{'fraction_non_alphanumeric': 0.013584117032392894, 'fraction_numerical': 0.0, 'mean_word_length': 5.942028985507246, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Modelling of Bektas Creek Daily Streamflow with Generalized Regression Neural Network Method

The dependence of agricultural production on water resources is a known fact. Therefore, understanding hydrological processes and events in agricultural production form the basis of effective and reliable management of water resources. Many traditional methods used for the analysis of time-dependent variables in hydrology and meteorology assume linear relationships between these variables. However, the temporal changes of these parameters are quite complex and therefore cannot be easily modeled by conventional estimation methods. Artificial neural networks (ANNs), on the other hand, allow the analysis of nonlinear relationships or processes whose statistical or mathematical calculations cannot be determined in such systems. ANNs have been accepted as a successful model in multidimensional research involving dynamic processes in the field of hydrology.