthly flow prediction is assessed to detect the appropriate model. The considered methods include feedforward neural networks (FFNNs), time delay neural networks (TDNNs), radial basis neural networks (RBFNNs), recurrent neural network (RNN), a grasshopper optimization algorithm (GOA)-based support vector machine (SVM) and K-nearest neighbors (KNN) model. For this purpose, the performance of each model is evaluated in terms of several residual metrics using a monthly flow time series for two real case studies with different flow regimes. The results show that the KNN outperforms the different neural network configurations for the first case study, whereas RBFNN model has better performance for the second case study in terms of the correlation coefficient. According to the accuracy of the results, in the first case study with more input features, the KNN model is recommended for short-term predictions and for the second case with a smaller number of input features, but more training observations, the RBFNN model is suitable.

The Applicability of LSTM-KNN Model for Real-Time Flood Forecasting in Different Climate Zones in China

“…(3) Estimation of errors. The weights of each historical error were calculated using the inverse distance weighted method as in Equation (9), and the errors at time t were determined by the weighted average of the number of k simulation errors.…”

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Estimating Daily Dew Point Temperature Using Machine Learning Algorithms

“…Furthermore, R 2 is a statistical tool for determining the type and degree of the relationship of a variable with other variables. This coefficient varies from 0 to 1; when there is no relationship between two variables, its value is equal to zero [29,30]. Furthermore, Taylor diagrams [31] were used to check the accuracy of the mentioned models.…”

Subdaily Rainfall Estimation through Daily Rainfall Downscaling Using Random Forests in Spain

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