Scrutinizing different predictive modeling validation methodologies and data-partitioning strategies: new insights using groundwater modeling case study.

Autor: Lal A; Global Centre for Environmental Remediation, College of Engineering, Science and Environment, The University of Newcastle, Callaghan, New South Wales, Australia.; CRC for Contamination Assessment and Remediation of the Environment (crcCARE), The University of Newcastle, Callaghan, New South Wales, 2308, Australia., Sharan A; Dicipline of Civil Engineering, College of Science & Engineering, James Cook University, Townsville, Australia. ashneel.sharan@my.jcu.edu.au.; C&R Consulting, Geochemical and Hydrobiological Solutions Pty Ltd, Aitkenvale, Queensland, 4814, Australia. ashneel.sharan@my.jcu.edu.au., Sharma K; School of Environmental and Life Sciences, College of Engineering, Science and Environment, The University of Newcastle, Callaghan, New South Wales, Australia., Ram A; School of Agriculture, Geography, Environment, Ocean and Natural Sciences, University of the South Pacific, Laucala Campus, Suva, Fiji., Roy DK; Irrigation and Water Management Division, Bangladesh Agricultural Research Institute, Joydebpur, Gazipur, 1701, Bangladesh., Datta B; Dicipline of Civil Engineering, College of Science & Engineering, James Cook University, Townsville, Australia.
Jazyk: angličtina
Zdroj: Environmental monitoring and assessment [Environ Monit Assess] 2024 Jun 17; Vol. 196 (7), pp. 623. Date of Electronic Publication: 2024 Jun 17.
DOI: 10.1007/s10661-024-12794-w
Abstrakt: Groundwater salinity is a critical factor affecting water quality and ecosystem health, with implications for various sectors including agriculture, industry, and public health. Hence, the reliability and accuracy of groundwater salinity predictive models are paramount for effective decision-making in managing groundwater resources. This pioneering study presents the validation of a predictive model aimed at forecasting groundwater salinity levels using three different validation methods and various data partitioning strategies. This study tests three different data validation methodologies with different data-partitioning strategies while developing a group method of data handling (GMDH)-based model for predicting groundwater salinity concentrations in a coastal aquifer system. The three different methods are the hold-out strategy (last and random selection), k-fold cross-validation, and the leave-one-out method. In addition, various combinations of data-partitioning strategies are also used while using these three validation methodologies. The prediction model's validation results are assessed using various statistical indices such as root mean square error (RMSE), means squared error (MSE), and coefficient of determination (R 2 ). The results indicate that for monitoring wells 1, 2, and 3, the hold-out (random) with 40% data partitioning strategy gave the most accurate predictive model in terms of RMSE statistical indices. Also, the results suggested that the GMDH-based models behave differently with different validation methodologies and data-partitioning strategies giving better salinity predictive capabilities. In general, the results justify that various model validation methodologies and data-partitioning strategies yield different results due to their inherent differences in how they partition the data, assess model performance, and handle sources of bias and variance. Therefore, it is important to use them in conjunction to obtain a comprehensive understanding of the groundwater salinity prediction model's behavior and performance.
(© 2024. The Author(s).)
Databáze: MEDLINE
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