{'fraction_non_alphanumeric': 0.015517241379310345, 'fraction_numerical': 0.005172413793103448, 'mean_word_length': 5.384615384615385, '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}}
Forest Fire Risk Forecast Method with Pseudo Label Based on Semi-supervised Learning

In this paper, we introduce a forecast method to the forest fire risk with pseudo label based on semi-supervised learning with meteorological factors input. Compared with the classical machine learning methods based on supervised learning, our method can improve the generalization performance in case of being exposed to unfamiliar circumstances through utilizing unlabeled data, and it has improved the accuracy by 4.61% compared with supervised learning with the same amount of labeled data.