Data Preprocessing to Enhance Flow Forecasting in a Tropical River Basin

Abstract

Missing hydrometric data is a critical issue for water resources management projects and problems related to flow damage and risk assessment. Though numerous ways can be found in the literature to impute them (i.e. Box-Jenkins models, Linear regression models, case deletion, listwise and pairwise deletion, etc.), not all will render effective on a given dataset. in tropical river basin, it’s still needed to develop proven and simplified methods to deal with hydrometric data missingness and scarcity. This paper presents the analyses including an assessment of the condition of the existing hydrometric data and works related to the way in which the record was treated for flow forecasting purposes and the construction of the artificial neural network (ANN) models used for predicting the flows. The study was led based on 15-min rainfall, water surface elevation and discharge data, derived from the continuous real-time monitoring station located in the del Medio River Basin from the years 2012 to 2016. As a result, the proposed modeling approach followed two modeling methods, one employing the missing data record and the other was used a multiple imputation (MI) technique to impute the missing data and forecast flow for 1, 2 and 4 h ahead under each approach. The statistical metrics results for the two-modeling approaches, suggest the non-imputed data scenario to rule out the imputed data. This means it is recommended to further optimize the MI technique if to be used effectively to fill in the missing required days of measurements for estimating H3 gaps and afterwards to forecast the flow employing multilayer perceptron (MLP), artificial neural networks (ANNs) with 10-fold cross-validation.