Artificial Neural Networks for Filling Missing Streamflow Data in Rio do Carmo Basin, Minas Gerais, Brazil

Abstract

Adequate availability of data directly influences the quality of hydrological studies. In this sense, procedures for filling gaps of observations are often applied in order to improve the length of hydrological series. One technique that can be used is the Artificial Neural Network (ANN), which process information from input data creating an output. This study aims to evaluate the application of ANN to fill missing data from monthly average streamflow series at Rio do Carmo Basin in the state of Minas Gerais, Brazil. A 26-years series (from 1989 to 2012) was used for ANN modelling while the two proceeding years, 2013 and 2014, were used to simulate failures pursuant to evaluating the performance of the ANN. The ANN construction was performed by the software WEKA that uses the multilayer perceptron model with sigmoidal activation functions. Four types of ANN were generated: five attributes and two (MLP1) or five (MLP2) neurons; and with three attributes and one (MLP3) or three (MLP4) neurons. The best-fit model to ANN was the MLP1, verified by Pearson correlation coefficients (0.9824), and coefficient of determination r² (0.9646). The model used five attributes, four input data (year, month, streamflow data from Acaiaca and Fazenda Paraíso stations) and one output data (streamflow from Fazenda Oriente station), that considered the temporal variation of streamflow. Hence, the utilization of the ANN generated by the WEKA was adequate and can be considered a simple approach, not requiring great computational programming knowledge.

Comparison of Missing Data Infilling Mechanisms for Recovering a Real-World Single Station Streamflow Observation

“…Moreover, since univariate time series have no other correlated variables except time, these algorithms fail for missing data imputation of univariate time series [4,9]. Different methods are used for the infilling or reconstruction of missing data, including: univariate time series [9,[14][15][16][17][18], hydroclimatic values, and streamflow values [7,[19][20][21][22][23][24][25][26][27], among others. However, some drawbacks from some of these studies have been identified.…”

“…The other drawback is the requirements of multiple variable input data from other sources [7,26,34], which might pose a challenge when climate variables are not readily available or only data from a single streamflow outlet station is available. Many more additional drawbacks are related to the procedure used in the data processing such as hydrological models [27,35] or machine learning models (e.g., artificial neural networks (ANNs)) [2,5,7,22,24,36,37].…”

Robotic Process Automation Extended with Artificial Intelligence Techniques in Power Distribution Utilities

“…Together with an RPA platform, the concept of Robotic Cognitive Automation (RCA) can be included in processes with a complex environment and a great amount of data, It is the use of Artificial Intelligence (AI) techniques: machine learning (ML), data analytics, deep learning, among others, so as to incorporate the emulated human cognition into the solution of complex processes [4]. RPA is supported by the process, while RCA is supported by data and can initially apply machine learning techniques for data analysis and decisionmaking, reaching the application of AI techniques for deductive analysis [6][7][8].…”