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The protection of high quality fresh water in times of global climate changes is of tremendous importance since it is the key factor of local demographic and economic development. One such fresh water source is Vrana Lake, located on the completely karstified Island of Cres in Croatia. Over the last few decades a severe and dangerous decrease of the lake level has been documented. In order to develop a reliable lake level prediction, the application of the artificial neural networks (ANN) was used for the first time. The paper proposes timeseries forecasting models based on the monthly measurements of the lake level during the last 38 years, capable to predict 6 or 12 months ahead. In order to gain the best possible model performance, the forecasting models were built using two types of ANN: the Long-Short Term Memory (LSTM) recurrent neural network (RNN), and the feed forward neural network (FFNN). Instead of classic lagged data set, the proposed models were trained with the set of sequences with different length created from the time series data. The models were trained with the same set of the training parameters in order to establish the same conditions for the performance analysis. Based on root mean squared error (RMSE) and correlation coefficient (R) the performance analysis shown that both model types can achieve satisfactory results. The analysis also revealed that regardless of the model types, they outperform classic ANN models based on datasets with fixed number of features and one month the prediction period. Analysis also revealed that the proposed models outperform classic time series forecasting models based on ARIMA and other similar methods .ANN is one of the basic methods of machine learning (ML) and artificial intelligence (AI), which uses the concept of supervised learning in solving various problems, such as Bahrudin Hrnjica

SummaryThe paper presents the process of two-dimensional axisymmetric quenching of cylindrical samples in water at 40 C. Experimental work consists of quenching three dimensionally different cylindrical probes. The dimensions of the probes are: 25x50, 50x150 and 75x225 mm. Three measuring points are 1.5 mm below the cylinder surface positioned at the cylinder height, whereas the fourth measuring point is in the center of gravity of the cylinder. The quenching was conducted in strict controlled rate of water flow at the cylinder head. The problem of the task belongs to inverse problems of heat conduction, or ill posed problems, so that the solution to the problem leads to a sufficiently accurate estimate of the unknown heat transfer coefficient, imposed on the outer surface of the cylinder. Computer model solutions use experimental results of temperature measurements to minimize errors between computer calculations and measured temperature values in the same place. The selected solution algorithm is a hybrid algorithm that consists of a combination of three different algorithms of solution, connected into a single unit.

This paper describes an example of an explainable AI (Artificial Intelligence) (XAI) in a form of Predictive Maintenance (PdM) scenario for manufacturing. Predictive maintenance has the potential of saving a lot of money by reducing and predicting machine breakdown. In this case study we work with generalized data to show how this scenario could look like with real production data. For this purpose, we created and evaluated a machine learning model based on a highly efficient gradient boosting decision tree in order to predict machine errors or tool failures. Although the case study is strictly experimental, we can conclude that explainable AI in form of focused analytic and reliable prediction model can reasonably contribute to prediction of maintenance tasks.