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Spatiotemporal changes of gross primary productivity and its response to drought in the Mongolian Plateau under climate change

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Journal of Arid Land 2024年第1期16卷 46-70页

作者： ZHAO Xuqin LUO Min MENG Fanhao SA Chula BAO Shanhu BAO Yuhai College of geographical Science Inner Mongolia Normal UniversityHohhot 010022China Inner Mongolia Key Laboratory of Remote Sensing and geographic information systems Hohhot 010022China

Gross primary productivity(GPP)of vegetation is an important constituent of the terrestrial carbon sinks and is significantly influenced by *** the impact of droughts on different types of vegetation GPP provides insight into the spatiotemporal variation of terrestrial carbon sinks,aiding efforts to mitigate the detrimental effects of climate *** this study,we utilized the precipitation and temperature data from the Climatic Research Unit,the standardized precipitation evapotranspiration index(SPEI),the standardized precipitation index(SPI),and the simulated vegetation GPP using the eddy covariance-light use efficiency(EC-LUE)model to analyze the spatiotemporal change of GPP and its response to different drought indices in the Mongolian Plateau during *** main findings indicated that vegetation GPP decreased in 50.53% of the plateau,mainly in its northern and northeastern parts,while it increased in the remaining 49.47%***,meadow steppe(78.92%)and deciduous forest(79.46%)witnessed a significant decrease in vegetation GPP,while alpine steppe(75.08%),cropland(76.27%),and sandy vegetation(87.88%)recovered *** aridification areas accounted for 71.39% of the affected areas,while 28.53% of the areas underwent severe aridification,mainly located in the south and central ***,the warming aridification areas of desert steppe(92.68%)and sandy vegetation(90.24%)were *** warming was found to amplify the sensitivity of coniferous forest,deciduous forest,meadow steppe,and alpine steppe GPP to ***,the drought sensitivity of vegetation GPP in the Mongolian Plateau gradually decreased as altitude *** cumulative effect of drought on vegetation GPP persisted for 3.00-8.00 *** findings of this study will improve the understanding of how drought influences vegetation in arid and semi-arid areas.

关键词： gross primary productivity(GPP) climate change warming aridification areas drought sensitivity cumulative effect duration(CED) Mongolian Plateau

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ConvNeXt-UperNet-Based Deep Learning Model for Road Extraction from High-Resolution Remote Sensing Images

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Computers, Materials & Continua 2024年第8期80卷 1907-1925页

作者： Jing Wang Chen Zhang Tianwen Lin geographic information and Tourism College Chuzhou UniversityChuzhou239000China Anhui Province Key Laboratory of Physical geographic Environment Chuzhou UniversityChuzhou239000China

When existing deep learning models are used for road extraction tasks from high-resolution images,they are easily affected by noise factors such as tree and building occlusion and complex backgrounds,resulting in incomplete road extraction and low *** propose the introduction of spatial and channel attention modules to the convolutional neural network ***,ConvNeXt is used as the backbone network,which cooperates with the perceptual analysis network UPerNet,retains the detection head of the semantic segmentation,and builds a new model ConvNeXt-UPerNet to suppress noise *** on the open-source DeepGlobe and CHN6-CUG datasets and introducing the DiceLoss on the basis of CrossEntropyLoss solves the problem of positive and negative sample *** results show that the new network model can achieve the following performance on the DeepGlobe dataset:79.40%for precision(Pre),97.93% for accuracy(Acc),69.28% for intersection over union(IoU),and 83.56% for mean intersection over union(MIoU).On the CHN6-CUG dataset,the model achieves the respective values of 78.17%for Pre,97.63%for Acc,65.4% for IoU,and 81.46% for *** with other network models,the fused ConvNeXt-UPerNet model can extract road information better when faced with the influence of noise contained in high-resolution remote sensing *** also achieves multiscale image feature information with unified perception,ultimately improving the generalization ability of deep learning technology in extracting complex roads from high-resolution remote sensing images.

关键词： Deep learning semantic segmentation remote sensing imagery road extraction

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Monitoring Surface Water Change in Northeast China in 1999–2020:Evidence from Satellite Observation and Refined Classification

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Chinese geographical Science 2024年第1期34卷 106-117页

作者： LIU Kai ZHANG Dapeng CHEN Tan CUI Peipei FAN Chenyu SONG Chunqiao Key Laboratory of Watershed geographic Sciences Nanjing Institute of Geography and LimnologyChinese Academy of SciencesNanjing210008China University of Chinese Academy of Sciences Nanjing(UCASNJ)Nanjing211135China geographic information and Tourism College Chuzhou UniversityChuzhou239000China

As a typical region with high water demand for agricultural production,understanding the spatiotemporal surface water changes in Northeast China is critical for water resources management and sustainable ***,the long-term variation characteristics of surface water of different water body types in Northeast China remain rarely *** study investigated how surface water bodies of different types(e.g.,lake,reservoir,river,coastal aquaculture,marsh wetland,ephemeral water) changed during1999–2020 in Northeast China based on various remote sensing-based *** results showed that surface water in Northeast China grew dramatically in the past two decades,with an equivalent area increasing from 24 394 km^(2) in 1999 to 34 595 km^(2) in *** surge of ephemeral water is the primary driver of surface water expansion,which could ascribe to shifted precipitation *** wetlands,rivers,and reservoirs experienced a similar trend,with an approximate 20% increase at the interdecadal *** contrast,coastal aquacultures and natural lakes remain relatively *** study is expected to provide a more comprehensive investigation of the surface water variability in Northeast China and has important practical significance for the scientific management of different types of surface water.

关键词： surface water spatiotemporal variation water body classification remote sensing Northeast China

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A rapid field crop data collection method for complexity cropping patterns using UAV and YOLOv3

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Frontiers of Earth Science 2024年第1期18卷 242-255页

作者： Huifang ZHANG Binyao WANG Zhonggang TANG Jiahui XUE Ruihang CHEN Hongcheng KAN Shumiao LU Lina FENG Ye HE Shuhua YI Institute of Fragile Eco-environment Nantong UniversityNantong 226019China School of geographic Science Nantong UniversityNantong 226019China Nantong Key Laboratory of Spatial information Technology R&D and Application School of Geographic ScienceNantong UniversityNantong 226019China

Crop type mapping using remote sensing is critical for global agricultural monitoring and food ***,the complexity of crop planting patternsand spatial heterogeneity pose significant challenges to field data collection,thereby limiting the accuracy of remotely sensed crop *** study proposed a new approach for rapidly collecting field crop data by integrating unmanned aerial vehicle(UAV)images with the YOLOv3(You Only Look Once version 3)*** impacts of UAV flight altitude and the number of training samples on the accuracy of crop identification models were investigated using peanut,soybean,and maize as *** results showed that the average Fl-score for crop type detection accuracy reached 0.91 when utilizing UAV images captured at an altitude of 20 *** addition,a positive correlation was observed between identification accuracy and the number of training *** model developed in this study can rapidly and automatically identify crop types from UAV images,which significantly improves the survey efficiency and provides an innovative solution for acquiring field crop data in large areas.

关键词： deep learning flight altitude crop survey peanut soybean maize

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A New Integrated Interpolation Method for High Missing Unstable Disease Surveillance Data—12 Urban Agglomerations,China,2009-2020

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China CDC weekly 2024年第27期6卷 670-676,I0005-I0012页

作者： Yuanhao Shi Yilan Liao The State Key Laboratory of Resources and Environmental information System Institute of Geographic Sciences and Natural Resources ResearchChinese Academy of SciencesBeijingChina University of Chinese Academy of Science BeijingChina

Introduction:The prevalence of unstable and incomplete monitoring data significantly complicates syndromic *** data interpolation methods currently available demonstrate inadequate effectiveness in overcoming this ***:To improve the accuracy of interpolation,we propose the integration of the SHapley Additive exPlanation model(SHAP)with the structural equation model(SEM),forming a combined SHAP-SEM approach.A case study is then performed to assess the enhanced performance of this novel model compared to traditional ***:The SHAP-SEM model was utilized to develop an interpolation model employing data from the Chinese respiratory syndrome surveillance *** executed three distinct experiments to establish the model datasets,comprising a total of 100 *** performance of the model was evaluated using the root mean square error(RMSE),correlation coefficient(r),and *** findings demonstrate that the SHAP-SEM model consistently achieves superior accuracy in data interpolation,which is evident across different seasons and in overall ***:We conclude that the SHAP-SEM model demonstrates an exceptional capacity for accurately interpolating volatile and incomplete *** capability is crucial for developing a comprehensive database that is essential for conducting risk assessments related to syndromes.

关键词： interpolation incomplete exceptional

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Interannual evolution of the chemical composition,sources and processes of PM_(2.5)in Chengdu,China:Insights from observations in four winters

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Journal of Environmental Sciences 2024年第4期138卷 32-45页

作者： Junke Zhang Jiaqi Li Yunfei Su Chunying Chen Luyao Chen Xiaojuan Huang Fangzheng Wang Yawen Huang Gehui Wang Faculty of Geosciences and Environmental Engineering Southwest Jiaotong UniversityChengdu 611756China Department of Environmental Science&Engineering Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention(LAP3)Fudan UniversityShanghai 200438China Key Lab of geographic information Science of the Ministry of Education School of Geographic SciencesEast China Normal UniversityShanghai 200241China

The air quality in China has improved significantly in the last decade and,correspondingly,the characteristics of PM_(2.5)have also *** studied the interannual variation of PM_(2.5)in Chengdu,one of the most heavily polluted megacities in southwest China,during the most polluted season(winter).Our results show that the mass concentrations of PM_(2.5)decreased significantly year-by-year,from 195.8±91.0μg/m~3in winter 2016 to 96.1±39.3μg/m^(3)in winter *** mass concentrations of organic matter(OM),SO_()4^(2-),NH_(4)^(+)and NO_(3)^(-)decreased by 49.6%,57.1%,49.7% and 28.7%,*** differential reduction in the concentrations of chemical components increased the contributions from secondary organic carbon and NO_(3)^(-)and there was a larger contribution from mobile *** contribution of OM and NO_(3)^(-)not only increased with increasing levels of pollution,but also increased year-by-year at the same level of *** sources of PM_(2.5)were identified:combustion sources,vehicular emissions,dust and secondary *** aerosols made the highest contribution and increased year-by-year,from 40.6%in winter 2016 to 46.3% in winter *** contrast,the contribution from combustion sources decreased from 14.4% to 8.7%.Our results show the effectiveness of earlier pollution reduction policies and emphasizes that priority should be given to key pollutants(e.g.,OM and NO_(3)^(-))and sources(secondary aerosols and vehicular emissions)in future policies for the reduction of pollution in Chengdu during the winter months.

关键词： PM_(2.5) Chemical composition Sources Interannual evolution Winter Chengdu

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Enhancing Telemarketing Success Using Ensemble-Based Online Machine Learning

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Big Data Mining and Analytics 2024年第2期7卷 294-314页

作者： Shahriar Kaisar Md Mamunur Rashid Abdullahi Chowdhury Sakib Shahriar Shafin Joarder Kamruzzaman Abebe Diro Department of information systems and Business Analytics RMIT UniversityMelbourne 3000Australia School of Engineering and Technology Central Queensland UniversityRockhampton 4700Australia Faculty of Engineering Computer and Mathematical ScienceUniversity of AdelaideAdelaide 5005Australia centre for Smart Analytics Federation University AustraliaBallarat 3350Australia School of Accounting Information Systems and Supply ChainRMIT UniversityMelbourne 3000Australia

Telemarketing is a well-established marketing approach to offering products and services to prospective *** effectiveness of such an approach,however,is highly dependent on the selection of the appropriate consumer base,as reaching uninterested customers will induce annoyance and consume costly enterprise resources in vain while missing interested *** introduction of business intelligence and machine learning models can positively influence the decision-making process by predicting the potential customer base,and the existing literature in this direction shows promising ***,the selection of influential features and the construction of effective learning models for improved performance remain a ***,from the modelling perspective,the class imbalance nature of the training data,where samples with unsuccessful outcomes highly outnumber successful ones,further compounds the problem by creating biased and inaccurate ***,customer preferences are likely to change over time due to various reasons,and/or a fresh group of customers may be targeted for a new product or service,necessitating model retraining which is not addressed at all in existing works.A major challenge in model retraining is maintaining a balance between stability(retaining older knowledge)and plasticity(being receptive to new information).To address the above issues,this paper proposes an ensemble machine learning model with feature selection and oversampling techniques to identify potential customers more accurately.A novel online learning method is proposed for model retraining when new samples are available over *** newly introduced method equips the proposed approach to deal with dynamic data,leading to improved readiness of the proposed model for practical adoption,and is a highly useful addition to the *** experiments with real-world data show that the proposed approach achieves excellent results in all cases(e.g.,98.6

关键词： machine learning online learning oversampling telemarketing imbalanced dataset ensemble model

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Modelling and validation of flash flood inundation in drylands

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Journal of geographical Sciences 2024年第1期34卷 185-200页

作者： GAO Dan YIN Jie WANG Dandan YANG Yuhan LU Yi CHEN Ruishan School of geographic Sciences East China Normal UniversityShanghai 200241China Key Laboratory of geographic information Science of Ministry of Education East China Normal UniversityShanghai 200241China National Disaster Reduction Center of China Ministry of Emergency Management of People's Republic of ChinaBeijing 100124China School of Design Shanghai Jiao Tong UniversityShanghai 200240China

In the context of climate change and human activities,flood disasters in arid mountainous areas have become increasingly frequent,and seriously threatened the safety of people's lives and *** and accurate flash flood inundation modelling is an essential foundational research area,which can aid in the reduction of casualties and the minimization of disaster losses;however,this modelling is also very difficult,and models need to be urgently developed to address flash flood forecasting and *** objective of this study is to construct a numerical modelling method for flash floods in *** on a 2D high-resolution flood numerical model(Flood Map-Hydro Inundation2D),we hindcasted the dynamic process of flash flooding and show the spatio-temporal characteristics of flash flood inundation for the“8·18”flash flood disaster that occurred in Datong county,Qinghai *** results showed that the model output effectively agreed with the observed inundation after the event in terms of both spatial extent and temporal *** flooding mainly occurred between 00:00 and 01:00 on August 18,***,Hejiazhuang and Longwo villages were affected most *** further conducted model sensitivity analysis and found that the model was highly sensitive to both roughness and hydraulic conductivity in drylands,and the effect of hydraulic conductivity was more *** study confirmed the good performance of our model for the simulation of flash flooding in arid areas and provides a potential method for flash flood assessment and management in arid areas.

关键词： flash flood numerical modelling arid areas Datong county Flood Map-Hydro Inundation2D

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geographical features and development models of estuarine cities

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Journal of geographical Sciences 2024年第1期34卷 25-40页

作者： CHEN Mingxing XIAN Yue HUANG Yaohuan SUN Zhigang WU Chengbin Key Laboratory of Regional Sustainable Development Modeling Institute of Geographic Sciences and Natural Resources ResearchCASBeijing 100101China College of Resource and Environment University of Chinese Academy of SciencesBejing 100049China State Key Laboratory of Resources and Environmental information System Institute of Geographic Sciences and Natural Resources ResearchCASBeijing 100101China Key Laboratory of Ecosystem Network Observation and Modeling Institute of Geographic Sciences and Natural Resources ResearchCASBeijing 100101China Zhongke Shandong Dongying Institute of Geography Dongying 257509ShandongChina CAs Engineering Laboratory for Yellow River Delta Modern Agriculture Institute of Geographic Sciences and Natural Resources ResearchCASBeijing 100101China

Cities are the key areas for human beings to achieve sustainable development goals in the *** cities are a special type of coastal city in urgent need of a clear *** paper proposed that estuarine cities are cities developed on the coast where rivers and oceans meet and defined four connotations,including the intersection of rivers and marine water systems,the coordinated development of land and oceans,the location advantages of connecting rivers and seas,and the important fragility of the ecological *** used Hydro SHEDS,OSM,GPW,and urban socioeconomic statistics and selected 50 estuarine cities with large rivers as representatives to summarize the main geographical basis and socioeconomic *** are primarily found in low-altitude,flat regions with average annual temperatures that mainly vary from 10℃to 25℃,relatively abundant precipitation,and extensive biological *** are substantial variations in the socioeconomic features of estuarine *** proposed eight development patterns,including open and inclusive city spirit,high-quality livable cities,high-quality development driven by innovation,integration of internal and external communication with ports and cities,construction of an international financial center,ecological environment protection and restoration,active promotion of cultural tourism,and positive international exchanges.

关键词： estuarine cities great rivers development models sustainability physical geography socioeconomy

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Quantum self-attention neural networks for text classification

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Science China(information Sciences) 2024年第4期67卷 301-313页

作者： Guangxi LI Xuanqiang ZHAO Xin WANG Institute for Quantum Computing Baidu Research centre for Quantum Software and information University of Technology Sydney QICI Quantum information and Computation Initiative Department of Computer ScienceThe University of Hong Kong Thrust of Artificial Intelligence Information HubHong Kong University of Science and Technology(Guangzhou)

An emerging direction of quantum computing is to establish meaningful quantum applications in various fields of artificial intelligence, including natural language processing(NLP). Although some efforts based on syntactic analysis have opened the door to research in quantum NLP(QNLP), limitations such as heavy syntactic preprocessing and syntax-dependent network architecture make them impracticable on larger and real-world data sets. In this paper, we propose a new simple network architecture, called the quantum selfattention neural network(QSANN), which can compensate for these limitations. Specifically, we introduce the self-attention mechanism into quantum neural networks and then utilize a Gaussian projected quantum self-attention serving as a sensible quantum version of self-attention. As a result, QSANN is effective and scalable on larger data sets and has the desirable property of being implementable on near-term quantum devices. In particular, our QSANN outperforms the best existing QNLP model based on syntactic analysis as well as a simple classical self-attention neural network in numerical experiments of text classification tasks on public data sets. We further show that our method exhibits robustness to low-level quantum noises and showcases resilience to quantum neural network architectures.

关键词： quantum neural networks self-attention natural language processing text classification parameterized quantum circuits

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