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spatial distribution prediction model for lake water quality parameters from ultra-sparse sampling data with recent satellite data

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INTERNATIONAL JOURNAL OF REMOTE SENSING 2023年第21期44卷 6579-6594页

作者： Lin, Mengxue Zhu, Ming Zhou, Kejian Tao, Yunfei Xiao, Xiao Huazhong Univ Sci & Technol Sch Elect Informat & Commun Wuhan Peoples R China Natl Engn Res Ctr Fire & Emergency Rescue Wuhan Peoples R China

Daily spatial distributions of water quality parameters (WQPs) are a crucial indicator in delicacy management of lake water environment in China. However, because of the high cost of construction and maintenance, there are a few automatic monitoring stations (AMSs) for continuous water sampling with only ultra-sparse spatial sampling data can be obtained. Meanwhile, although WQPs from satellite inversion are spatially continuous, they are missing on most days due to severe affected by weather and satellite revisit cycle, besides the high purchase cost. Thus, the model called MLP-GPR is formulated in this study to address the issue of predicting the spatial distribution of WQPs from ultra-sparse sampling data of AMSs combined with the spatial correlation information based on ultra-sparse satellite inversion data. This model includes the multilayer perceptron model (MLP) and Gaussian process regression model (GPR). MLP is trained to learn the interaction factors of WQPs between different locations through the satellite inversion data for predicting WQPs at more locations based on the data measured by AMSs. Then, GPR regresses the spatial distribution of WQPs at all locations on the entire lake from the predicted data by MLP. Based on the above model, we applied the satellite inversion, AMSs and artificial sampling data to predict the WQP spatial distribution of Shahu Lake in Wuhan, China. The experiment results show that the spatial resolution of WQP distribution predicted by MLP-GPR trained by satellite inversion data can reach 15 m by sampling data from artificial stations on the lake with 3.197 km2. Moreover, for each of these WQPs, compared with the satellite inversion data, the average MRE, R, and NSE of prediction results are below 3.95%, above 0.85, and above 0.69, respectively.

关键词： water quality parameters spatial distribution prediction ultra-sparse sampling data multilayer perceptron Gaussian process regression

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What are more important for aftershock spatial distribution prediction, features, or models? A case study in China

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JOURNAL OF SEISMOLOGY 2022年第1期26卷 181-196页

作者： Zhao, Sha Wang, Haiyan Xue, Yan Wang, Yilin Li, Shijian Liu, Jie Pan, Gang Zhejiang Univ Dept Comp Sci Hangzhou 310027 Peoples R China China Earthquake Networks Ctr Beijing 100045 Peoples R China

Aftershocks can cause disasters again after mainshocks, which result in threat to life and economic loss. In order to avoid secondary disasters, it is necessary to predict whether aftershocks would happen in a given region. There have been studies using different features and methods to predict aftershocks spatial distribution. However, it is still unclear which are more important for aftershock prediction, input features or models;which type of features is more predictive for the prediction task. In this paper, we predict aftershock spatial distribution by combining different types of features and applying different machine learning methods. We introduce five different types of features and combine them together for prediction: the stress change sensors, their logarithmic values, the physical quantities, the magnitude of mainshocks, and the distance between the grid cell and the epicenter of mainshocks. We train different classifiers: Naive Bayes, Support Vector Machine,Gradient Boosting Decision Tree, k-Nearest Neighbors, Logistic regression, and DMAP (a Deep Neural Network model). Based on the 62,811 aftershocks of 171 distinct mainshocks in the past about 40 years in China, we conduct comprehensive experiments and analyses. We find that features play a more important role for this prediction task. Using the same feature type, different classifiers obtain quite similar performance. With different features, the same model performs differently. Taking the combined features as input, we achieve the state-of-the-art performance, with an AUC of 0.9530, about 4% higher than that of DeVries et al., showing the superiority of the combined features. Among all the features, adding the distance to the stress change sensors contributes the most to improve the prediction performance. In addition, it is found that the model prediction performance varies in terms of the time spans after mainshocks and the aftershock magnitudes.

关键词： spatial distribution prediction Aftershocks Machine learning Feature combination

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Deep Fully Convolutional Network-Based spatial distribution prediction for Hyperspectral Image Classification

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2017年第10期55卷 5585-5599页

作者： Jiao, Licheng Liang, Miaomiao Chen, Huan Yang, Shuyuan Liu, Hongying Cao, Xianghai Xidian Univ Sch Elect Engn Int Res Ctr Intelligent Percept & ComputatMinist Key Lab Intelligent Percept & Image Understanding Xian 710071 Shaanxi Peoples R China

Most of the existing spatial-spectral-based hyperspectral image classification (HSIC) methods mainly extract the spatial-spectral information by combining the pixels in a small neighborhood or aggregating the statistical and morphological characteristics. However, those strategies can only generate shallow appearance features with limited representative ability for classes with high interclass similarity and spatial diversity and therefore reduce the classification accuracy. To this end, we present a novel HSIC framework, named deep multiscale spatial-spectral feature extraction algorithm, which focuses on learning effective discriminant features for HSIC. First, the well pretrained deep fully convolutional network based on VGG-verydeep-16 is introduced to excavate the potential deep multiscale spatial structural information in the proposed hyperspectral imaging framework. Then, the spectral feature and the deep multiscale spatial feature are fused by adopting the weighted fusion method. Finally, the fusion feature is put into a generic classifier to obtain the pixelwise classification. Compared with the existing spectral-spatial-based classification techniques, the proposed method provides the state-of-the-art performance and is much more effective, especially for images with high nonlinear distribution and spatial diversity.

关键词： Deep multiscale feature feature fusion fully convolutional network (FCN) hyperspectral image classification (HSIC) spatial distribution prediction

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Habitat Selection and spatial distribution prediction of Rhinolophidae in Wuling Mountain Based on 3S Technology 6

Habitat Selection and Spatial Distribution Prediction of Rhi...

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6th International Conference on Agro-Geoinformatics

作者： Luo Xiang Liang Liang Wang Jiahui Shen Qiu Liu Zhixiao Huang Taifu Jiangsu Normal Univ Sch Geodesy & Geomat Xuzhou Peoples R China Jishou Univ Coll Biol & Environm Sci Jishou Peoples R China

ISBN: (纸本)9781538638859

The study of species habitat selection mechanism and spatial distribution prediction has important guiding significance for the conservation and maintenance of endangered species. In this study, the "3S" technology was used to obtain the ground survey data and ecological driving factors. Then the differences and correlations among the ecological driving factors were analyzed. The binary logistic regression model was used to study the habitat selection and spatial distribution prediction of rhinolophidae in Wuling mountain area. The results showed that the rhinolophidae preferred area far from the roads and resident land and near the rivers. The reason may be that the rivers could provide the water and food source for the activities of the rhinolophidae. While the human disturbance was serious near the roads and resident land, there was a certain restriction on the predatory behavior and commuting behavior of the rhinolophidae. This led to the phenomenon of plaque separation and seriously affected the survival and development of the rhinolophidae. So it is recommended to protect the surrounding water body and take full account of the human activities on the impact of the rhinolophidae habitat in the process of economic construction and resource development, to improve the habitat quality of rhinolophidae and protect the survival and reproduction of the population.

关键词： Rhinolophidae Logistic regression model Habitat selection spatial distribution prediction

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Probabilistic evaluation of cultural soil heritage hazards in China from extremely imbalanced site investigation data using SMOTE-Gaussian process classification

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JOURNAL OF CULTURAL HERITAGE 2024年 67卷 121-133页

作者： Song, Chao Peng, Hongzhen Xu, Ling Zhao, Tengyuan Guo, Zhiqian Chen, Wenwu Xi An Jiao Tong Univ Sch Human Settlements & Civil Engn Xian Shaanxi Provinc Peoples R China Lanzhou Univ Sch Hist & Culture Lanzhou Gansu Province Peoples R China Lanzhou Univ Key Lab Mech Disaster & Environm Western China Minist Educ Lanzhou Gansu Province Peoples R China Lanzhou Univ Coll Civil Engn & Mech Lanzhou Gansu Povince Peoples R China

Cultural soil heritages (CSHs) are artifacts with historical, artistic, and scientific significance;however, they are vulnerable to various hazards, such as weathering, fractures, hollowing, collapses, and gullies. This is especially true for those CSHs exposed to the outdoors. Due to the large number of CSHs sites within China, managing and protecting these heritages with the aid of detailed on-site investigations is time-consuming and expensive. Consequently, evaluating the spatial distribution and degree of hazards developed in all these heritages becomes impractical. To address this issue, this paper developed a Gaussian process classification (GPC) method to predict the spatial distribution of typical hazards (i.e., weathering, fractures, hollowing, collapses, and gullies) and the development level of each hazard from eight environmental factors (e.g., annual relative humidity and annual sunshine time) and a limited number of investigation data. As the number of investigation data for different levels of each hazard is usually imbalanced and sparse, this study proposed a synthetic minority oversampling technique (SMOTE) with GPC to form the SMOTE-GPC method. A real-world example is used to illustrate this approach. Results from real-world data demonstrated that the proposed method achieved an F 1 score, precision, recall, and Cohen's kappa with values greater than 0.93 in both the training and testing datasets, indicating its good performance. (c) 2024 Consiglio Nazionale delle Ricerche (CNR). Published by Elsevier Masson SAS. All rights reserved.

关键词： Cultural soil heritage Extremely imbalanced data Machine learning methods spatial distribution prediction Non -parametric approach

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A novel Mahalanobis distance method for predicting oil and gas resource spatial distribution

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ENERGY EXPLORATION & EXPLOITATION 2023年第2期41卷 481-496页

作者： Guo, Qiulin Ren, Hongjia Liu, Haoyun Liu, Jifeng Chen, Ningsheng Yu, Jingdu PetroChina Res Inst Petr Explorat & Dev Beijing Peoples R China Yanshan Univ Sch Informat Sci & Engn Qinhuangdao Peoples R China Beijing Univ Posts & Telecommun Sch Informat & Commun Engn Beijing Peoples R China Beijing Tinten Grid Technol Co Ltd Beijing Peoples R China Yanshan Univ Sch Informat Sci & Engn Qinhuangdao 066004 Peoples R China

Accurate prediction of spatial distribution of petroleum resources is important for petroleum exploration. Mahalanobis distance is a popular and effective method to predict the spatial distribution of oil and gas. However, this method has equal weights for each variable, exaggerates secondary variables, and is prone to misjudge when the distance is close or equal, which impairs the accuracy of classification. To solve these problems, this paper proposes a novel Mahalanobis distance method based on genetic algorithm (GA-MD) to optimize attribute weights. The Sangonghe Formation in the hinterland of the Junggar Basin was used as an example, the validity of GA-MD was evaluated in the exploratory well data set. Compared with the current mainstream methods, the results show that the accuracy of GA-MD method is the highest, and the accuracy is improved by 2-6.2%. The application effect of the proposed method is verified by the prediction result of oil and gas probability map, the GA-MD method not only shows higher oil and gas bearing probability in the reserve areas but also has better trend extrapolation ability compared with other methods. Based on the GA-MD results, the favorable zones with remaining petroleum resources in the Sangonghe Formation in the hinterland of the Junggar Basin were visualized. Three types of favorable oil and gas distribution areas are selected. The favorable areas provide a basis for quantitative decision-making for the optimization of the next drilling strategy and determination of oil and gas exploration deployment direction in the study area.

关键词： Petroleum resources spatial distribution prediction Mahalanobis distance genetic algorithm Sangonghe Formation

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Near real-time spatial prediction of earthquake-induced landslides:A novel interpretable self-supervised learning method

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International Journal of Digital Earth 2023年第1期16卷 1885-1906页

作者： Xuewen Wang Xianmin Wang Xinlong Zhang Lizhe Wang Haixiang Guo Dongdong Li Hubei Subsurface Multi-scale Imaging Key Laboratory School of Geophysics and GeomaticsChina University of GeosciencesWuhanPeople’s Republic of China State Key Laboratory of Biogeology and Environmental Geology China University of GeosciencesWuhanPeople’s Republic of China Key Laboratory of Geological and Evaluation of Ministry of Education China University of GeosciencesWuhanPeople’s Republic of China Laboratory of Natural Disaster Risk Prevension and Emergency Management School of Economics and ManagementChina University of GeosciencesWuhanPeople’s Republic of China College of Electronic Science and Engineering National University of Defense TechnologyChangshaPeople’s Republic of China

Near real-time spatial prediction of earthquake-induced landslides(EQILs)can rapidly forecast the occurrence position of widespread landslides just after a violent earthquake;thus,EQIL prediction is very crucial to the 72-hour‘golden window’for *** work focuses on a series of earthquake events from 2008 to 2022 occurring in the Tibetan Plateau,a famous seismically-active zone,and proposes a novel interpretable self-supervised learning(ISeL)method for the near real-time spatial prediction of *** new method innovatively introduces swap noise at the unsupervised mechanism,which can improve the generalization performance and transferability of the model,and can effectively reduce false alarm and improve accuracy through *** interpretable module is built based on a self-attention mechanism to reveal the importance and contribution of various influencing factors to EQIL spatial *** results demonstrate that the ISeL model is superior to the excellent state-of-the-art machine learning and deep learning ***,according to the interpretable module in the ISeL method,the critical controlling and triggering factors are *** ISeL method can also be applied in other earthquake-frequent regions worldwide because of its good generalization and transferability.

关键词： Coseismic landslide near real-time interpretable artificial intelligence self-supervised learning spatial distribution prediction

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A method of predicting oil and gas resource spatial distribution based on Bayesian network and its application

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JOURNAL OF PETROLEUM SCIENCE AND ENGINEERING 2022年第PartE期208卷 109267-109267页

作者： Guo, Qiulin Ren, Hongjia Yu, Jingdu Wang, Jian Liu, Jifeng Chen, Ningsheng PetroChina Res Inst Petr Explorat & Dev Beijing 100083 Peoples R China Jilin Univ Coll Comp Sci & Technol Changchun 130012 Peoples R China Beijing Tianteng Grid Technol Co Ltd Beijing 100095 Peoples R China

The spatial distribution prediction of hydrocarbon resource is essential to reduce exploration risks and improve investment returns. Determining the location of the spatial distribution of oil and gas resources is a complex and uncertain problem. This paper systematically analyzes oil and gas exploration risk assessment and resource spatial distribution prediction technology, and proposes a method of predicting hydrocarbon spatial distribution based on averaged one-dependence estimators(AODE). Firstly, the transformation process of petroleum geological problem to mathematical model is described. Then, the classification principle and decision rule of AODE model are expounded. Finally, a case study of Sangonghe Formation in the hinterland of Junggar Basin in China is given to further illustrate the proposed method and work flow. The case prediction results not only reveal that the accuracy of the AODE model can reach 85.2% on the data set of 203 exploratory wells, which is higher than state-of-the-art methods (such as tree augmented Bayesian network method and the Mahalanobis distance method), but also point out the areas with high probability of remaining hydrocarbon resources in Sangonghe Formation, which provides decision-making basis for the next exploration. The application results show that the AODE model can effectively predict the spatial distribution of oil and gas and visualize the risk of geological exploration, thereby optimizing drilling strategy and increasing economic benefits.

关键词： Petroleum resources spatial distribution prediction Bayesian network AODE model Sangonghe Formation Junggar Basin

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Habitat selection and prediction of the spatial distribution of the Chinese horseshoe bat (R-sinicus) in the Wuling Mountains

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ENVIRONMENTAL MONITORING AND ASSESSMENT 2019年第1期191卷 4-4页

作者： Liang, Liang Luo, Xiang Liu, Zhixiao Wang, Jiahui Huang, Ting Li, Erzhu Jiangsu Normal Univ Sch Geog Geomat & Planning Xuzhou 221116 Jiangsu Peoples R China Nanjing Univ Sch Geog & Oceanog Sci Nanjing 210008 Jiangsu Peoples R China Jishou Univ Coll Biol & Environm Sci Jishou 416000 Hunan Peoples R China

Habitat selection by the Chinese horseshoe bat (Rhinolophus sinicus) in the Wuling Mountains was studied in this paper. Global positioning system (GPS), remote sensing (RS) and geographic information system (GIS) technologies were used to obtain ground survey data and analyse the habitat factors driving the distribution of R. sinicus. Based on these basic data, a binary logistic regression method was used to establish habitat selection models of R. sinicus. Then, the corrected Akaike information criterion (AIC(C)) was used to screen an optimal model, and the Hosmer-Lemeshow test indicated that the optimal model has suitable goodness of fit. Finally, the optimal model was used to predict the spatial distribution of R. sinicus in the Wuling Mountains. Verification analysis showed that the overall accuracy of the model was 72.7% and that the area under the curve (AUC) value was 0.947, which indicated that the model was effective for predicting suitable habitat for R. sinicus. The model results also showed that the main factors that influenced habitat selection were slope, annual mean temperature and distances from roads, rivers and residential land. R. sinicus preferred areas far from roads and residential land and areas near rivers. Generally, higher values of slope and annual mean temperature were associated with a greater likelihood of R. sinicus presence. Therefore, the protection of the water bodies surrounding R. sinicus habitats and fully addressing the impacts of human activities on R. sinicus habitats are recommended to protect the survival and reproduction of the population.

关键词： Habitat selection spatial distribution prediction Rhinolophus sinicus Logistic regression model

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