LONG RANGE LAKE WATER LEVEL ESTIMATION USING ARTIFICIAL INTELLIGENCE METHODS

Ovaj rad obuhvaća predviđanje vodostaja jezera Beysehir, smještenog u središtu Turske, pomoću umjetne inteligencije (AI) poput neuronskih mreža (NN) i neizrazite logike (FL). Studija razmatra detaljno istraživanje utjecaja trajanja dugoročnog predviđanja na predviđanje vodostaja jezera. Analizirana razdoblja predviđanja su bila 1 dan, 30 dana, 60 dana i 90 dana. Parametri jezera poput kratkovalnog zračenja, ukupne brzine dotjecanja u jezero, ukupne brzine otjecanja iz jezera i ranijih vodostaja jezera činili su ulazni sloj AI konfiguracija. Ova studija je jasno pokazala da se uspješnost predviđanja AI metodama smanjuje s povećanjem razdoblja predviđanja. Također se vidi da se najbolji kriteriji za uspješnost predviđanja dobivaju različitim AI metodama za različita razdoblja predviđanja. Vidljivo je da je generalizirana regresijska neuronska mreža (GRNN) pokazala relativno bolje rezultate u usporedbi s druge dvije umjetne neuronske mreže, tj. radijalnom baznom funkcijom (RBF) i metodom proslijeđivanja prema naprijed s povratnim rasprostiranjem pogreške (FFBP), te metodom prilagodljivog sustava neuro-neizrazitog zaključivanja (ANFIS), za duga razdoblja predviđanja, kao što su 60 i 90 dana. Drugu ukupnu najbolju uspješnost postigla je FFBP.This paper covers the estimation of the water levels of Beysehir Lake, located in middle of Turkey, using the artificial intelligence (AI) such as the neural networks (NN) and the fuzzy logic (FL). The study considers the detailed investigation of the effect of the long-term estimate duration on the lake water level estimation. The analysed estimate ranges were 1 day, 30 days, 60 days and 90 days. The lake parameters such as the shortwave radiation, the lake total inflow rate, the lake total outflow rate and the past lake water levels constituted the input layer of the AI configurations. This study clearly showed that the estimate performance of the AI methods decreases with the increasing estimate range. It is also seen that the best estimate performance criteria are obtained by different AI methods for different estimate ranges. It is seen that the Generalized Regression Neural Network (GRNN) showed relatively superior performance compared with the other two artificial neural networks, i.e. the Radial Basis Function (RBF) and the Feed Forward Back Propagation method (FFBP), and the Adaptive Neuro-Fuzzy Inference System (ANFIS) method, for the long estimation ranges such as 60 and 90 days. The second overall best performance was obtained by FFBP

Trend direction changes of Turkish temperature series in the first half of 1990s

The presented study was concentrated on the trend analysis of the annual mean temperature series of 40 meteorological stations in all climatic zones of Turkey. The sensitivity of the parametric and nonparametric tests to the selected record periods was investigated in detail. Backward-shifted and forward-shifted trend analyses were accomplished by keeping either the beginning or the ending data period constant and varying the other period ending. This analysis resulted with a trend statistic direction turning point at the year 1992. Following this result, the trend tests were applied to three different records to distinguish the effect of 1992 on the trend direction. For the period 1950-1992, the downward trend was dominating several stations whereas only upward trend was observed for 1986-2006 period. Clearly, the trend direction change in 1992 dominated the trend behavior between 1986 and 2006. The opposite trend orientations on 1950-1992 and 1986-2006 periods seem to be neutralized on 1950-2006 period with the majority of the stations showing no trend as the result. This study displays the effect of different lengths of data record on the trend analysis results. It has been clear by this study that a sudden change on trend direction is obvious at the stations above 39 degrees N in Turkey provinces in 1992. These results are conformed to the previous studies related with climate change like temperature, sea level, meteorological observations, and dominant climatic events as North Atlantic Oscillation and El-Ni (n) over tildeo and Southern Oscillation

Investigation of sea level anomalies related with NAO along the west coasts of Turkey and their consistency with sea surface temperature trends

It is well-known that North Atlantic Oscillation (NAO), which is one of the large-scaled climate modes effective in the Northern Hemisphere, has a considerable affect on the water resources and climatic indicators especially in the Mediterranean basin. In recent years, also crucial studies about the sea level rise in relation to climate change have been accelerated. Turkey has about 20 modernized tide gauge stations equipped with permanent GPS receivers and targets to contribute to global sea level rise studies in the future. The aim of this study is to find out the effects of North Atlantic Oscillation on the national shores using the data of four tide-gauge stations located on the Aegean and Mediterranean coasts of Turkey. Implications from these four tide gauges would motivate researches to take into account the effect of NAO in calculating the true sea level rise at the national coasts. While studying the sea level changes, vertical crustal movement has been observed using the data of tide gauge GPS stations, and this situation has been taken into consideration in the evaluation of sea levels. Besides, in order to investigate the influences of thermal expansion on sea levels, sea surface temperature data of the meteorology stations near the tide gauges have been evaluated. The homogeneity of the data sets was analyzed using four statistical tests. As a result, all of the meteorology stations' temperature series and tide gauges' data are subjected to trend detection after the homogeneity analysis. Eventually, the effects of North Atlantic Oscillation on both sea levels and sea surface temperatures have been introduced. The study results indicate high correlation between North Atlantic Oscillation and the sea level and sea surface temperature events. It is seen that the linear correlation between the sea level trends of the considered stations and the sea surface temperature data of the related meteorology stations is considerably significant

Comparison of Rainfall-Runoff Relationship Modeling using Different Methods in a Forested Watershed

The daily rainfall-runoff relationship in an experimental watershed was modeled using a statistical method and an artificial neural network method. The estimations were examined and a performance evaluation was done. It was seen that the ANN method, FFBP (Feed Forward Back Propagation), provided closer flow estimations reproducing the shape of the observed hydrograph more realistic. The superiority of FFBP was reflected in the performance evaluation criteria. The extreme flows, i.e., high and low flows, were relatively better approximated by FFBP indicating its promise as a useful tool for hydrologic studies such as flood modeling. The Rational Method was also used, as a conventional tool, to predict the maximum discharge for selected return periods. It was found to be realistic for the forested watershed under consideration when the C coefficient was taken as 0.20 for the 10-year period

High accuracy monitoring system to estimate forest road surface degradation on horizontal curves

Well-maintained pavements reduce occurring severe accidents on horizontal curves. For this reason, the monitoring and evaluation of pavement conditions are important. This study evaluates pavement conditions considering volumetric degradation or displacement on 11 horizontal curves in forest roads, depending on meteorological conditions, traffic effects, and curve parameters. Within this context, pavement displacement (degradation) was investigated and measured with terrestrial laser scanning (TLS) for a year on a monthly basis. In this study, two multiple regression models were developed to estimate the degradation values of a forest road. According to model 1, which was developed to estimate the loss volume values, the adjusted R-2 was 0.658. For model 2, which was developed to estimate the gain volume values, the adjusted R-2 was 0.490. Validations of models were evaluated with different statistical tests. In conclusion, volumetric degradation can be calculated with TLS-based data. Forest road designers should determine horizontal curve characteristics, taking into consideration the pavement degradation and traffic safety