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Bordbar M, Heggy E, Jun C, Bateni SM, Kim D, Moghaddam HK, Rezaie F. Comparative study for coastal aquifer vulnerability assessment using deep learning and metaheuristic algorithms. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2024; 31:24235-24249. [PMID: 38436856 DOI: 10.1007/s11356-024-32706-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/12/2023] [Accepted: 02/26/2024] [Indexed: 03/05/2024]
Abstract
Coastal aquifer vulnerability assessment (CAVA) studies are essential for mitigating the effects of seawater intrusion (SWI) worldwide. In this research, the vulnerability of the coastal aquifer in the Lahijan region of northwest Iran was investigated. A vulnerability map (VM) was created applying hydrogeological parameters derived from the original GALDIT model (OGM). The significance of OGM parameters was assessed using the mean decrease accuracy (MDA) method, with the current state of SWI emerging as the most crucial factor for evaluating vulnerability. To optimize GALDIT weights, we introduced the biogeography-based optimization (BBO) and gray wolf optimization (GWO) techniques to obtain to hybrid OGM-BBO and OGM-GWO models, respectively. Despite considerable research focused on enhancing CAVA models, efforts to modify the weights and rates of OGM parameters by incorporating deep learning algorithms remain scarce. Hence, a convolutional neural network (CNN) algorithm was applied to produce the VM. The area under the receiver-operating characteristic curves for OGM-BBO, OGM-GWO, and VMCNN were 0.794, 0.835, and 0.982, respectively. According to the CNN-based VM, 41% of the aquifer displayed very high and high vulnerability to SWI, concentrated primarily along the coastline. Additionally, 32% of the aquifer exhibited very low and low vulnerability to SWI, predominantly in the southern and southwestern regions. The proposed model can be extended to evaluate the vulnerability of various coastal aquifers to SWI, thereby assisting land use planers and policymakers in identifying at-risk areas. Moreover, deep-learning-based approaches can help clarify the associations between aquifer vulnerability and contamination resulting from SWI.
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Affiliation(s)
- Mojgan Bordbar
- Department of Environmental, Biological and Pharmaceutical Sciences and Technologies, University of Campania "Luigi Vanvitelli", Via Vivaldi 43, 81100, Caserta, Italy
| | - Essam Heggy
- Department of Electrical and Computer Engineering, Ming Hsieh, University of Southern California, 3737 Watt Way, PHE 502, Los Angeles, CA, 90089-0271, USA
- NASA Jet Propulsion Laboratory, California Institute of Technology, 4800 Oak Grove Drive, Pasadena, CA, 91109, USA
| | - Changhyun Jun
- Department of Civil and Environmental Engineering, College of Engineering, Chung-Ang University, Seoul, 06974, Republic of Korea
| | - Sayed M Bateni
- Department of Civil, Environmental, and Construction Engineering and Water Resources Research Center, University of Hawai'i at Manoa, Honolulu, HI, 96822, USA
| | - Dongkyun Kim
- Department of Civil Engineering, Hongik University, Mapo-Gu, Seoul, Republic of Korea
| | | | - Fatemeh Rezaie
- Department of Civil, Environmental, and Construction Engineering and Water Resources Research Center, University of Hawai'i at Manoa, Honolulu, HI, 96822, USA.
- Geoscience Data Center, Korea Institute of Geoscience and Mineral Resources (KIGAM), 124 Gwahak-Ro, Yuseong-Gu, Daejeon, 34132, Republic of Korea.
- Department of Geophysical Exploration, Korea University of Science and Technology, 217 Gajeong-Ro, Yuseong-Gu, Daejeon, 34113, Republic of Korea.
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Ahmed F, Abbas S, Athar A, Shahzad T, Khan WA, Alharbi M, Khan MA, Ahmed A. Identification of kidney stones in KUB X-ray images using VGG16 empowered with explainable artificial intelligence. Sci Rep 2024; 14:6173. [PMID: 38486010 PMCID: PMC10940612 DOI: 10.1038/s41598-024-56478-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2023] [Accepted: 03/06/2024] [Indexed: 03/18/2024] Open
Abstract
A kidney stone is a solid formation that can lead to kidney failure, severe pain, and reduced quality of life from urinary system blockages. While medical experts can interpret kidney-ureter-bladder (KUB) X-ray images, specific images pose challenges for human detection, requiring significant analysis time. Consequently, developing a detection system becomes crucial for accurately classifying KUB X-ray images. This article applies a transfer learning (TL) model with a pre-trained VGG16 empowered with explainable artificial intelligence (XAI) to establish a system that takes KUB X-ray images and accurately categorizes them as kidney stones or normal cases. The findings demonstrate that the model achieves a testing accuracy of 97.41% in identifying kidney stones or normal KUB X-rays in the dataset used. VGG16 model delivers highly accurate predictions but lacks fairness and explainability in their decision-making process. This study incorporates the Layer-Wise Relevance Propagation (LRP) technique, an explainable artificial intelligence (XAI) technique, to enhance the transparency and effectiveness of the model to address this concern. The XAI technique, specifically LRP, increases the model's fairness and transparency, facilitating human comprehension of the predictions. Consequently, XAI can play an important role in assisting doctors with the accurate identification of kidney stones, thereby facilitating the execution of effective treatment strategies.
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Affiliation(s)
- Fahad Ahmed
- School of Computer Science, National College of Business Administration and Economics, Lahore, 54000, Pakistan
| | - Sagheer Abbas
- Department of Computer Sciences, Bahria University, Lahore Campus, Lahore, 54000, Pakistan
| | - Atifa Athar
- Department of Computer Science, Comsats University Islamabad, Lahore Campus, Lahore, 54000, Pakistan
| | - Tariq Shahzad
- Department of Computer Sciences, COMSATS University Islamabad, Sahiwal Campus, Sahiwal, 57000, Pakistan
| | - Wasim Ahmad Khan
- School of Computer Science, National College of Business Administration and Economics, Lahore, 54000, Pakistan
| | - Meshal Alharbi
- Department of Computer Science, College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University, 11942, Alkharj, Saudi Arabia
| | - Muhammad Adnan Khan
- School of Computing, Skyline University College, University City Sharjah, 1797, Sharjah, UAE.
- Department of Software, Faculty of Artificial Intelligence and Software, Gachon University, Seongnam-si, 13120, Republic of Korea.
- Riphah School of Computing and Innovation, Faculty of Computing, Riphah International University, Lahore Campus, Lahore, 54000, Pakistan.
| | - Arfan Ahmed
- AI Center for Precision Health, Weill Cornell Medicine-Qatar, Doha, Qatar.
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Allal Z, Noura HN, Salman O, Chahine K. Machine learning solutions for renewable energy systems: Applications, challenges, limitations, and future directions. JOURNAL OF ENVIRONMENTAL MANAGEMENT 2024; 354:120392. [PMID: 38387355 DOI: 10.1016/j.jenvman.2024.120392] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/11/2023] [Revised: 12/26/2023] [Accepted: 02/11/2024] [Indexed: 02/24/2024]
Abstract
The Paris Agreement, a landmark international treaty signed in 2016 to limit global warming to 2°C, has urged researchers to explore various strategies for achieving its ambitious goals. While Renewable Energy (RE) innovation holds promise, it alone may not be sufficient as critical deadlines approach. This field of research presents numerous challenges, foremost among them being the costliness of materials involved. However, emerging advancements in Machine Learning (ML) technologies provide a glimmer of hope; these sophisticated algorithms can accurately predict the output of energy systems without relying on physical resources and instead leverage available data from diverse energy platforms that have emerged over recent decades. The primary objective of this paper is to comprehensively explore various ML techniques and algorithms in the context of Renewable Energy Systems (RES). The investigation will address several vital inquiries, including identifying and evaluating existing RE technologies, assessing their potential for further advancement, and thoroughly analyzing the challenges and limitations associated with their deployment and testing. Furthermore, this research examines how ML can effectively overcome these obstacles by enhancing RES performance. By identifying future research opportunities and outlining potential directions for improvement, this work seeks to contribute to developing environmentally sustainable energy systems.
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Affiliation(s)
- Zaid Allal
- Univ. Franche-Comté (UFC), FEMTO-ST Institute, France
| | | | - Ola Salman
- American University of Beirut, Electrical and Computer Engineering Department, Beirut, Lebanon
| | - Khaled Chahine
- College of Engineering and Technology, American University of the Middle East, Kuwait.
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Sharma N, Puri V, Mahajan S, Abualigah L, Zitar RA, Gandomi AH. Solar power forecasting beneath diverse weather conditions using GD and LM-artificial neural networks. Sci Rep 2023; 13:8517. [PMID: 37231039 DOI: 10.1038/s41598-023-35457-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Accepted: 05/18/2023] [Indexed: 05/27/2023] Open
Abstract
Large-scale solar energy production is still a great deal of obstruction due to the unpredictability of solar power. The intermittent, chaotic, and random quality of solar energy supply has to be dealt with by some comprehensive solar forecasting technologies. Despite forecasting for the long-term, it becomes much more essential to predict short-term forecasts in minutes or even seconds prior. Because key factors such as sudden movement of the clouds, instantaneous deviation of temperature in ambiance, the increased proportion of relative humidity and uncertainty in the wind velocities, haziness, and rains cause the undesired up and down ramping rates, thereby affecting the solar power generation to a greater extent. This paper aims to acknowledge the extended stellar forecasting algorithm using artificial neural network common sensical aspect. Three layered systems have been suggested, consisting of an input layer, hidden layer, and output layer feed-forward in conjunction with back propagation. A prior 5-min te output forecast fed to the input layer to reduce the error has been introduced to have a more precise forecast. Weather remains the most vital input for the ANN type of modeling. The forecasting errors might enhance considerably, thereby affecting the solar power supply relatively due to the variations in the solar irradiations and temperature on any forecasting day. Prior approximation of stellar radiations exhibits a small amount of qualm depending upon climatic conditions such as temperature, shading conditions, soiling effects, relative humidity, etc. All these environmental factors incorporate uncertainty regarding the prediction of the output parameter. In such a case, the approximation of PV output could be much more suitable than direct solar radiation. This paper uses Gradient Descent (GD) and Levenberg Maquarndt Artificial Neural Network (LM-ANN) techniques to apply to data obtained and recorded milliseconds from a 100 W solar panel. The essential purpose of this paper is to establish a time perspective with the greatest deal for the output forecast of small solar power utilities. It has been observed that 5 ms to 12 h time perspective gives the best short- to medium-term prediction for April. A case study has been done in the Peer Panjal region. The data collected for four months with various parameters have been applied randomly as input data using GD and LM type of artificial neural network compared to actual solar energy data. The proposed ANN based algorithm has been used for unswerving petite term forecasting. The model output has been presented in root mean square error and mean absolute percentage error. The results exhibit a improved concurrence between the forecasted and real models. The forecasting of solar energy and load variations assists in fulfilling the cost-effective aspects.
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Affiliation(s)
- Neetan Sharma
- Electrical Engineering Department, BGSB University, Rajouri, India.
| | - Vinod Puri
- Electrical Engineering Department, BGSB University, Rajouri, India
| | - Shubham Mahajan
- Ajeenkya DY Patil University, Pune, Maharashtra, 412105, India
- University Center for Research & Development (UCRD), Chandigarh University, Mohali, India
| | - Laith Abualigah
- MEU Research Unit, Middle East University, Amman, Jordan
- Hourani Center for Applied Scientific Research, Al-Ahliyya Amman University, Amman, 19328, Jordan
- Applied science research center, Applied science private university, Amman, 11931, Jordan
- School of Computer Sciences, Universiti Sains Malaysia, 11800, Pulau Pinang, Malaysia
- School of Engineering and Technology, Sunway University Malaysia, Petaling Jaya 27500, Malaysia
- Computer Science Department, Prince Hussein Bin Abdullah Faculty for Information Technology, Al al-Bayt University, Mafraq 25113, Jordan
| | - Raed Abu Zitar
- Sorbonne Center of Artificial Intelligence, Sorbonne University-Abu Dhabi, Abu Dhabi, United Arab Emirates
| | - Amir H Gandomi
- Faculty of Engineering and Information Technology, University of Technology Sydney, Ultimo, NSW, 2007, Australia.
- University Research and Innovation Center (EKIK), Óbuda University, 1034, Budapest, Hungary.
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Khan KI, Nasir A. Application of artificial intelligence in solar and wind energy resources: a strategy to deal with environmental pollution. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2023; 30:64845-64859. [PMID: 37097570 DOI: 10.1007/s11356-023-27038-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/16/2022] [Accepted: 04/11/2023] [Indexed: 05/17/2023]
Abstract
Environmental pollution has become a significant concern of nations. International organizations, local authorities, and social activists try to achieve sustainable development goals (SDGs) to protect the environment. However, this cannot be achieved without acknowledging the role of advanced technology applications. Previous studies found a significant relationship between technology and energy resources. But the need to highlight the significance of artificial intelligence (AI) in dealing with inevitable environmental issues still requires more attention. This study aims to analyze the application of AI applications in predicting, developing, and implementing wind and solar energy resources through a bibliometric analysis from 1991 to 2022. It uses bilioshiny of the "bibliometrix 3.0" package of R-programming for influential core aspects and keyword analysis and VOSviewer for co-occurrence analysis. The study provides significant implications for core authors, documents, sources, affiliations, and countries. It also provides keyword analysis and a co-occurrence network to cope with the conceptual integration of the literature. It reports three significant streams of literature in clusters: AI optimization and renewable energy resources; smart renewable energy resource challenges and opportunities; deep learning and machine learning forecasting; and energy efficiency. The findings will uncover the strategic perspective of AI technology for wind and solar energy generation projects.
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Affiliation(s)
- Kanwal Iqbal Khan
- Department of Management Sciences, University of Engineering and Technology, New Campus, Kala Shah Kaku, Pakistan
| | - Adeel Nasir
- Department of Management Sciences, Lahore College for Women University, Lahore, Pakistan.
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Multilevel thresholding image segmentation using meta-heuristic optimization algorithms: comparative analysis, open challenges and new trends. APPL INTELL 2022. [DOI: 10.1007/s10489-022-04064-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
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Ewees AA, Al-qaness MA, Abualigah L, Elaziz MA. HBO-LSTM: Optimized long short term memory with heap-based optimizer for wind power forecasting. ENERGY CONVERSION AND MANAGEMENT 2022; 268:116022. [DOI: 10.1016/j.enconman.2022.116022] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/02/2023]
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Abstract
The Harris hawk optimizer is a recent population-based metaheuristics algorithm that simulates the hunting behavior of hawks. This swarm-based optimizer performs the optimization procedure using a novel way of exploration and exploitation and the multiphases of search. In this review research, we focused on the applications and developments of the recent well-established robust optimizer Harris hawk optimizer (HHO) as one of the most popular swarm-based techniques of 2020. Moreover, several experiments were carried out to prove the powerfulness and effectivness of HHO compared with nine other state-of-art algorithms using Congress on Evolutionary Computation (CEC2005) and CEC2017. The literature review paper includes deep insight about possible future directions and possible ideas worth investigations regarding the new variants of the HHO algorithm and its widespread applications.
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Abstract
This review study attempts to summarize available energy storage systems in order to accelerate the adoption of renewable energy. Inefficient energy storage systems have been shown to function as a deterrent to the implementation of sustainable development. It is therefore critical to conduct a thorough examination of existing and soon-to-be-developed energy storage technologies. Various scholarly publications in the fields of energy storage systems and renewable energy have been reviewed and summarized. Data and themes have been further highlighted with the use of appropriate figures and tables. Case studies and examples of major projects have also been researched to gain a better understanding of the energy storage technologies evaluated. An insightful analysis of present energy storage technologies and other possible innovations have been discovered with the use of suitable literature review and illustrations. This report also emphasizes the critical necessity for an efficient storage system if renewable energy is to be widely adopted.
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Heating/Cooling Fresh Air Using Hot/Cold Exhaust Air of Heating, Ventilating, and Air Conditioning Systems. ENERGIES 2022. [DOI: 10.3390/en15051877] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/27/2023]
Abstract
This paper suggests a heat recovery concept that is based on preheating/precooling the cold/hot fresh outside air by means of the relatively hot/cold exhaust air in winter/summer weather conditions. To investigate the feasibility of such a concept, an experimental setup is established to simulate conditions similar to an All-Air HVAC system. The prototype consists of a 6.7-m3 air-conditioned chamber by means of a split unit of 5.3-kW capacity. The heat recovery module consists of a duct system that is used to reroute the exhaust air from a conditioned chamber to flow through the fin side of a fin-and-tube heat exchanger of crossflow type. At the same time, outside, fresh air is flowing through the tube side of the fin-and-tube heat exchanger. A parametric study is performed to assess the amount of heat that can be recovered by varying the mass flow rates on both the duct and heat exchanger sides. The results show that up to 200 W of power can be saved for an exhaust flow rate of 0.1 kg/s and a fresh, outdoor air flow rate of 0.05 kg/s. Environmentally speaking, this leads to a reduction in production of about 1 tons of CO2 per year when the system operates 24 h/day. From an economic point of view, the system is able to return its price after 1.5 years when it is used 24 h per day during hot days at 196-W thermal recovery, whereas it requires at least 6.3 years when it is used during cold days at a 60-W thermal recovery rate, which, in both cases, represents a duration less than the lifespan of an air conditioner.
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Integrated Carbon-Capture-Based Low-Carbon Economic Dispatch of Power Systems Based on EEMD-LSTM-SVR Wind Power Forecasting. ENERGIES 2022. [DOI: 10.3390/en15051613] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/27/2023]
Abstract
The optimal utilization of wind power and the application of carbon capture power plants are important measures to achieve a low-carbon power system, but the high-energy consumption of carbon capture power plants and the uncertainty of wind power lead to low-carbon coordination problems during load peaks. To address these problems, firstly, the EEMD-LSTM-SVR algorithm is proposed to forecast wind power in the Belgian grid in order to tackle the uncertainty and strong volatility of wind power. Furthermore, the conventional thermal power plant is transformed into an integrated carbon capture power plant containing split-flow and liquid storage type, and the low-carbon mechanism of the two approaches is adequately discussed to give the low-carbon realization mechanism of the power system. Secondly, the mathematical model of EEMD-LSTM-SVR algorithm and the integrated low-carbon economic dispatch model are constructed. Finally, the simulation is verified in a modified IEEE-39 node system with carbon capture power plant. Compared with conventional thermal power plants, the carbon emissions of integrated carbon capture plants will be reduced by 78.248%; the abandoned wind of split carbon capture plants is reduced by 53.525%; the total cost of wind power for dispatch predicted using the EEMD-LSTM-SVR algorithm will be closer to the actual situation, with a difference of only USD 60. The results demonstrate that the dispatching strategy proposed in this paper can effectively improve the accuracy of wind power prediction and combine with the integrated carbon capture power plant to improve the system wind power absorption capacity and operational efficiency while achieving the goal of low carbon emission.
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