FLOOD FORECASTING USING TRANSBOUNDARY DATA WITH THE FUZZY INFERENCE SYSTEM: THE MARITZA (MERIC) RIVER

In the present study, in order to predict the current flow of the Kirişhane station (Turkey) from the transboundary data of Plovdiv and Svilengrad stations (Bulgaria), four different models (M1‒M4) were developed by using the fuzzy inference system (FIS) for different number of membership functions (MFs) (i.e. 13, 25, and 49 MFs). In addition, multiple linear regression (MLR) was selected as simpler data driven forecasting method to show how FIS improves the other simpler forecasting models. Flow data from the Plovdiv, Svilengrad and Kirişhane stations were gauged at two hour-intervals covering the period from 9 February 2010 00:00:00 to 21 February 2010 22:00:00. In addition, flow data at two hour-intervals covering the flood period from 6 February 2012 14:00:00 to 13 February 2012 10:00:00 were obtained to test developed FIS and MLR models. In the first model, estimation was made using the current flows of the Plovdiv and Svilengrad stations. In the second model, estimation was made based on a two hour ahead prediction of the Svilengrad station and a four hour ahead prediction of the Plovdiv station. In the third model, calculations were based on predictions of four hours ahead of the Svilengrad station and eight hours ahead of the Plovdiv station. In the last model, estimation was based on predictions of six hours ahead of the Svilengrad station and twelve hours ahead of the Plovdiv station.The performance of the developed FIS models and MLR was evaluated by using the mean absolute error (MAE), the Nach-Sutcliffe model efficiency coefficient (NSMEC), and the normalized root mean square error (NRMSE). According to the performance criteria of the models, FIS model with 49 number of MFs provided highest accuracy. When FIS models with 25 MFs and 49 MFs are compared with respect to performance criteria for 2010 data (training data), NSMEC values are close to each other, but MAE values of FIS models with 49 MFs were obtained less than FIS model with 25 MFs. Even though NSMEC values were obtained close to each other for FIS models with 25 MFs and 49 MFs, NSMEC values of FIS model with 25 MFs were obtained less than 0.90 for 2012 data (validation data). With respect to MLR, all models failed to predict 2012 data, even NSMEC values of MLR model were obtained higher than 0.85 for training data. Even the accuracy of FIS models decrease based on decrease in the number of MFs, all FIS models provided better prediction of 2012 data than MLR.