Accelerated Exploration for Long-Term Urban Water Infrastructure Planning through Machine Learning

Zhang, Junyu; Fu, Dafang; Urich, Christian; Singh, Rajendra Prasad

doi:10.3390/su10124600

Cited by 4 publications

(2 citation statements)

References 45 publications

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“…• forecasting the flow of costs in construction projects [30] increasing the efficiency of design and adaptation of municipal water infrastructure [51].…”

Section: Artificial Neural Network-classifiermentioning

confidence: 99%

Quantitative Risk Assessment in Construction Disputes Based on Machine Learning Tools

2021

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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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“…• forecasting the flow of costs in construction projects [30] increasing the efficiency of design and adaptation of municipal water infrastructure [51].…”

Section: Artificial Neural Network-classifiermentioning

confidence: 99%

Quantitative Risk Assessment in Construction Disputes Based on Machine Learning Tools

2021

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“…Smart urban community governance can provide more convenient and efficient public services and improve the quality of life of community residents. At the same time, to realize sustainable development, smart urban community governance can integrate sustainable development goals into urban community management and promote the green and sustainable development of the city [9][10].…”

Section: Introductionmentioning

confidence: 99%

Urban community governance and machine learning: practice and prospects for intelligent decision making

Zhang

2024

Applied Mathematics and Nonlinear Sciences

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Urban community governance faces unprecedented challenges, but machine learning provides new ideas for it. The objective of this study is to examine the use of machine learning technology in urban community governance to enhance governance efficiency and decision-making quality. The study constructs an artificial neural network intelligent decision support model based on genetic algorithm optimization, which is based on the operational requirements of a smart decision support system. A weighted fuzzy inference network is further developed by the fusion of fuzzy logic and neural networks to enhance the system’s ability to deal with uncertainty and ambiguity. Next, prediction tests were conducted on the development levels of six communities to validate the model’s effectiveness. After 1686 training steps, the error squared SSE drops below 0.2%, according to the results. The composite index’s prediction error was 5.12%, while the minimum error was −1.79%. The predicted rankings of the communities did not change from the actual ones, which was in line with the normal trend, and the algorithmic model achieved better prediction results. This study not only provides a new intelligent decision support tool for community governance but also sets a theoretical and practical foundation for the intelligent development of urban community governance in the future.

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

Lal,

Sharan,

Sharma

et al. 2024

Environ Monit Assess

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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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Accelerated Exploration for Long-Term Urban Water Infrastructure Planning through Machine Learning

Cited by 4 publications

References 45 publications

Quantitative Risk Assessment in Construction Disputes Based on Machine Learning Tools

Quantitative Risk Assessment in Construction Disputes Based on Machine Learning Tools

Urban community governance and machine learning: practice and prospects for intelligent decision making

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

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