Analytics of Radioactive Materials Released in the Fukushima Daiichi Nuclear Accident
- Nuclear Engineering and Radiological Science Center, Alabama A and M University, Huntsville, AL (United States)
- Nuclear Engineering Department, University of Tennessee, Knoxville, TN (United States)
The 2011 Fukushima Daiichi nuclear accident in Japan resulted in the release of radioactive materials into the atmosphere, the nearby sea, and the surrounding land. Following the accident, several meteorological models were used to predict the transport of the radioactive materials to other continents such as North America and Europe. Also of high importance is the dispersion of radioactive materials locally and within Japan. Based on the International Atomic Energy Agency (IAEA) Convention on Early Notification of a nuclear accident, several radiological data sets were collected on the accident by the Japanese authorities. Among the radioactive materials monitored, are I-131 and Cs-137 which form the major contributions to the contamination of drinking water. The radiation dose in the atmosphere was also measured. It is impractical to measure contamination and radiation dose in every place of interest. Therefore, modeling helps to predict contamination and radiation dose. Some modeling studies that have been reported in the literature include the simulation of transport and deposition of I-131 and Cs-137 from the accident, Cs-137 deposition and contamination of Japanese soils, and preliminary estimates of I-131 and Cs-137 discharged from the plant into the atmosphere. In this paper, we present statistical analytics of I-131 and Cs-137 with the goal of predicting gamma dose from the Fukushima Daiichi nuclear accident. The data sets used in our study were collected from the IAEA Fukushima Monitoring Database. As part of this study, we investigated several regression models to find the best algorithm for modeling the gamma dose. The modeling techniques used in our study include linear regression, principal component regression (PCR), partial least square (PLS) regression, and ridge regression. Our preliminary results on the first set of data showed that the linear regression model with one variable was the best with a root mean square error of 0.0133 μSv/h, compared to 0.0210 μSv/h for PCR, 0.231 μSv/h for ridge regression L-curve, 0.0856 μSv/h for PLS, and 0.0860 μSv/h for ridge regression cross validation. Complete results using the full datasets for these models will also be presented. (authors)
- Research Organization:
- Institute of Electrical and Electronics Engineers - IEEE, 3 Park Avenue, 17th Floor, New York, N.Y. 10016-5997 (United States)
- OSTI ID:
- 22531341
- Report Number(s):
- ANIMMA-2015-IO-293; TRN: US16V0501102282
- Resource Relation:
- Conference: ANIMMA 2015: 4. International Conference on Advancements in Nuclear Instrumentation Measurement Methods and their Applications, Lisboa (Portugal), 20-24 Apr 2015; Other Information: Country of input: France; 4 Refs.
- Country of Publication:
- United States
- Language:
- English
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Related Subjects
54 ENVIRONMENTAL SCIENCES
ALGORITHMS
CESIUM 137
COMPARATIVE EVALUATIONS
CONTAMINATION
DIAGRAMS
FUKUSHIMA DAIICHI NUCLEAR POWER STATION
IODINE 131
LEAST SQUARE FIT
RADIATION ACCIDENTS
RADIATION DOSES
RADIATION MONITORING
RADIOACTIVE MATERIALS
REACTOR ACCIDENTS
SOILS
VALIDATION