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El-Kenawy ESM, Zerouali B, Bailek N, Bouchouich K, Hassan MA, Almorox J, Kuriqi A, Eid M, Ibrahim A. Improved weighted ensemble learning for predicting the daily reference evapotranspiration under the semi-arid climate conditions. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2022; 29:81279-81299. [PMID: 35731435 DOI: 10.1007/s11356-022-21410-8] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/18/2022] [Accepted: 06/07/2022] [Indexed: 06/15/2023]
Abstract
Evapotranspiration is an important quantity required in many applications, such as hydrology and agricultural and irrigation planning. Reference evapotranspiration is particularly important, and the prediction of its variations is beneficial for analyzing the needs and management of water resources. In this paper, we explore the predictive ability of hybrid ensemble learning to predict daily reference evapotranspiration (RET) under the semi-arid climate by using meteorological datasets at 12 locations in the Andalusia province in southern Spain. The datasets comprise mean, maximum, and minimum air temperatures and mean relative humidity and mean wind speed. A new modified variant of the grey wolf optimizer, named the PRSFGWO algorithm, is proposed to maximize the ensemble learning's prediction accuracy through optimal weight tuning and evaluate the proposed model's capacity when the climate data is limited. The performance of the proposed approach, based on weighted ensemble learning, is compared with various algorithms commonly adopted in relevant studies. A diverse set of statistical measurements alongside ANOVA tests was used to evaluate the predictive performance of the prediction models. The proposed model showed high-accuracy statistics, with relative root mean errors lower than 0.999% and a minimum R2 of 0.99. The model inputs were also reduced from six variables to only two for cost-effective predictions of daily RET. This shows that the PRSFGWO algorithm is a good RET prediction model for the semi-arid climate region in southern Spain. The results obtained from this research are very promising compared with existing models in the literature.
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Affiliation(s)
- El-Sayed M El-Kenawy
- Department of Communications and Electronics, Delta Higher Institute of Engineering and Technology, Mansour, 35111, Egypt
- Faculty of Artificial Intelligence, Delta University for Science and Technology, Mansoura, 35712, Egypt
| | - Bilel Zerouali
- Vegetal Chemistry-Water-Energy Laboratory, Faculty of Civil Engineering and Architecture, Department of Hydraulic, Hassiba Benbouali University of Chlef, B.P. 78C, Ouled Fares, 02180, Chlef, Algeria
| | - Nadjem Bailek
- Energies and Materials Research Laboratory, Department of Matter Sciences, Faculty of Sciences and Technology, University of Tamanghasset, Tamanghasset, Algeria.
| | - Kada Bouchouich
- Unité de Recherche en Energies Renouvelables en Milieu Saharien (URERMS), Centre de Développement Des Energies Renouvelables (CDER), 01000, Adrar, Algeria
| | - Muhammed A Hassan
- Mechanical Power Engineering Department, Faculty of Engineering, Cairo University, Giza, Giza, 12613, Egypt
| | - Javier Almorox
- Agricultural Engineering Department, Faculty of Agriculture, Mansoura University, Mansoura, 35516, Egypt
| | - Alban Kuriqi
- CERIS, Instituto Superior Técnico, Universidade de Lisboa, Lisbon, Portugal
- Civil Engineering Department, University for Business and Technology, Pristina, Kosovo
| | - Marwa Eid
- Faculty of Artificial Intelligence, Delta University for Science and Technology, Mansoura, 35712, Egypt
| | - Abdelhameed Ibrahim
- Computer Engineering and Control Systems Department, Faculty of Engineering, Mansoura University, Mansoura, 35516, Egypt
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Adeoye J, Akinshipo A, Thomson P, Su YX. Artificial intelligence-based prediction for cancer-related outcomes in Africa: Status and potential refinements. J Glob Health 2022; 12:03017. [PMID: 35493779 PMCID: PMC9022723 DOI: 10.7189/jogh.12.03017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Affiliation(s)
- John Adeoye
- Division of Oral and Maxillofacial Surgery, Faculty of Dentistry, The University of Hong Kong, Hong Kong SAR, China
- Oral Cancer Research Theme, Faculty of Dentistry, The University of Hong Kong, Hong Kong SAR, China
| | - Abdulwarith Akinshipo
- Department of Oral and Maxillofacial Pathology and Biology, Faculty of Dentistry, University of Lagos, Lagos, Nigeria
| | - Peter Thomson
- College of Medicine and Dentistry, James Cook University, Cairns, Queensland, Australia
| | - Yu-Xiong Su
- Division of Oral and Maxillofacial Surgery, Faculty of Dentistry, The University of Hong Kong, Hong Kong SAR, China
- Oral Cancer Research Theme, Faculty of Dentistry, The University of Hong Kong, Hong Kong SAR, China
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A Text Mining Approach in the Classification of Free-Text Cancer Pathology Reports from the South African National Health Laboratory Services. INFORMATION 2021. [DOI: 10.3390/info12110451] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
Abstract
A cancer pathology report is a valuable medical document that provides information for clinical management of the patient and evaluation of health care. However, there are variations in the quality of reporting in free-text style formats, ranging from comprehensive to incomplete reporting. Moreover, the increasing incidence of cancer has generated a high throughput of pathology reports. Hence, manual extraction and classification of information from these reports can be intrinsically complex and resource-intensive. This study aimed to (i) evaluate the quality of over 80,000 breast, colorectal, and prostate cancer free-text pathology reports and (ii) assess the effectiveness of random forest (RF) and variants of support vector machine (SVM) in the classification of reports into benign and malignant classes. The study approach comprises data preprocessing, visualisation, feature selections, text classification, and evaluation of performance metrics. The performance of the classifiers was evaluated across various feature sizes, which were jointly selected by four filter feature selection methods. The feature selection methods identified established clinical terms, which are synonymous with each of the three cancers. Uni-gram tokenisation using the classifiers showed that the predictive power of RF model was consistent across various feature sizes, with overall F-scores of 95.2%, 94.0%, and 95.3% for breast, colorectal, and prostate cancer classification, respectively. The radial SVM achieved better classification performance compared with its linear variant for most of the feature sizes. The classifiers also achieved high precision, recall, and accuracy. This study supports a nationally agreed standard in pathology reporting and the use of text mining for encoding, classifying, and production of high-quality information abstractions for cancer prognosis and research.
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