Artificial Neural Networks and Extended Kalman Filter for Easy-to-Implement Runoff Estimation Models

Abstract

Determination of suitable sites for small hydropower projects could offer new opportunities for sustainable developments. However, the non-scalable initial investigation costs are one of the biggest burdens when planning small projects. Moreover, solving a complex problem with only a few available parameters is almost impossible with many traditional models, and lack of data may make many design studies infeasible for remote, hard to access or developing areas. Artificial neural networks (ANNs) could help reduce investigation costs and make many projects feasible to study by acting as input–output mapping algorithms. This study provides an easy to understand and implement method to develop fast ANN-based estimation models using the multilayer perceptron (MLP) neural network and extended Kalman filter (EKF) or gradient descent (GD) as the training algorithm. Also, three approaches to feeding training data to the models were studied. Estimating runoff is an important challenge in water resources engineering, especially for development and operation plans. Therefore, the proposed method is applied for a runoff estimating problem using only easily measured precipitation and temperature. Results of this case study indicate that for a relatively similar performance, ANN models using EKF required a fewer number of neurons and training epochs than GD. Compared to the prior research in this study area, the methods in this study are much easier to understand and implement and are not dependent on data mining techniques or continuous long-term time series. Based on the results, a combination of the proposed data feeding methods and the EKF training algorithm improved estimation models by reducing the number of training epochs and the size of the network.