The bio-chemical and physical characteristics of a river are directly affected by water temperature, which thereby affects the overall health of aquatic ecosystems. It is a complex problem to accurately estimate water temperature. Modelling of river water temperature is usually based on a suitable mathematical model and field measurements of various atmospheric factors. In this article, the air–water temperature relationship of the Missouri River is investigated by developing three different machine learning models (Artificial Neural Network (ANN), Gaussian Process Regression (GPR), and Bootstrap Aggregated Decision Trees (BA-DT)). Standard models (linear regression, non-linear regression, and stochastic models) are also developed and compared to machine learning models. Analyzing the three standard models, the stochastic model clearly outperforms the standard linear model and nonlinear model. All the three machine learning models have comparable results and outperform the stochastic model, with GPR having slightly better results for stations No. 2 and 3, while BA-DT has slightly better results for station No. 1. The machine learning models are very effective tools which can be used for the prediction of daily river temperature.

Measurements made in the past few decades undeniably indicate change in the climate. The most visible sign of global climate change is air temperature, while less visible indicators include changes in river water temperatures. Changes in river temperature can significantly affect the environment, primarily the biosphere. The physical, biological and chemical characteristics of the river are directly affected by water temperature, although estimation of this relationship presents a complex problem. Although river temperature is influenced by hydrological and meteorological factors, the purpose of this study is to model daily water temperature using only one known parameter, mean air temperature. The relationship between the daily mean air and daily water temperature of the River Drava in Croatia is analysed using linear regression, stochastic modelling or nonlinear regression and multilayer perceptron (MLP) feed-forward neural networks. The results indicate that the MLP models are much better models which can be used for the estimation and prediction of daily mean river temperature.

In parallel with the increase in vehicle sales worldwide, waste tires are becoming an increasing problem. The storage and disposal of these waste tires are critical environmental problems. Re-using these wastes in different areas instead of being disposed of is vital in preventing environmental pollution and creating new low-cost products. From this motivation, this paper investigates the properties of traditional reinforced-concrete beam with recycled steel wires (RSWT) obtained from the waste tires. RSWT were added to reinforced-concrete beam between 1% and 3% by weight with an increment of 1%. In total, 9 cubes, 12 cylinders and 12 reinforced-concrete beams were cast and tested to obtain the compressive, splitting tensile and flexural strengths, respectively. RSWT added to the concrete by 1%, 2% and 3% increased the compressive strength by 17.2%, 30.8% and 46.4%, respectively, compared to the reference concrete. In split tensile strength, 14.4%, 25.1% and 36.7% increases were observed, respectively. This showed that there was an effective increase in the compressive and tensile strength of concrete with the increase of fiber content. Although the effect of fiber content in samples with high stirrup spacing (27 cm) provides significant benefit in improving the beam behavior, the effect of fibers was more limited as the stirrup spacing decreased (20 cm and 16 cm). An approximation of over 91% was obtained between the analytical calculations and the experimental results. This shows that the analytical calculations given in the standards can be used for new experimental studies.