{'fraction_non_alphanumeric': 0.010716677829872739, 'fraction_numerical': 0.0, 'mean_word_length': 5.610619469026549, '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}}
APPLICATION OF KALMAN FILTER FOR ANALYSIS OF CONTAMINANT TRANSPORT IN GROUND WATER ORIGINATING FROM POINT SOURCES

A sequential stochastic model is developed to identify the locations and strengths of pollution sources in a two-dimensional uniform ground water flow field. The model also estimates the concentration distribution in the flow field due to the presence of pollution sources and the error covariances of the estimation process. The present study focuses on incorporating the measurement errors and modeling errors. The findings presented herein are considered only a first step toward introducing the potential application of system theory to contaminant transport problems in the ground water environment. This paper presents details on the model development and the physical significance of various steps associated with its development. In addition, the following items are discussed in detail: the criteria for selecting initial source covariances, the role of measurement error covariances, the criteria for handling measurement errors when there is insufficient information to define those errors, the functional relationship between covariances and Kalman gain matrices, the model's sensitivity to initial assumptions of covariances, and the limitations of the model developed during the present study. A series of numerical experiments was conducted to support the discussions of the above listed issues for a case with a single point source and a constant injection rate.