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Sahın Demırel AN. Investigating the effect of climate factors on fig production efficiency with machine learning approach. JOURNAL OF THE SCIENCE OF FOOD AND AGRICULTURE 2024; 104:7885-7894. [PMID: 38817196 DOI: 10.1002/jsfa.13619] [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: 04/01/2024] [Revised: 05/08/2024] [Accepted: 05/12/2024] [Indexed: 06/01/2024]
Abstract
BACKGROUND This study employs a machine learning approach to investigate the impact of climate change on fig production in Turkey. The eXtreme Gradient Boosting (XGBoost) algorithm is used to analyze production performance and climate variable data from 1988 to 2023. Fig production is a significant component of Turkey's agricultural economy. Therefore, understanding how climate change affects fig production is essential for the development of sustainable agricultural practices. RESULTS Despite an observed increase in fig production between 2005 and 2020, potential yield may be negatively impacted by climate variables. Identifying the specific climatic factors affecting fig production efficiency remains a challenge. In the study, two different machine learning models are created: one for fig production yield per decare and another for fig production yield per bearing fig sapling. Eight climate variables (16 variables considering day and night values) serve as independent variables in the models. The models reveal that temperature change has the highest impact, with a percentage contribution of 41.30% in the first model and 43.90% in the second model. Thermal radiation (day and night) and 2 m temperature also significantly affect individually fig production. Wind speed, precipitation and humidity contribute to a lesser extent. CONCLUSION This study illuminates the intricate interrelationship between climate change and fig production in Turkey. The utilization of machine learning as a predictive tool for future production trends and an instrument for informing agricultural practices is a valuable contribution to the field. © 2024 The Author(s). Journal of The Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.
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Affiliation(s)
- Ayca Nur Sahın Demırel
- Faculty of Agriculture, Department of Agricultural Economics, Iğdır University, Iğdır, Turkey
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Liu Y, Zhu X, Xue J, Maimaitituerxun R, Chen W, Dai W. Machine learning models for mortality prediction in critically ill patients with acute pancreatitis-associated acute kidney injury. Clin Kidney J 2024; 17:sfae284. [PMID: 39385947 PMCID: PMC11462445 DOI: 10.1093/ckj/sfae284] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2024] [Indexed: 10/12/2024] Open
Abstract
Background The occurrence of acute kidney injury (AKI) was associated with an increased mortality rate among acute pancreatitis (AP) patients, indicating the importance of accurately predicting the mortality rate of critically ill patients with acute pancreatitis-associated acute kidney injury (AP-AKI) at an early stage. This study aimed to develop and validate machine learning-based predictive models for in-hospital mortality rate in critically ill patients with AP-AKI by comparing their performance with the traditional logistic regression (LR) model. Methods This study used data from three clinical databases. The predictors were identified by the Recursive Feature Elimination algorithm. The LR and two machine learning models-random forest (RF) and eXtreme Gradient Boosting (XGBoost)-were developed using 10-fold cross-validation to predict in-hospital mortality rate in AP-AKI patients. Results A total of 1089 patients from the Medical Information Mart for Intensive Care-IV (MIMIC-IV) and eICU Collaborative Research Database (eICU-CRD) were included in the training set and 176 patients from Xiangya Hospital were included in the external validation set. The in-hospital mortality rates of the training and external validation sets were 13.77% and 54.55%, respectively. Compared with the area under the curve (AUC) values of the LR model and the RF model, the AUC value of the XGBoost model {0.941 [95% confidence interval (CI) 0.931-0.952]} was significantly higher (both P < .001) and the XGBoost model had the smallest Brier score of 0.039 in the training set. In the external validation set, the performance of the XGBoost model was acceptable, with an AUC value of 0.724 (95% CI 0.648-0.800). However, it did not differ significantly from the LR and RF models. Conclusions The XGBoost model was superior to the LR and RF models in terms of both the discrimination and calibration in the training set. Whether the findings can be generalized needs to be further validated.
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Affiliation(s)
- Yamin Liu
- Department of Epidemiology and Health Statistics, Xiangya School of Public Health, Central South University, Changsha, Hunan, China
| | - Xu Zhu
- Department of Epidemiology and Health Statistics, College of Integrated Traditional Chinese and Western Medicine, Hunan University of Chinese Medicine, Changsha, Hunan, China
| | - Jing Xue
- Department of Scientific Research, Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Rehanguli Maimaitituerxun
- Department of Epidemiology and Health Statistics, Xiangya School of Public Health, Central South University, Changsha, Hunan, China
| | - Wenhang Chen
- Department of Nephrology, Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Wenjie Dai
- Department of Epidemiology and Health Statistics, Xiangya School of Public Health, Central South University, Changsha, Hunan, China
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Li J, Lin B, Wang P, Chen Y, Zeng X, Liu X, Chen R. A Hierarchical RF-XGBoost Model for Short-Cycle Agricultural Product Sales Forecasting. Foods 2024; 13:2936. [PMID: 39335865 PMCID: PMC11431005 DOI: 10.3390/foods13182936] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2024] [Revised: 09/13/2024] [Accepted: 09/15/2024] [Indexed: 09/30/2024] Open
Abstract
Short-cycle agricultural product sales forecasting significantly reduces food waste by accurately predicting demand, ensuring producers match supply with consumer needs. However, the forecasting is often subject to uncertain factors, resulting in highly volatile and discontinuous data. To address this, a hierarchical prediction model that combines RF-XGBoost is proposed in this work. It adopts the Random Forest (RF) in the first layer to extract residuals and achieve initial prediction results based on correlation features from Grey Relation Analysis (GRA). Then, a new feature set based on residual clustering features is generated after the hierarchical clustering is applied to classify the characteristics of the residuals. Subsequently, Extreme Gradient Boosting (XGBoost) acts as the second layer that utilizes those residual clustering features to yield the prediction results. The final prediction is by incorporating the results from the first layer and second layer correspondingly. As for the performance evaluation, using agricultural product sales data from a supermarket in China from 1 July 2020 to 30 June 2023, the results demonstrate superiority over standalone RF and XGBoost, with a Mean Absolute Percentage Error (MAPE) reduction of 10% and 12%, respectively, and a coefficient of determination (R2) increase of 22% and 24%, respectively. Additionally, its generalization is validated across 42 types of agricultural products from six vegetable categories, showing its extensive practical ability. Such performances reveal that the proposed model beneficially enhances the precision of short-term agricultural product sales forecasting, with the advantages of optimizing the supply chain from producers to consumers and minimizing food waste accordingly.
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Affiliation(s)
- Jiawen Li
- School of Computer Science, Guangdong Polytechnic Normal University, Guangzhou 510665, China
- Guangxi Key Lab of Multi-Source Information Mining & Security, Guangxi Normal University, Guilin 541004, China
| | - Binfan Lin
- School of Computer Science, Guangdong Polytechnic Normal University, Guangzhou 510665, China
| | - Peixian Wang
- School of Computer Science, Guangdong Polytechnic Normal University, Guangzhou 510665, China
| | - Yanmei Chen
- School of Computer Science, Guangdong Polytechnic Normal University, Guangzhou 510665, China
| | - Xianxian Zeng
- School of Computer Science, Guangdong Polytechnic Normal University, Guangzhou 510665, China
- Guangdong Provincial Key Laboratory of Intellectual Property and Big Data, Guangdong Polytechnic Normal University, Guangzhou 510665, China
- Guangdong Provincial Key Laboratory of Big Data Computing, The Chinese University of Hong Kong, Shenzhen (CUHK-Shenzhen), Shenzhen 518172, China
| | - Xin Liu
- Department of Electrical and Computer Engineering, University of Macau, Macau 999078, China
| | - Rongjun Chen
- School of Computer Science, Guangdong Polytechnic Normal University, Guangzhou 510665, China
- Guangdong Provincial Key Laboratory of Intellectual Property and Big Data, Guangdong Polytechnic Normal University, Guangzhou 510665, China
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Yang Z, Chen S, Tang X, Wang J, Liu L, Hu W, Huang Y, Hu J, Xing X, Zhang Y, Li J, Lei H, Liu Y. Development and validation of machine learning-based prediction model for severe pneumonia: A multicenter cohort study. Heliyon 2024; 10:e37367. [PMID: 39296114 PMCID: PMC11408761 DOI: 10.1016/j.heliyon.2024.e37367] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2024] [Revised: 07/30/2024] [Accepted: 09/02/2024] [Indexed: 09/21/2024] Open
Abstract
Severe pneumonia (SP) is a prevalent respiratory ailment characterized by high mortality and poor prognosis. Current scoring systems for pneumonia are not only time-consuming but also exhibit limitations in early SP prediction. To address this gap, this study aimed to develop a machine-learning model using inflammatory markers from peripheral blood for early prediction of SP. A total of 204 pneumonia patients from seven medical centers were studied, with 143 (68 SP cases) in the training cohort and 61 (32 SP cases) in the test cohort. Clinical characteristics and laboratory test results were collected at diagnosis. Various models including Logistic Regression, Random Forest, Naïve Bayes, XGBoost, Support Vector Machine, and Decision Tree were built and evaluated. Seven predictors-age, sex, WBC count, T-lymphocyte count, NLR, CRP, TNF-α, IL-4/IFN-γ ratio, IL-6/IL-10 ratio-were selected through LASSO regression and clinical insight. The XGBoost model, exhibiting best performance, achieved an AUC of 0.901 (95 % CI: 0.827 to 0.985) in the test cohort, with an accuracy of 0.803, sensitivity of 0.844, specificity of 0.759, and F1_score of 0.818. Indeed, SHAP analysis emphasized the significance of elevated WBC counts, older age, and elevated CRP as the top predictors. The use of inflammatory biomarkers in this concise predictive model shows significant potential for the rapid assessment of SP risk, thereby facilitating timely preventive interventions.
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Affiliation(s)
- Zailin Yang
- Department of Hematology-Oncology, Chongqing Key Laboratory of Translational Research for Cancer Metastasis and Individualized Treatment, Chongqing University Cancer Hospital, Chongqing, 400030, China
| | - Shuang Chen
- Department of Hematology-Oncology, Chongqing Key Laboratory of Translational Research for Cancer Metastasis and Individualized Treatment, Chongqing University Cancer Hospital, Chongqing, 400030, China
| | - Xinyi Tang
- Department of Hematology-Oncology, Chongqing Key Laboratory of Translational Research for Cancer Metastasis and Individualized Treatment, Chongqing University Cancer Hospital, Chongqing, 400030, China
- School of Medicine Chongqing University, Chongqing, 400044, China
| | - Jiao Wang
- Department of Medical Laboratory, Chongqing General Hospital, Chongqing, 401121, China
| | - Ling Liu
- Department of Medical Laboratory, the People's Hospital of Chongqing Liangjiang New Area, Chongqing, 401121, China
| | - Weibo Hu
- Department of Medical Laboratory, the People's Hospital of Rongchang District, Chongqing, 402460, China
| | - Yulin Huang
- Department of Medical Laboratory, the People's Hospital of Kaizhou District, Chongqing, 405499, China
| | - Jian'e Hu
- Department of Medical Laboratory, the Three Gorges Hospital Affiliated of Chongqing University, Chongqing, 404000, China
| | - Xiangju Xing
- Department of Respiratory Medicine, the Third Affiliated Hospital of Chongqing Medical University, Chongqing, 401120, China
| | - Yakun Zhang
- Department of Hematology-Oncology, Chongqing Key Laboratory of Translational Research for Cancer Metastasis and Individualized Treatment, Chongqing University Cancer Hospital, Chongqing, 400030, China
- School of Medicine Chongqing University, Chongqing, 400044, China
| | - Jun Li
- Department of Hematology-Oncology, Chongqing Key Laboratory of Translational Research for Cancer Metastasis and Individualized Treatment, Chongqing University Cancer Hospital, Chongqing, 400030, China
| | - Haike Lei
- Department of Hematology-Oncology, Chongqing Key Laboratory of Translational Research for Cancer Metastasis and Individualized Treatment, Chongqing University Cancer Hospital, Chongqing, 400030, China
| | - Yao Liu
- Department of Hematology-Oncology, Chongqing Key Laboratory of Translational Research for Cancer Metastasis and Individualized Treatment, Chongqing University Cancer Hospital, Chongqing, 400030, China
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Stipek C, Hauser T, Adams D, Epting J, Brelsford C, Moehl J, Dias P, Piburn J, Stewart R. Inferring building height from footprint morphology data. Sci Rep 2024; 14:18651. [PMID: 39134571 PMCID: PMC11319631 DOI: 10.1038/s41598-024-66467-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2024] [Accepted: 07/01/2024] [Indexed: 08/15/2024] Open
Abstract
As cities continue to grow globally, characterizing the built environment is essential to understanding human populations, projecting energy usage, monitoring urban heat island impacts, preventing environmental degradation, and planning for urban development. Buildings are a key component of the built environment and there is currently a lack of data on building height at the global level. Current methodologies for developing building height models that utilize remote sensing are limited in scale due to the high cost of data acquisition. Other approaches that leverage 2D features are restricted based on the volume of ancillary data necessary to infer height. Here, we find, through a series of experiments covering 74.55 million buildings from the United States, France, and Germany, it is possible, with 95% accuracy, to infer building height within 3 m of the true height using footprint morphology data. Our results show that leveraging individual building footprints can lead to accurate building height predictions while not requiring ancillary data, thus making this method applicable wherever building footprints are available. The finding that it is possible to infer building height from footprint data alone provides researchers a new method to leverage in relation to various applications.
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Affiliation(s)
- Clinton Stipek
- Oak Ridge National Laboratory, Oak Ridge, TN, 37831, USA.
| | - Taylor Hauser
- Oak Ridge National Laboratory, Oak Ridge, TN, 37831, USA
| | - Daniel Adams
- Oak Ridge National Laboratory, Oak Ridge, TN, 37831, USA
| | - Justin Epting
- Oak Ridge National Laboratory, Oak Ridge, TN, 37831, USA
| | | | - Jessica Moehl
- Oak Ridge National Laboratory, Oak Ridge, TN, 37831, USA
| | - Philipe Dias
- Oak Ridge National Laboratory, Oak Ridge, TN, 37831, USA
| | - Jesse Piburn
- Oak Ridge National Laboratory, Oak Ridge, TN, 37831, USA
| | - Robert Stewart
- Oak Ridge National Laboratory, Oak Ridge, TN, 37831, USA
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Oztekin PS, Katar O, Omma T, Erel S, Tokur O, Avci D, Aydogan M, Yildirim O, Avci E, Acharya UR. Comparison of Explainable Artificial Intelligence Model and Radiologist Review Performances to Detect Breast Cancer in 752 Patients. JOURNAL OF ULTRASOUND IN MEDICINE : OFFICIAL JOURNAL OF THE AMERICAN INSTITUTE OF ULTRASOUND IN MEDICINE 2024. [PMID: 39051752 DOI: 10.1002/jum.16535] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/17/2024] [Revised: 07/11/2024] [Accepted: 07/13/2024] [Indexed: 07/27/2024]
Abstract
OBJECTIVES Breast cancer is a type of cancer caused by the uncontrolled growth of cells in the breast tissue. In a few cases, erroneous diagnosis of breast cancer by specialists and unnecessary biopsies can lead to various negative consequences. In some cases, radiologic examinations or clinical findings may raise the suspicion of breast cancer, but subsequent detailed evaluations may not confirm cancer. In addition to causing unnecessary anxiety and stress to patients, such diagnosis can also lead to unnecessary biopsy procedures, which are painful, expensive, and prone to misdiagnosis. Therefore, there is a need for the development of more accurate and reliable methods for breast cancer diagnosis. METHODS In this study, we proposed an artificial intelligence (AI)-based method for automatically classifying breast solid mass lesions as benign vs malignant. In this study, a new breast cancer dataset (Breast-XD) was created with 791 solid mass lesions belonging to 752 different patients aged 18 to 85 years, which were examined by experienced radiologists between 2017 and 2022. RESULTS Six classifiers, support vector machine (SVM), K-nearest neighbor (K-NN), random forest (RF), decision tree (DT), logistic regression (LR), and XGBoost, were trained on the training samples of the Breast-XD dataset. Then, each classifier made predictions on 159 test data that it had not seen before. The highest classification result was obtained using the explainable XGBoost model (X2GAI) with an accuracy of 94.34%. An explainable structure is also implemented to build the reliability of the developed model. CONCLUSIONS The results obtained by radiologists and the X2GAI model were compared according to the diagnosis obtained from the biopsy. It was observed that our developed model performed well in cases where experienced radiologists gave false positive results.
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Affiliation(s)
- Pelin Seher Oztekin
- Department of Radiology, University of Health Sciences, Ankara Training and Research Hospital, Ankara, Turkey
| | - Oguzhan Katar
- Department of Software Engineering, Firat University, Elazig, Turkey
| | - Tulay Omma
- Department of Endocrinology and Metabolism, University of Health Sciences, Ankara Training and Research Hospital, Ankara, Turkey
| | - Serap Erel
- Department of Surgery, University of Health Sciences, Ankara Training and Research Hospital, Ankara, Turkey
| | - Oguzhan Tokur
- Department of Radiology, University of Health Sciences, Ankara Training and Research Hospital, Ankara, Turkey
| | - Derya Avci
- Department of Computer Technology, Firat University, Elazig, Turkey
| | - Murat Aydogan
- Department of Software Engineering, Firat University, Elazig, Turkey
| | - Ozal Yildirim
- Department of Software Engineering, Firat University, Elazig, Turkey
| | - Engin Avci
- Department of Software Engineering, Firat University, Elazig, Turkey
| | - U Rajendra Acharya
- School of Mathematics, Physics, and Computing, University of Southern Queensland, Springfield, Queensland, Australia
- Centre for Health Research, University of Southern Queensland, Springfield, Queensland, Australia
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Chowdhury AH, Rad D, Rahman MS. Predicting anxiety, depression, and insomnia among Bangladeshi university students using tree-based machine learning models. Health Sci Rep 2024; 7:e2037. [PMID: 38650723 PMCID: PMC11033350 DOI: 10.1002/hsr2.2037] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2023] [Revised: 02/21/2024] [Accepted: 03/26/2024] [Indexed: 04/25/2024] Open
Abstract
Background and Aims Mental health problem is a rising public health concern. People of all ages, specially Bangladeshi university students, are more affected by this burden. Thus, the objective of the study was to use tree-based machine learning (ML) models to identify major risk factors and predict anxiety, depression, and insomnia in university students. Methods A social media-based cross-sectional survey was employed for data collection. We used Generalized Anxiety Disorder (GAD-7), Patient Health Questionnaire (PHQ-9) and Insomnia Severity Index (ISI-7) scale for measuring students' anxiety, depression and insomnia problems. The tree-based supervised decision tree (DT), random forest (RF) and robust eXtreme Gradient Boosting (XGBoost) ML algorithms were used to build the prediction models and their predictive performance was evaluated using confusion matrix and receiver operating characteristic (ROC) curves. Results Of the 1250 students surveyed, 64.7% were male and 35.3% were female. The students' ages ranged from 18 to 26 years old, with an average age of 22.24 years (SD = 1.30). Majority of the students (72.6%) were from rural areas and social media addicted (56.6%). Almost 83.3% of the students had moderate to severe anxiety, 84.7% had moderate to severe depression and 76.5% had moderate to severe insomnia problems. Students' social media addiction, age, academic performance, smoking status, monthly family income and morningness-eveningness are the main risk factors of anxiety, depression and insomnia. The highest predictive performance was observed from the XGBoost model for anxiety, depression and insomnia. Conclusion The study findings offer valuable insights for stakeholders, families and policymakers enabling a more profound comprehension of the pressing mental health disorders. This understanding can guide the formulation of improved policy strategies, initiatives for mental health promotion, and the development of effective counseling services within university campus. Additionally, our proposed model might play a critical role in diagnosing and predicting mental health problems among Bangladeshi university students and similar settings.
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Affiliation(s)
| | - Dana Rad
- Center of Research Development and Innovation in PsychologyAurel Vlaicu University of AradAradRomania
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Guo Y, Yang Y, Li R, Liao X, Li Y. Cadmium accumulation in tropical island paddy soils: From environment and health risk assessment to model prediction. JOURNAL OF HAZARDOUS MATERIALS 2024; 465:133212. [PMID: 38101012 DOI: 10.1016/j.jhazmat.2023.133212] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/26/2023] [Revised: 11/22/2023] [Accepted: 12/07/2023] [Indexed: 12/17/2023]
Abstract
Cultivated soil quality is crucial because it directly affects food safety and human health, and rice is of primary concern because of its centrality to global food networks. However, a detailed understanding of cadmium (Cd) geochemical cycling in paddy soils is complicated by the multiple influencing factors present in many rice-growing areas that overlap with industrial centers. This study analyzed the pollution characteristics and health risks of Cd in paddy soils across Hainan Island and identified key influencing factors based on multi-source environmental data and prediction models. Approximately 27.07% of the soil samples exceeded the risk control standard screening value for Cd in China, posing an uncontaminated to moderate contamination risk. Cd concentration and exposure duration contributed the most to non-carcinogenic and carcinogenic risks to children, teens, and adults through ingestion. Among the nine prediction models tested, Extreme Gradient Boosting (XGBoost) exhibited the best performance for Cd prediction with soil properties having the highest importance, followed by climatic variables and topographic attributes. In summary, XGBoost reliably predicted the soil Cd concentrations on tropical islands. Further research should incorporate additional soil properties and environmental variables for more accurate predictions and to comprehensively identify their driving factors and corresponding contribution rates.
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Affiliation(s)
- Yan Guo
- Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yi Yang
- Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Ruxia Li
- Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Xiaoyong Liao
- Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
| | - Yonghua Li
- Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China.
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