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Lal A, Sharan A, Sharma K, Ram A, Roy DK, Datta B. Scrutinizing different predictive modeling validation methodologies and data-partitioning strategies: new insights using groundwater modeling case study. ENVIRONMENTAL MONITORING AND ASSESSMENT 2024; 196:623. [PMID: 38880864 DOI: 10.1007/s10661-024-12794-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/16/2023] [Accepted: 06/06/2024] [Indexed: 06/18/2024]
Abstract
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 (R2). 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.
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Affiliation(s)
- Alvin Lal
- 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
| | - Ashneel Sharan
- Dicipline of Civil Engineering, College of Science & Engineering, James Cook University, Townsville, Australia.
- C&R Consulting, Geochemical and Hydrobiological Solutions Pty Ltd, Aitkenvale, Queensland, 4814, Australia.
| | - Krishneel Sharma
- School of Environmental and Life Sciences, College of Engineering, Science and Environment, The University of Newcastle, Callaghan, New South Wales, Australia
| | - Arishma Ram
- School of Agriculture, Geography, Environment, Ocean and Natural Sciences, University of the South Pacific, Laucala Campus, Suva, Fiji
| | - Dilip Kumar Roy
- Irrigation and Water Management Division, Bangladesh Agricultural Research Institute, Joydebpur, Gazipur, 1701, Bangladesh
| | - Bithin Datta
- Dicipline of Civil Engineering, College of Science & Engineering, James Cook University, Townsville, Australia
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Quantitative Risk Assessment in Construction Disputes Based on Machine Learning Tools. Symmetry (Basel) 2021. [DOI: 10.3390/sym13050744] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
A high monetary value of the construction projects is one of the reasons of frequent disputes between a general contractor (GC) and a client. A construction site is a unique, one-time, and single-product factory with many parties involved and dependent on each other. The organizational dependencies and their complexity make any fault or mistake propagate and influence the final result (delays, cost overruns). The constant will of the parties involved results in completing a construction object. The cost increase, over the expected level, may cause settlements between parties difficult and lead to disputes that often finish in a court. Such decision of taking a client to a court may influence the future relations with a client, the trademark of the GC, as well as, its finance. To ascertain the correctness of the decision of this kind, the machine learning tools as decision trees (DT) and artificial neural networks (ANN) are applied to predict the result of a dispute. The dataset of about 10 projects completed by an undisclosed contractor is analyzed. Based on that, a much bigger database is simulated for automated classifications onto the following two classes: a dispute won or lost. The accuracy of over 93% is achieved, and the reasoning based on results from DT and ANN is presented and analyzed. The novelty of the article is the usage of in-company data as the independent variables what makes the model tailored for a specific GC. Secondly, the calculation of the risk of wrong decisions based on machine learning tools predictions is introduced and discussed.
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