Forecasting surface water level fluctuations of lake Serwy (Northeastern Poland) by artificial neural networks and multiple linear regression. Issue 4 (2nd October 2017)

• Record Type: Journal Article
• Authors: Piasecki, Adam; Jurasz, Jakub; Skowron, Rajmund
• Is Part Of: Journal of environmental engineering and landscape management. Volume 25:Issue 4(2017)
• Page Start: 379
• Page End: 388
• Publication Date: 2017-10-02
• Subjects: ANN -- multiple linear regression -- lake Serwy -- water level -- post-glacial lake; Environmental protection -- Periodicals; Environmental engineering -- Periodicals; Pollution -- Periodicals; Environmental sciences -- Periodicals; Forest landscape management -- Periodicals; Agricultural landscape management -- Periodicals; Agricultural landscape management; Environmental engineering; Environmental protection; Environmental sciences; Forest landscape management; Pollution; Periodicals; 333.705
• DOI: 10.3846/16486897.2017.1303498
• Languages: English
• ISSNs: 1648-6897

Abstract

Abstract: The aim of this study is to assess the possibility of forecasting water level fluctuations in a relatively small (<100 km 2 ), post-glacial lake located in a temperate climate zone by means of artificial neural networks and multiple linear regression. The area of study was Lake Serwy, located in northeastern Poland. Two artificial neural network (ANN) multilayer perceptron (MLP) and multiple linear regression (MLR) models were built. The following explanatory variables were considered: maximal and minimal temperature (Tmax, Tmin) wind speed (WS), vertical circulation (VC) and water level from previous periods (WL). Additionally, a binary variable describing the period of the year (winter, summer) has been considered in one of the two MLP and MLR models. The forecasting models have been assessed based on selected criteria: mean absolute percentage error (MAPE), root mean squared error (RMSE), coefficient of determination (R2) and mean biased error. Considering their values and absolute deviations from observed values it was concluded that the ANN model using an additional binary variable (MLP_B+) has the best forecasting performance. Absolute deviations from observed values were the determining factor which made this model the most efficient. In the case of the MLP_B+ model, those values were about 10% lower than in other models. The conducted analyses indicated good performance of ANN networks as a forecasting tool for relatively small lakes located in temperate climate zones. It is acknowledged that they enable water level forecasting with greater precision and lower absolute deviations than the use of multiple linear regression models.