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AI-Decision Support System: Engineering, Geology, Climate, and Socioeconomic Aspects' Implications on Machine Learning

AI-Decision Support System: Engineering, Geology, Climate, and Socioeconomic Aspects' Implications on Machine Learning

Phan Truong Khanh, Tran Thi Hong Ngoc, Sabyasachi Pramanik
Copyright: © 2024 |Pages: 22
ISBN13: 9798369306390|ISBN13 Softcover: 9798369306437|EISBN13: 9798369306406
DOI: 10.4018/979-8-3693-0639-0.ch008
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MLA

Khanh, Phan Truong, et al. "AI-Decision Support System: Engineering, Geology, Climate, and Socioeconomic Aspects' Implications on Machine Learning." Using Traditional Design Methods to Enhance AI-Driven Decision Making, edited by Tien V. T. Nguyen and Nhut T. M. Vo, IGI Global, 2024, pp. 181-202. https://doi.org/10.4018/979-8-3693-0639-0.ch008

APA

Khanh, P. T., Ngoc, T. T., & Pramanik, S. (2024). AI-Decision Support System: Engineering, Geology, Climate, and Socioeconomic Aspects' Implications on Machine Learning. In T. Nguyen & N. Vo (Eds.), Using Traditional Design Methods to Enhance AI-Driven Decision Making (pp. 181-202). IGI Global. https://doi.org/10.4018/979-8-3693-0639-0.ch008

Chicago

Khanh, Phan Truong, Tran Thi Hong Ngoc, and Sabyasachi Pramanik. "AI-Decision Support System: Engineering, Geology, Climate, and Socioeconomic Aspects' Implications on Machine Learning." In Using Traditional Design Methods to Enhance AI-Driven Decision Making, edited by Tien V. T. Nguyen and Nhut T. M. Vo, 181-202. Hershey, PA: IGI Global, 2024. https://doi.org/10.4018/979-8-3693-0639-0.ch008

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Abstract

From the impact of several corporeal, mechanized, ecological, and civic conditions, underground water pipelines degrade. A motivated administrative approach of the water supply network (WSN) depends on accurate pipe failure prediction that is difficult for the traditional physics-dependent model to provide. The research used data-directed machine learning approaches to forecast water pipe breakdowns using the extensive water supply network's historical maintenance data history. To include multiple contributing aspects to subterranean pipe degradation, a multi-source data-aggregation system was originally developed. The framework specified the requirements for integrating several data sources, such as the classical pipe leakage dataset, the soil category dataset, the geographic dataset, the population count dataset, and the climatic dataset. Five machine learning (ML) techniques are created for predicting pipe failure depending on the data, like LightGBM, ANN, Logistic Regression, K-NN, and SVM algorithm.

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