Water plays a pivotal role in many physical processes, and most importantly in sustaining human life, animal life and plant life. Water supply entities therefore have the responsibility to supply clean and safe water at the rate required by the consumer. It is therefore necessary to implement mechanisms and systems that can be employed to predict both short-term and long-term water demands. The increasingly growing field of computational intelligence techniques has been proposed as an efficient tool in the modelling of dynamic phenomena. The primary objective of this paper is to compare the efficiency of two computational intelligence techniques in water demand forecasting. The techniques under comparison are the Artificial Neural Networks (ANNs) and the Support Vector Machines (SVMs). In this study it was observed that the ANNs perform better than the SVMs. This performance is measured against the generalisation ability of the two.
Integrating Deep Learning Demand Forecasting with Multi-Objective Optimization for Circular Coffee Supply Chains: A Data-Driven Framework for Cost, Emissions, and Freshness Management
Ankara Yıldırım Beyazıt University（安卡拉耶尔德勒姆贝亚泽特大学）；Texas Tech University（德克萨斯理工大学）；University of Alabama（阿拉巴马大学）
A Novel Deep Neural Network Architecture for Real-Time Water Demand Forecasting
School of Automation and Electrical Engineering, University of Science and Technology Beijing（自动化与电气工程学院，科学技术大学）；Institute of Artificial Intelligence, University of Science and Technology Beijing（人工智能研究院，科学技术大学）；Bogazici University, Istanbul Turkey（博雅大学，伊斯坦布尔土耳其）
Hybrid Deep Learning Approach for Coupled Demand Forecasting and Supply Chain Optimization
organization= Department of Information Technology, Washington University of Science；organization= Department of Information Technology, University of the Potomac , addressline= , city= Washington, D.C. , postcode= 20005 , state= DC , country= USA；organization= Department of Computer Science, Campbellsville University , addressline= , city= Louisville , postcode= 40220 , state= KY , country= USA；organization= Center for Advanced Analytics (CAA), COE for Artificial Intelligence, Faculty of Engineering \& Technology (FET), Multimedia University , addressline= , city= Melaka , postcode= 75450 , state= , country= Malaysia；organization= American International University - Bangladesh (AIUB) , addressline= Kuratoli, Khilkhet , city= Dhaka , postcode= 1229 , country= Bangladesh
Data Pre-Processing and Evaluating the Performance of Several Data Mining Methods for Predicting Irrigation Water Requirement
School of Environmental Sciences, Charles Sturt University（查尔斯斯特大学环境科学学院）；School of Computing and Mathematics, Charles Sturt University（查尔斯斯特大学计算与数学学院）；International Water Management Institute (IWMI)（国际水资源管理研究所）
Probabilistic electrical power demand forecasting with uncertainty quantification
Nepal College of Information Technology（尼泊尔信息技术学院）；Nepal Telecommunications Authority（尼泊尔电信管理局）；Nepal Electricity Authority（尼泊尔电力局）；University of Bergen（卑尔根大学）；Western Norway University of Applied Sciences（挪威西部应用科学大学）
Cross-household Transfer Learning Approach with LSTM-based Demand Forecasting
Department of Mathematics and Computer Science（数学与计算机科学系）；Karlstad University, Sweden（卡尔斯塔德大学，瑞典）；Department of Research and Development（研究与发展系）；Thermia AB, Sweden（Thermia AB，瑞典）；American University of Bahrain, Bahrain（巴林美国大学，巴林）
Neuroplasticity-inspired dynamic ANNs for multi-task demand forecasting
Institute of Theoretical and Applied Informatics（理论与应用信息学院）；Polish Academy of Sciences（波兰科学院）；School of Information Technology（信息科技学院）；Halmstad University（哈姆斯塔德大学）
Job-SDF: A Multi-Granularity Dataset for Job Skill Demand Forecasting and Benchmarking
University of Science and Technology of China（中国科学技术大学）；Computer Network Information Center, Chinese Academy of Sciences（中国科学院计算机网络信息中心）；Baidu Inc.（百度公司）；Institute of Artificial Intelligence, Beihang University（北京航空航天大学人工智能研究院）；AI Thrust, The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州）AI 分支）
MCDFN: Supply Chain Demand Forecasting via an Explainable Multi-Channel Data Fusion Network Model
Khulna University of Engineering and Technology (KUET)（库尔纳工程技术大学）；Okinawa Institute of Science and Technology Graduate University (OIST)（冲绳科学技术大学院大学）；Hamad Bin Khalifa University（哈马德·本·哈利法大学）
