Version 1
: Received: 6 May 2024 / Approved: 7 May 2024 / Online: 7 May 2024 (10:32:04 CEST)
How to cite:
Xie, S.; Xiang, Z.; Wang, Y.; Wu, B.; Shen, K.; Wang, J. An Index Used to Evaluate the Applicability of Mid-long Term Runoff Prediction in a Basin Based on Mutual Information. Preprints2024, 2024050365. https://doi.org/10.20944/preprints202405.0365.v1
Xie, S.; Xiang, Z.; Wang, Y.; Wu, B.; Shen, K.; Wang, J. An Index Used to Evaluate the Applicability of Mid-long Term Runoff Prediction in a Basin Based on Mutual Information. Preprints 2024, 2024050365. https://doi.org/10.20944/preprints202405.0365.v1
Xie, S.; Xiang, Z.; Wang, Y.; Wu, B.; Shen, K.; Wang, J. An Index Used to Evaluate the Applicability of Mid-long Term Runoff Prediction in a Basin Based on Mutual Information. Preprints2024, 2024050365. https://doi.org/10.20944/preprints202405.0365.v1
APA Style
Xie, S., Xiang, Z., Wang, Y., Wu, B., Shen, K., & Wang, J. (2024). An Index Used to Evaluate the Applicability of Mid-long Term Runoff Prediction in a Basin Based on Mutual Information. Preprints. https://doi.org/10.20944/preprints202405.0365.v1
Chicago/Turabian Style
Xie, S., Keyan Shen and Jin Wang. 2024 "An Index Used to Evaluate the Applicability of Mid-long Term Runoff Prediction in a Basin Based on Mutual Information" Preprints. https://doi.org/10.20944/preprints202405.0365.v1
Abstract
Accurate and reliable mid-long runoff prediction (MLTRP) is of great importance in water re-sources management. However the MLTRP is not suitable in each basin and how to evaluate the applicability of MLTRP is still a question. Therefore, the total mutual information (TMI) index is developed in this study based on the predictor selection method using mutual information (MI) and partial MI (PMI). The relationship between the TMI and the predictive performance of five models is analyzed by applying five models in 222 forecasting scenarios in Australia. The results over 222 forecasting scenarios demonstrate that, compared with the MI, the developed TMI index can better represent the available information in the predictors, and has more significant negative correlation with the RRMSE with the correlation coefficient between -0.62 and -0.85. This means the model's predictive performance will become better along with the increase of TMI, and there-fore the developed TMI index can be used to evaluate the applicability of MLTRP. When TMI is more than 0.1, the available information in the predictors can support the construction of MLTRP models. In addition, the TMI is used to partly explain the difference of predictive performance among five models. In general, the complex models, which can better utilize the contained infor-mation, are more sensitive to the TMI, and have more significant improvement in terms of predic-tive performance along with the increase of TMI. This research can provide support for the study of MLTRP.
Keywords
mid-long term runoff prediction; total mutual information; applicability evaluation; artificial intelligence models; available information
Subject
Engineering, Other
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
