The aim of this study was to analyse the win, draw, and loss outcomes of soccer matches with situational variables and performance indicators. Data from group stage matches spanning the ten years between the 2010/2011 and 2019/2020 seasons in the European Champions League, were used. One-way analysis of variance (ANOVA) and Tukey HSD (honestly significant difference) tests indicated performance indicators which affected the outcome of matches. K-mean clustering, with statistically significant variables, categorized the quality of the opposition into three clusters: weak, balanced, and strong. Multidimensional scaling (MDS) and decision tree analysis were applied to each of these clusters, highlighting that performance indicators of the teams differed according to the quality of their opponent. Furthermore, according to the decision tree analysis, certain performance indicators, including scoring first and shots on target, increased the chances of winning regardless of the quality of the opposition. Finally, particular performance indicators increased the chance of winning, while others decreased this, in accordance with the quality of the opposition. These findings can help coaches develop different strategies, before or during the match, based on the quality of opponents, situational variables, and performance indicators.
In this study, a hybrid model combining discrete wavelet transforms (WTs) and artificial neural networks (ANNs) is used to estimate the monthly streamflow. The WT-ANN hybrid model was developed using the Daubechies main wavelet to predict the streamflow for three gauging stations on the Çoruh river basin one month in advance, with different combinations of air temperature, precipitation, and streamflow variables, and their wavelet transformations. Four different hybrid WT-ANN models were generated and compared with four different conventional ANN models. The dataset was chronologically divided into training, validation, and testing data. The results indicated that the WT-ANN hybrid models performed better than the traditional ANN models for all three stations. Furthermore, the chronologically divided dataset was used to examine the effects of changes in hydrological data over time on model performance. In conclusion, model performances in the training period deteriorated during the validation and testing periods due to structural changes in the hydrological data.
The Euphrates River Basin system is of vital importance for water, food, energy security in Turkey, Syria, Iraq and under adverse climate change impact. It is therefore this paper mainly focuses on the naturally changing trend in long term streamflow with temperature and precipitation on the Upper Euphrates Basin that lies in Turkey. Because of the complexity of the setting up a basin-wide comprehensive hydrological model in the basin which have several large dams under operation ,we mainly used "run off data" in the statistical regression and time series analyses to predict the long term streamflow trends. We conclude that the annual natural flow has decreased mainly due to natural factors not because of construction dams. Obtained long term precipitation and air temperature trends in study area support the obtained streamflow trends. Results obtained from this study indicate that collaborative approach on transboundary water management between riparian's is an urgent need.
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