Journal Article

Using Optimized Deep Learning to Predict Daily Streamflow: A Comparison to Common Machine Learning Algorithms

Tiefenbacher, John P, Khosravi Khabat, Golkarian Ali

2022

Overview

From a watershed management perspective, streamflow need to be predicted accurately using simple, reliable, and cost-effective tools. Present study demonstrates the first applications of a novel optimized deep-learning algorithm of a convolutional neural network (CNN) using BAT metaheuristic algorithm (i.e., CNN-BAT). Using the prediction powers of 4 well-known algorithms as benchmarks – multilayer perceptron (MLP-BAT), adaptive neuro-fuzzy inference system (ANFIS-BAT), support vector regression (SVR-BAT) and random forest (RF-BAT), the CNN-BAT model is tested for daily streamflow (Qt) prediction in the Korkorsar catchment in northern Iran. Fifteen years of daily rainfall (Rt) and streamflow data from 1997 to 2012 were collected and used for model development and evaluation. The dataset was divided into two groups for building and testing models. The correlation coefficient (r) between rainfall and streamflow with and without antecedent events (i.e., Rt-1, Rt-2, etc.) (as the input variables) and Qt (as the output variable) served as the basis for constructing different input scenarios. Several quantitative and visually-based evaluation metrics were used to validate and compare the model’s performance. The results indicate that Rt was the most effective input variable on Qt prediction and the integration of Rt, Rt-1, and Qt-1 was the optimal input combination. The evaluation metrics show that the CNN-BAT algorithm outperforms the other algorithms. The Friedman and Wilcoxon signed-rank test indicates that the prediction power of CNN-BAT algorithm is significantly/statistically different from the other developed algorithms.

• Publisher: Springer Nature B.V
• Subject: Adaptive systems / Algorithms / Artificial neural networks / Benchmarks / Correlation coefficient / Correlation coefficients / Daily / Deep learning / Fuzzy logic / Heuristic methods / Hydrologic data / Learning algorithms / Machine learning / Model testing / Multilayer perceptrons / Neural networks / Predictions / Rain / Rainfall / Rank tests / Statistical analysis / Stream discharge / Stream flow / Support vector machines / Watershed management / Watersheds