An intelligent decision support system for groundwater supply management and electromechanical infrastructure controls.

Autor:	Ataei P; Department of Civil Engineering, Birjand University of Technology, Birjand, Iran., Takhtravan A; Department of Civil Engineering, Birjand University of Technology, Birjand, Iran., Gheibi M; Institute for Nanomaterials, Advanced Technologies and Innovation, Technical University of Liberec, 46117, Liberec, Czech Republic.; Faculty of Mechatronics, Informatics, and Interdisciplinary Studies, Technical University of Liberec, Liberec, Czech Republic., Chahkandi B; School of Civil Engineering, University of Tehran, Tehran, Iran., Faramarz MG; Department of Building, Civil, and Environmental Engineering, Concordia University, Montreal, QC, H3G1M8, Canada., Wacławek S; Institute for Nanomaterials, Advanced Technologies and Innovation, Technical University of Liberec, 46117, Liberec, Czech Republic.; Faculty of Mechatronics, Informatics, and Interdisciplinary Studies, Technical University of Liberec, Liberec, Czech Republic., Fathollahi-Fard AM; Département d'Analytique, Opérations et Technologies de l'Information, Université Du Québec à Montréal, B.P. 8888, Succ. Centre-ville, Montréal, QC, H3C 3P8, Canada.; New Era and Development in Civil Engineering Research Group, Scientific Research Center, Al-Ayen University, Nasiriyah, Thi-Qar 64001, Iraq., Behzadian K; School of Computing and Engineering, University of West London, England, UK.
Jazyk:	angličtina
Zdroj:	Heliyon [Heliyon] 2024 Jan 20; Vol. 10 (3), pp. e25036. Date of Electronic Publication: 2024 Jan 20 (Print Publication: 2024).
DOI:	10.1016/j.heliyon.2024.e25036
Abstrakt:	This study presents an intelligent Decision Support System (DSS) aimed at bridging the theoretical-practical gap in groundwater management. The ongoing demand for sophisticated systems capable of interpreting extensive data to inform sustainable groundwater decision-making underscores the critical nature of this research. To meet this challenge, telemetry data from six randomly selected wells were used to establish a comprehensive database of groundwater pumping parameters, including flow rate, pressure, and current intensity. Statistical analysis of these parameters led to the determination of threshold values for critical factors such as water pressure and electrical current. Additionally, a soft sensor was developed using a Random Forest (RF) machine learning algorithm, enabling real-time forecasting of key variables. This was achieved by continuously comparing live telemetry data to pump design specifications and results from regular field testing. The proposed machine learning model ensures robust empirical monitoring of well and pump health. Furthermore, expert operational knowledge from water management professionals, gathered through a Classical Delphi (CD) technique, was seamlessly integrated. This collective expertise culminated in a data-driven framework for sustainable groundwater facilities monitoring. In conclusion, this innovative DSS not only addresses the theory-application gap but also leverages the power of data analytics and expert knowledge to provide high-precision online insights, thereby optimizing groundwater management practices. Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. (© 2024 Published by Elsevier Ltd.)
Databáze:	MEDLINE
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