Article
Geochemistry & Geophysics
P. Hill, J. Biggs, V. Ponce-Lopez, D. Bull
Summary: The study compared different time series forecasting methods for seasonal signal prediction and found that SARIMA and sinusoid extrapolation performed better in different time windows, while machine learning methods (LSTM) showed less satisfactory results. Additionally, simple extrapolation of a constant function outperformed more sophisticated time series prediction methods in most cases.
JOURNAL OF GEOPHYSICAL RESEARCH-SOLID EARTH
(2021)
Article
Chemistry, Analytical
Muhammad Asif Nauman, Mahlaqa Saeed, Oumaima Saidani, Tayyaba Javed, Latifah Almuqren, Rab Nawaz Bashir, Rashid Jahangir
Summary: Evapotranspiration (ET) is crucial for efficient water resource management, especially in agriculture, and accurate forecasting relies on large meteorological variables. Internet of Things (IoT) and ensemble-learning-based approach are recommended for data collection and ET forecasting under limited meteorological conditions.
Article
Public, Environmental & Occupational Health
Alfred B. Amendolara, David Sant, Horacio G. Rotstein, Eric Fortune
Summary: The study suggests that short-term flu forecasting can be effectively accomplished using traditional time series analysis techniques. The proposed LSTM-based model outperformed other models and accurately predicted short-term variations in flu infection rates. Temperature was found to be the strongest predictor of seasonal flu infection rates.
Article
Automation & Control Systems
Indrajeet Kumar, Bineet Kumar Tripathi, Anugrah Singh
Summary: Petroleum production forecasting involves predicting fluid production from wells using historical data. Traditional methods and conventional machine learning techniques are time-consuming and have limited forecasting power. In this study, we developed time-series forecast models based on an attention-based long short-term memory network, which outperforms other models for petroleum production forecasting.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2023)
Article
Computer Science, Artificial Intelligence
Suryanshi Mishra, Tinku Singh, Manish Kumar, Satakshi
Summary: This paper focuses on short-term forecasting of cumulative reported incidences and mortality. Mathematical and deep learning models are employed for multivariate time series forecasting, with LSTM model outperforming others in terms of accuracy.
Article
Engineering, Civil
Ryan Solgi, Hugo A. Loaiciga, Mark Kram
Summary: This study demonstrates the superiority of the long short-term memory neural network (LSTM-NN) in groundwater level prediction, highlighting the potential of machine learning in groundwater prediction and stressing the importance of collecting high-quality, long-term groundwater level data for sustainable groundwater management.
JOURNAL OF HYDROLOGY
(2021)
Article
Multidisciplinary Sciences
Nur'atiah Zaini, Lee Woen Ean, Ali Najah Ahmed, Marlinda Abdul Malek, Ming Fai Chow
Summary: Rapid industrialization and urbanization have led to high concentration of air pollutants, causing negative impacts on human health and well-being. Accurate prediction of air pollutant concentration is crucial to mitigate health risks. This study developed a hybrid deep learning model to forecast PM2.5 concentration in an urban area in Malaysia, and compared its performance with other deep learning models.
SCIENTIFIC REPORTS
(2022)
Article
Engineering, Civil
Rajesh Maddu, Indranil Pradhan, Ebrahim Ahmadisharaf, Shailesh Kumar Singh, Rehana Shaik
Summary: This study explores the relevance of large-scale climate phenomenon indices in improving short-term reservoir inflow prediction. A framework combining machine learning algorithms and climate variables is developed, and an ensemble model is created using a weighted voting method. The model consistently outperforms standalone algorithms in predicting high and low flows in two different reservoirs.
JOURNAL OF HYDROLOGY
(2022)
Article
Engineering, Civil
Francesco Granata, Fabio Di Nunno, Giovanni de Marinis
Summary: Prediction of river flow rates is a challenging task due to the high uncertainty associated with basin characteristics, hydrological processes, and climatic factors. This study compares two different daily streamflow prediction models and finds that they have comparable forecasting capabilities. The stacked model based on the Random Forest and Multilayer Perceptron algorithms outperforms the bi-directional LSTM network model in predicting peak flow rates, but is less accurate in forecasting low flow rates. The prediction accuracy of both models decreases as the forecast horizon increases. The length of the time series and the presence of outliers in the data can also affect the accuracy of the prediction models.
JOURNAL OF HYDROLOGY
(2022)
Article
Water Resources
Xin Jing, Jungang Luo, Ganggang Zuo, Xue Yang
Summary: The study focuses on the application of machine learning in precise runoff forecasting in the Han River Basin, Shaanxi Province, China. It explores the importance of explainable artificial intelligence in model interpretation. The Integrated Gradient method is used to analyze the long short-term memory model, and the uncertainty of interpretation results is analyzed from three dimensions: method, model, and input features.
JOURNAL OF HYDROLOGY-REGIONAL STUDIES
(2023)
Article
Energy & Fuels
Xin Tong, Jingya Wang, Changlin Zhang, Teng Wu, Haitao Wang, Yu Wang
Summary: A LSTM-Autoencoder model is proposed to address the weak representation ability and severe loss of time series features in traditional methods for large-scale and complex power load forecasting tasks. Experimental results show that the method outperforms many existing mainstream methods.
Article
Energy & Fuels
Shreya Sajid, Surender Reddy Salkuti, C. Praneetha, K. Nisha
Summary: This paper focuses on short-term wind speed forecasting using time series methods. Various time series forecasting techniques are applied and compared using performance metrics. A novel LSTM-ARIMA model is proposed, which achieves the highest prediction accuracy and the least error metrics at all time scales.
ENERGY SOURCES PART A-RECOVERY UTILIZATION AND ENVIRONMENTAL EFFECTS
(2022)
Article
Computer Science, Interdisciplinary Applications
Yanrui Ning, Hossein Kazemi, Pejman Tahmasebi
Summary: This paper proposes a machine learning-based time series forecasting method for predicting the production performance of unconventional reservoirs. Through the study and comparison of three algorithms, ARIMA and LSTM models are found to perform well and exhibit robustness in predicting oil and gas production in wells across the DJ Basin.
COMPUTERS & GEOSCIENCES
(2022)
Article
Energy & Fuels
Dalal AL-Alimi, Ayman Mutahar AlRassas, Mohammed A. A. Al-qaness, Zhihua Cai, Ahmad O. Aseeri, Mohamed Abd Elaziz, Ahmed A. Ewees
Summary: To achieve accurate energy forecasting, it is important to enhance data distribution and reduce data complexity to deal with weather and political fluctuations. This study introduces a novel method that combines ETR and TLIA models to improve the accuracy of energy forecasts. The TLIA model demonstrates superior performance compared to other models, achieving higher accuracy in various datasets.
Article
Environmental Sciences
Shaomei Yang, Aijia Yuan, Zhengqin Yu
Summary: This paper proposes an improved model for ultra-short-term wind power forecasting using complete ensemble empirical mode decomposition and adaptive noise, as well as an improved whale optimization algorithm. The model achieves accurate prediction of wind power and has been proven to be effective in practical applications.
ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH
(2023)
Article
Computer Science, Artificial Intelligence
Syed Aamir Ali Shah, Lei Zhang, Abdul Bais
Proceedings Paper
Engineering, Multidisciplinary
Jin Zhang, Lei Zhang
Summary: This paper presents the design and implementation of an efficient and accurate SNN on FPGA. It proposes a conversion method to map parameters from ANN to SNN with negligible accuracy loss, and demonstrates FPGA implementation techniques for various functions. The proposed model achieves high power efficiency and accuracy.
2023 IEEE INTERNATIONAL SYSTEMS CONFERENCE, SYSCON
(2023)
Proceedings Paper
Computer Science, Interdisciplinary Applications
Lei Zhang
Summary: The research aims to analyze and optimize a single spiking neuron model for constructing Spiking Neural Network (SNN). The hardware implementation of SNN has advantages of low power consumption and fast speed, making it suitable for battery-powered consumer electronic systems. This paper presents the dynamical analysis of a novel logistic spiking neuron model, provides a derivation for the differential equation of the model, explores the effects of model parameters on neuron dynamics, and demonstrates bifurcation phenomenon by controlling input stimulation current. A trigonometric representation in polar coordinates is also introduced to examine the neuron's periodic property.
2023 IEEE INTERNATIONAL CONFERENCE ON CONSUMER ELECTRONICS, ICCE
(2023)
Proceedings Paper
Computer Science, Artificial Intelligence
Lei Zhang
Summary: The paper presents the design and evaluation of a complex exponential neural network model, which aims to reduce computation and improve efficiency in neural networks by developing a mathematical representation for neural oscillation. The study shows that the difference in oscillation frequencies between two neurons is the dominant parameter that determines the oscillation patterns of the network.
2022 IEEE/WIC/ACM INTERNATIONAL JOINT CONFERENCE ON WEB INTELLIGENCE AND INTELLIGENT AGENT TECHNOLOGY, WI-IAT
(2022)
Proceedings Paper
Computer Science, Theory & Methods
Lei Zhang
Summary: This paper presents a nonlinear dynamic model to forecast the potential upsurge of COVID-19 transmission cases. The developed model can be used to predict case rate trends, prepare healthcare systems, and evaluate the effectiveness of public health measures.
2022 IEEE CANADIAN CONFERENCE ON ELECTRICAL AND COMPUTER ENGINEERING (CCECE)
(2022)
Article
Environmental Studies
Zeineb Behi, Kelvin Tsun Wai Ng, Amy Richter, Nima Karimi, Abhijeet Ghosh, Lei Zhang
Summary: The study investigated solar irradiance and climatic conditions at eight locations on a University campus in Regina, Saskatchewan. It found that solar utilities with automatically adjusting PV receivers could increase energy capture, but solar irradiance was lower in August. Some locations were more susceptible to shadow effects, highlighting the importance of spatial allocations of these small smart disposal bin systems.
ENERGY & ENVIRONMENT
(2022)
Proceedings Paper
Engineering, Electrical & Electronic
Vinay Kumar Reddy Chimmula, Lei Zhang, Dhanya Palliath, Abhinay Kumar
2020 11TH INTERNATIONAL CONFERENCE ON AWARENESS SCIENCE AND TECHNOLOGY (ICAST)
(2020)
Proceedings Paper
Engineering, Biomedical
Lei Zhang
BIOMEDICAL ENGINEERING AND COMPUTATIONAL INTELLIGENCE, BIOCOM 2018
(2020)
Proceedings Paper
Engineering, Biomedical
Lei Zhang
2019 41ST ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC)
(2019)
Article
Computer Science, Artificial Intelligence
Lei Zhang
INTERNATIONAL JOURNAL OF COGNITIVE INFORMATICS AND NATURAL INTELLIGENCE
(2019)
Article
Computer Science, Information Systems
Lei Zhang
Proceedings Paper
Computer Science, Artificial Intelligence
Lei Zhang
2018 17TH IEEE INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND APPLICATIONS (ICMLA)
(2018)
Proceedings Paper
Computer Science, Artificial Intelligence
Lei Zhang
2018 JOINT 7TH INTERNATIONAL CONFERENCE ON INFORMATICS, ELECTRONICS & VISION (ICIEV) AND 2018 2ND INTERNATIONAL CONFERENCE ON IMAGING, VISION & PATTERN RECOGNITION (ICIVPR)
(2018)
Proceedings Paper
Computer Science, Information Systems
Lei Zhang
2018 IEEE 61ST INTERNATIONAL MIDWEST SYMPOSIUM ON CIRCUITS AND SYSTEMS (MWSCAS)
(2018)
Article
Mathematics, Interdisciplinary Applications
Bo Li, Tian Huang
Summary: This paper proposes an approximate optimal strategy based on a piecewise parameterization and optimization (PPAO) method for solving optimization problems in stochastic control systems. The method obtains a piecewise parameter control by solving first-order differential equations, which simplifies the control form and ensures a small model error.
CHAOS SOLITONS & FRACTALS
(2024)
Article
Mathematics, Interdisciplinary Applications
Guram Mikaberidze, Sayantan Nag Chowdhury, Alan Hastings, Raissa M. D'Souza
Summary: This study explores the collective behavior of interacting entities, focusing on the co-evolution of diverse mobile agents in a heterogeneous environment network. Increasing agent density, introducing heterogeneity, and designing the network structure intelligently can promote agent cohesion.
CHAOS SOLITONS & FRACTALS
(2024)
Article
Mathematics, Interdisciplinary Applications
Gengxiang Wang, Yang Liu, Caishan Liu
Summary: This investigation studies the impact behavior of a contact body in a fluidic environment. A dissipated coefficient is introduced to describe the energy dissipation caused by hydrodynamic forces. A new fluid damping factor is derived to depict the coupling between liquid and solid, as well as the coupling between solid and solid. A new coefficient of restitution (CoR) is proposed to determine the actual physical impact. A new contact force model with a fluid damping factor tailored for immersed collision events is proposed.
CHAOS SOLITONS & FRACTALS
(2024)
