Modeling Dissolved Oxygen (DO) Concentration Using Different Neural Network Techniques

The concentration of dissolved oxygen (DO) is important for the healthy functioning of aquatic ecosystems, and a significant indicator of the state of aquatic ecosystems. DO is a parameter frequently used to evaluate the water quality on different reservoirs and watersheds.In this study, two different ANN models, that is, the multilayer perceptron (MLP) and radial basis neural network (RBNN), were developed to estimate DO concentration by using various combinations of daily input variables, pH, discharge (Q), temperature (T), and electrical conductivity (EC) measured by U.S. Geological Survey (USGS). The data of Fountain Creek Stream - Gauging Station (USGS Station No: 07106000) which cover 18 years daily data between 1994-2011 were used. The ANN results were compared with those of the multiple linear regression (MLR). Comparison of the results indicated that the MLP and RBNN performed better than the MLR model. The RBNN model with three inputs which are pH, Q,and T was found to be the best model in estimation of DO concentration according to the root mean square error, mean absolute error and determination coefficient (R2) criteria

Comparison of Mann-Kendall and innovative trend method (Şen trend) for monthly total precipitation (Middle Black Sea Region, Turkey)

The objective of this study is to determine possible trend in annual total precipitation based on Mann–Kendall (MK) and a novel method lately published by Şen. The novel method is used for trend analysis of annual total precipitation data recorded at Sinop, Samsun, Ordu, Corum, Amasya, and Tokat provinces in Turkey. This provinces are located in the central Black Sea region of Turkey. The novel Şen’s trend method is applied to this data. According to the Şen’s trend method, peak and low values of annual total precipitation of the six provinces demonstrate same trends (increasing, decreasing, or trendless time series) with the MK test. The study demonstrates that the Şen method can be used for identifying trend analysis of peak and low values of annual total precipitation data. According to the MK trend test, annual total precipitations demonstrate increasing trend for Sinop, Ordu and Tokat provinces while Şen’s method indicates increasing trend in Sinop, Amasya and Tokat in Turkey. As a result, Şen’s method provides an important advantage in terms of especially in all ranges graphically clarification of the data evaluation phase

Modelling COD concentration by using three different ANFIS techniques

Artificial intelligence (AI) techniques have been successfully performed in many different water resources applications such as rainfall-runoff, precipitation, evaporation, discharge (Q), dissolved oxygen (DO), chemical oxygen demand (COD), biological oxygen demand (BOD), sediment concentration and lake levels by many researchers over the last three decades. In this study, three different adaptive neuro-fuzzy inference system (ANFIS) techniques, ANFIS with fuzzy clustering (ANFIS-FCM), ANFIS with grid partition (ANFIS-GP) and ANFIS with subtractive clustering (ANFIS-SC), were developed to estimate COD concentration by using various combinations of daily input important variables water suspended solids (SS), discharge (Q), temperature (T) and pH. Root mean square error (RMSE), mean absolute error (MAE) and determination coefficient (R2) statistics were used for the comparison criteria. Training, testing and validation phase’s results of the optimal ANFIS models were also graphically compared each other. Comparison of the results indicated that the ANFIS-SC(1,0.3,1) model whose input is water SS was found to be slightly better than the other models in estimation of COD according to the comparison criteria in testing phase. In the validation phase, however, ANFISFCM( 1,3,gauss,1) model performed slightly better than ANFIS-GP(3,trimf,constant,1) and ANFIS-SC(1,0.3,1) models. It can be said that three different ANFIS techniques provide similar accuracy in estimating COD

Damage diagnosis in beam-like structures by artificial neural networks

Applicability of artificial neural networks is examined in determining the natural frequencies of intact beams and crack parameters of damaged beams. Multi-layer perceptron (MLP) and radial basis neural networks (RBNN) are utilized for training and validation of input data. In the first part of the study, the first four frequencies of free vibration are predicted based on beam properties by the networks. Showing the effectiveness of the neural networks in predicting the vibrational frequencies, the second part of the study is carried out. At this stage of the inverse problem, the frequencies and mode shape rotation deviations in addition to beam properties are used as input to the networks to determine the crack parameters. Different hidden nodes, epochs and spread values are tried to find the optimal neural networks that give the lowest error estimates. In both parts of the study, the RBNN model performs better. The robustness of the network models in the presence of noise is also shown. It is shown that the optimal MLP network predicts the crack parameters slightly better in the presence of noise. As a conclusion, the trained RBNN model can be used in health monitoring of beam-like structures as a crack identification algorithm

Streamflow forecasting of Astore River with Seasonal Autoregressive Integrated Moving Average model

Simulation of streamflow is one of important factors in water utilization. In this paper, a linear statistical model i.e. Seasonal Autoregressive Integrated Moving Average model (SARIMA) is applied for modeling streamflow data of Astore River (1974 – 2010). On the basis of minimum Akaike Information Criteria Corrected (AICc) and Bayesian Information Criteria (BIC) values, the best model from different model structures has been identified. For testing period (2004-2010), the prediction accuracy of selected SARIMA model in comparison of auto regressive (AR) is evaluated on basis of root mean square error (RMSE), the mean absolute error (MAE) and coefficient of determination (R2 ). The results show that SARIMA performed better than AR model and can be used in streamflow forecasting at the study site

Application of Time Series Models for Streamflow Forecasting

Precise prediction of the streamflow has a significantly importance in water resources management. In this study, two time series models, Autoregressive Moving Average model (ARMA) Autoregressive Integrated Moving Average model (ARIMA) are used for predicting streamflow. In this research, monthly streamflow from 1974 to 2010 were used. The statistics related to first 28 years were used to train the models and last 7 years were used to forecast. The prediction accuracy of both time series models is examined by comparing root mean square error (RMSE), the mean absolute percentage error (MAPE) and the Nash efficiency (NE). According to the results, ARIMA model performs better than the ARMA time series models. Keywords: Streamflow forecasting, Time series models, ARIMA, ARM

Modeliranje razdiobe napetosti smicanja u prirodnim malim vodotocima metodama mekog računanja

In this study, artificial neural networks (ANNs) and adaptive neuro-fuzzy inference system (ANFIS) were used to estimate shear stress distribution in streams. The methods were applied to the 145 field data gauged from four different sites on the Sarimsakli and Sosun streams in Turkey. The accuracy of the applied models was compared with the multiple-linear regression (MLR). The results showed that the ANNs and ANFIS models performed better than the MLR model in modeling shear stress distribution. The root mean square errors (RMSE) and mean absolute errors (MAE) of the MLR model were reduced by 47% and 50% using ANFIS model in estimating shear stress distribution in the test period, respectively. It is found that the best ANFIS model with RMSE of 3.85, MAE of 2.85 and determination coefficient (R2) of 0.921 in test period is superior to the MLR model with RMSE of 7.30, MAE of 5.75 and R2 of 0.794 in estimation of shear stress distribution, respectively.U ovoj studiji su za procjenu razdiobe napetosti smicanja u vodotocima korištene umjetne neuronske mreže (ANNs) i prilagodljivi neizraziti sustav zaključivanja (ANFIS). Metode su primijenjene na 145 nizova podataka prikupljenih na četiri različite postaje na vodotocima Sarimsakli i Sosun u Turskoj. Točnost primijenjenih modela uspoređena je s točnošću modela višestruke linearne regresije (MLR). Rezultati su pokazali da su oba modela (ANNs i ANFIS) bili bolji u modeliranju raspodjele napetosti smicanja od MLR modela. Pri korištenju ANFIS modela za procjenu raspodjele napetosti smicanja u testnom razdoblju srednje kvadratne pogreške (RMSE) i srednje apsolutne pogreške (MAE) su u odnosu na MLR model bile smanjene za 47%, odnosno 50%. Utvrđeno je da se za testno razdoblje najbolji ANFIS model, s RMSE = 3.85, MAE = 2.85 i koeficijentom određenosti R2 = 0.921, pokazao superiornim u procjeni napetosti smicanja u odnosu na MLR model, s RMSE = 7.30, MAE = 5.75 i R2 = 0.794

Modifying Ritchie equation for estimation of reference evapotranspiration at coastal regions of Anatolia

Evapotranspiration (ET) is of great importance in many disciplines, including irrigation system design, irrigation scheduling and hydrologic and drainage studies. A large number of more or less empirical methods have been developed to estimate the evapotranspiration from different climatic variables. The Food and Agriculture Organization (FAO) rates the Penman- Monteith equation as the major model for estimation of reference (grass) evapotranspiration (ET0) because of the fact that it gives more accurate and consistent results as compared to the other empirical models. However, the main disadvantage of this method is that it cannot be used when the sufficient data are not available. The FAO-56 PM equation requires quite a few independent variables such as solar radiation, air temperature, wind speed, and relative humidity in predicting ET0. Worldwide, the weather stations measuring all these variables are few as the majority measure air temperature only. Therefore, for regions which may not be measuring all these meteorological variables, the temperature based models like Ritchie, Hargreaves-Samani and Thornthwaite equations is necessarily used instead of the FAO-56 PM equation. In this study, the Ritchie equation is applied on the measured data recorded at 158 stations at the Coastal are of Turkey (Mediterranean, Aegean, Marmara and Black Sea regions of Anatolia), and the monthly ET0 values computed by it are observed to be smaller than those given by the Penman-Monteith equation. Next, average values for the coefficients of the Ritchie equation, which are constants originally developed in [6], are recomputed using the ET0 values given by the FAO-56 PM equation at all weather stations in coastal regions of Anatolia (Turkey). The Ritchie equation modified in such manner is observed to yield greater determination coefficients (R2), smaller root mean square errors (MSE), and smaller mean absolute relative errors (MARE) as compared to the original versions of Ritchie equation suggested by [6]. It is concluded that for estimation of reference evapotranspiration at coastal regions of Anatolia where the meteorological measurements are scarce, the modified Ritchie equation can be easily used for estimating the ET0 values

Application of Heuristic Algorithms in Improving Performance of Soft Computing Models for Prediction of Min, Mean and Max Air Temperatures

Traditionally, climate conditions has been one of the influential factors in population growth in worldwide. Hence, predicting these conditions can be an important step to improve life conditions in worldwide. In this study, application of genetic algorithm (GA) and particle swarm algorithm (PSO) were considered as alternatives to available algorithms for training artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) in order to predict air temperature. Therefore, monthly minimum, average and maximum air temperatures of Tehran-Iran station at 64-years (1951-2014) were selected as predicted time-series. Firstly, the most appropriate inputs were selected for models using sensitivity analysis. After that, long-term air temperatures (1 month, 1, 2 and 3 years ahead) were modeled.  Results showed that: 1) the given algorithms had acceptable results in improving the models’ performance in modeling minimum, mean and maximum air temperatures. Also, they could improve the performance of ANN and ANFIS in most of the prediction intervals, 2) ANFIS-GA was selected as the most suitable model so that its average determination coefficient (R2), root mean square errors (RMSE) and mean absolute errors (MAE) were 0.88, 1.41 and 2.52, respectively, 3) the sensitivity analysis provided suitable results in selecting the most appropriate model inputs for forecasting the minimum, mean and maximum air temperatures in different intervals