Smart Warehousing With Predictive Water Usage Insights Using Deep Learning Models

View Sample PDF

Author(s): Usharani Bhimavarapu (Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswara, India)
Copyright: 2026
Pages: 22
Source title: Innovations in Green and Energy-Efficient Warehousing
Source Author(s)/Editor(s): Mohamed Amine Frikha (King Faisal University, Saudi Arabia), Mohieddine Rahmouni (King Faisal University, Saudi Arabia)and Ben Othman Soufiane (King Faisal University, Saudi Arabia)
DOI: 10.4018/979-8-3373-3176-8.ch008

Keywords: Energy / Engineering Science Reference / Nuclear & Energy Engineering / Science & Engineering

Purchase

View Smart Warehousing With Predictive Water Usage Insights Using Deep Learning Models on the publisher's website for pricing and purchasing information.

Abstract

Water conservation in warehousing operations has become a critical concern due to increasing water scarcity and rising operational demands. Warehouses consume water for a variety of purposes including sanitation, cooling systems, fire safety, and landscape maintenance. This study explores the application of intelligent water conservation techniques supported by advanced deep learning models, specifically the Bi-Stacked Gated Recurrent Unit (Bi-Stacked GRU), to forecast water usage patterns and identify optimization opportunities. A dataset comprising 1,195 household-level responses was collected from eight districts and preprocessed for analysis. Feature selection was performed using Particle Swarm Optimization (PSO) to isolate key variables influencing water usage. The Bi-Stacked GRU model enabled accurate prediction of water consumption trends and detection of inefficiencies, allowing for proactive water-saving strategies in warehousing environments

The IRMA Community

Research IRM

Smart Warehousing With Predictive Water Usage Insights Using Deep Learning Models

Purchase

Abstract

Related Content

IRMA Sponsors