Modelling daily water level fluctuations of Lake Van (Eastern Turkey) using Artificial Neural Networks

2016 ◽  
Vol 187 (3) ◽  
pp. 177-189 ◽  
Author(s):  
Emrah Doğan ◽  
Uğur Erkin Kocamaz ◽  
Murat Utkucu ◽  
Eray Yıldırım
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Water Level ◽  
Water Level Fluctuations ◽  
Lake Van ◽  
Eastern Turkey ◽  
Daily Water ◽  
Artificial Neural

scholarly journals FORECASTING SURFACE WATER LEVEL FLUCTUATIONS OF LAKE SERWY (NORTHEASTERN POLAND) BY ARTIFICIAL NEURAL NETWORKS AND MULTIPLE LINEAR REGRESSION

2017 ◽  
Vol 25 (4) ◽  
pp. 379-388 ◽  
Author(s):  
Adam PIASECKI ◽  
Jakub JURASZ ◽  
Rajmund SKOWRON
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Water Level ◽  
Water Level Fluctuations ◽  
Temperate Climate ◽  
Binary Variable ◽  
Northeastern Poland ◽  
Artificial Neural

The aim of this study is to assess the possibility of forecasting water level fluctuations in a relatively small (<100 km2), 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.

scholarly journals Application of artificial neural networks (ANN) in Lake Drwęckie water level modelling

2015 ◽  
Vol 15 (1) ◽  
pp. 21-30 ◽  
Author(s):  
Adam Piasecki ◽  
Jakub Jurasz ◽  
Rajmund Skowron
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Water Level ◽  
Meteorological Data ◽  
Water Level Fluctuations ◽  
Explanatory Variables ◽  
Real Value ◽  
The Subject ◽  
Artificial Neural ◽  
Hidden Layer

Abstract This paper presents an attempt to model water-level fluctuations in a lake based on artificial neural networks. The subject of research was the water level in Lake Drwęckie over the period 1980-2012. For modelling purposes, meteorological data from the weather station in Olsztyn were used. As a result of the research conducted, the model M_Meteo_Lag_3 was identified as the most accurate. This artificial neural network model has seven input neurons, four neurons in the hidden layer and one neuron in the output layer. As explanatory variables meteorological parameters (minimal, maximal and mean temperature, and humidity) and values of dependent variables from three earlier months were implemented. The paper claims that artificial neural networks performed well in terms of modelling the analysed phenomenon. In most cases (55%) the modelled value differed from the real value by an average of 7.25 cm. Only in two cases did a meaningful error occur, of 33 and 38 cm.

scholarly journals Climate change or regional human impacts? remote sensing tools, artificial neural networks, and wavelet approaches aim to solve the problem

2020 ◽  
Author(s):  
Ehsan Foroumandi ◽  
Vahid Nourani ◽  
Elnaz Sharghi
Keyword(s):  
Remote Sensing ◽  
Neural Networks ◽  
Time Series ◽  
Artificial Neural Networks ◽  
Water Level ◽  
Positive Trend ◽  
Water Level Change ◽  
Lake Urmia ◽  
Level Change ◽  
Artificial Neural

Abstract Lake Urmia, as the largest lake in Iran, has suffered from water-level decline and this problem needs to be investigated accurately. The major reason for the decline is controversial. The current paper aimed to study the hydro-environmental variables over the Lake Urmia basin using remote sensing tools, artificial neural networks, wavelet transforms, and Mann–Kendall trend tests from 1995 to 2019 in order to determine the primary reason of the decline and to find the most important hydrologic periodicities over the basin. The results indicated that for the monthly-, seasonally-, and annually-based time series, the components with 4-month and 16-month, 24- and 48-month, and 2- and 4-year, respectively, are the most dominant periodicities over the basin. The agricultural increase according to the vegetation index and evapotranspiration and their close relationship with the water-level change indicated that human land-use is the main reason for the decline. The increasing agriculture, in the situations that the precipitation has not increased, caused the inflow runoff to the lake to decline and the remaining smaller discharge is not sufficient to stabilize the water level. Temperature time series, also, has experienced a significant positive trend which intensified the water-level change.

scholarly journals The greatest soda-water lake in the world and how it is influenced by climatic change

1997 ◽  
Vol 15 (11) ◽  
pp. 1489-1497 ◽  
Author(s):  
M. Kadioğlu ◽  
Z. Şen ◽  
E. Batur
Keyword(s):  
Water Level ◽  
Climatic Change ◽  
Lake Level ◽  
Agricultural Land ◽  
Drainage Basin ◽  
Hydrological Cycle ◽  
Water Level Fluctuations ◽  
Economic Losses ◽  
Lake Van ◽  
Eastern Turkey

Abstract. Global warming resulting from increasing greenhouse gases in the atmosphere and the local climate changes that follow affect local hydrospheric and biospheric environments. These include lakes that serve surrounding populations as a fresh water resource or provide regional navigation. Although there may well be steady water-quality alterations in the lakes with time, many of these are very much climate-change dependent. During cool and wet periods, there may be water-level rises that may cause economic losses to agriculture and human activities along the lake shores. Such rises become nuisances especially in the case of shoreline settlements and low-lying agricultural land. Lake Van, in eastern Turkey currently faces such problems due to water-level rises. The lake is unique for at least two reasons. First, it is a closed basin with no natural or artificial outlet and second, its waters contain high concentrations of soda which prevent the use of its water as a drinking or agricultural water source. Consequently, the water level fluctuations are entirely dependent on the natural variability of the hydrological cycle and any climatic change affects the drainage basin. In the past, the lake-level fluctuations appear to have been rather systematic and unrepresentable by mathematical equations. Herein, monthly polygonal climate diagrams are constructed to show the relation between lake level and some meteorological variables, as indications of significant and possible climatic changes. This procedure is applied to Lake Van, eastern Turkey, and relevant interpretations are presented.

Sea water level forecasting using genetic programming and comparing the performance with Artificial Neural Networks

2010 ◽  
Vol 36 (5) ◽  
pp. 620-627 ◽  
Author(s):  
Mohammad Ali Ghorbani ◽  
Rahman Khatibi ◽  
Ali Aytek ◽  
Oleg Makarynskyy ◽  
Jalal Shiri
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Genetic Programming ◽  
Water Level ◽  
Sea Water ◽  
Artificial Neural

Predicting piezometric water level in dams via artificial neural networks

2013 ◽  
Vol 24 (5) ◽  
pp. 1115-1121 ◽  
Author(s):  
Vesna Ranković ◽  
Aleksandar Novaković ◽  
Nenad Grujović ◽  
Dejan Divac ◽  
Nikola Milivojević
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Water Level ◽  
Artificial Neural

Application of self-organising maps and multi-layer perceptron-artificial neural networks for streamflow and water level forecasting in data-poor catchments: the case of the Lower Shire floodplain, Malawi

2014 ◽  
Vol 45 (6) ◽  
pp. 838-854 ◽  
Author(s):  
F. D. Mwale ◽  
A. J. Adeloye ◽  
R. Rustum
Keyword(s):  
Neural Networks ◽  
Developing Countries ◽  
Artificial Neural Networks ◽  
Water Level ◽  
Lead Time ◽  
Flood Forecasting ◽  
Multi Layer Perceptron ◽  
Raw Data ◽  
Artificial Neural ◽  
Forecasting And Warning

With a paradigm shift from flood protection to flood risk management that emphasises learning to live with the floods, flood forecasting and warning have received more attention in recent times. However, for developing countries, the lack of adequate and good quality data to support traditional hydrological modelling for flood forecasting and warning poses a big challenge. While there has been increasing attention worldwide towards data-driven models, their application in developing countries has been limited. A combination of self-organising maps (SOM) and multi-layer perceptron artificial neural networks (MLP-ANN) is applied to the Lower Shire floodplain of Malawi for flow and water level forecasting. The SOM was used to extract features from the raw data, which then formed the basis of infilling the gap-riddled data to provide more complete and much longer records that enhanced predictions. The MLP-ANN was used for the forecasting, using alternately the SOM features and the infilled raw data. Very satisfactory forecasts were obtained with the latter for up to 2-day lead time, with both the Nash–Sutcliffe index and coefficient of correlation being in excess of 0.9. When SOM features were used, however, the lead time for very satisfactory forecasts increased to 5 days.

Sign in / Sign up

Export Citation Format

Share Document