排序方式： 共有97条查询结果，搜索用时 15 毫秒
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Chen Wei Pradhan Biswajeet Li Shaojun Shahabi Himan Rizeei Hossein Mojaddadi Hou Enke Wang Shengquan 《Natural Resources Research》2019,28(4):1239-1258
Natural Resources Research - Groundwater is a vital water source in the rural and urban areas of developing and developed nations. In this study, a novel hybrid integration approach of... 相似文献
2.
In recent decades, attaining urban sustainability is the primary goal for urban planners and decision makers. Among various aspects of urban sustainability, environmental protection such as agricultural and forest conservations is very important in tropical countries like Malaysia. In this regard, compact urban development due to high density, rural development containment is known as the most sustainable urban forms. This paper attempts to propose an integrated modeling approach to predict the future land use changes by considering city compactness paradigms. First, the cellular automata (CA) were applied for calculating land use conversion. Next, weights-of-evidence (WoE) which is based on Bayes theory was utilized to calibrate CA model and to support the transitional rule assessment. Several urban-related parameters as well as compact city indicators were utilized to estimate the future land use maps. The results showed how compact development parameters and site characteristics can be combined using the WoE model to predict the probability of land use changes. The modeling approach supports the essential logic of probabilistic methods and indicates that spatial autocorrelation of various land use types and accessibility is the main drivers of urban land use changes. 相似文献
3.
In recent years,landslide susceptibility mapping has substantially improved with advances in machine learning.However,there are still challenges remain in landslide mapping due to the availability of limited inventory data.In this paper,a novel method that improves the performance of machine learning techniques is presented.The proposed method creates synthetic inventory data using Generative Adversarial Networks(GANs)for improving the prediction of landslides.In this research,landslide inventory data of 156 landslide locations were identified in Cameron Highlands,Malaysia,taken from previous projects the authors worked on.Elevation,slope,aspect,plan curvature,profile curvature,total curvature,lithology,land use and land cover(LULC),distance to the road,distance to the river,stream power index(SPI),sediment transport index(STI),terrain roughness index(TRI),topographic wetness index(TWI)and vegetation density are geo-environmental factors considered in this study based on suggestions from previous works on Cameron Highlands.To show the capability of GANs in improving landslide prediction models,this study tests the proposed GAN model with benchmark models namely Artificial Neural Network(ANN),Support Vector Machine(SVM),Decision Trees(DT),Random Forest(RF)and Bagging ensemble models with ANN and SVM models.These models were validated using the area under the receiver operating characteristic curve(AUROC).The DT,RF,SVM,ANN and Bagging ensemble could achieve the AUROC values of(0.90,0.94,0.86,0.69 and 0.82)for the training;and the AUROC of(0.76,0.81,0.85,0.72 and 0.75)for the test,subsequently.When using additional samples,the same models achieved the AUROC values of(0.92,0.94,0.88,0.75 and 0.84)for the training and(0.78,0.82,0.82,0.78 and 0.80)for the test,respectively.Using the additional samples improved the test accuracy of all the models except SVM.As a result,in data-scarce environments,this research showed that utilizing GANs to generate supplementary samples is promising because it can improve the predictive capability of common landslide prediction models. 相似文献
4.
One important step in binary modeling of environmental problems is the generation of absence-datasets that are traditionally generated by random sampling and can undermine the quality of outputs.To solve this problem,this study develops the Absence Point Generation(APG)toolbox which is a Python-based ArcGIS toolbox for automated construction of absence-datasets for geospatial studies.The APG employs a frequency ratio analysis of four commonly used and important driving factors such as altitude,slope degree,topographic wetness index,and distance from rivers,and considers the presence locations buffer and density layers to define the low potential or susceptibility zones where absence-datasets are gener-ated.To test the APG toolbox,we applied two benchmark algorithms of random forest(RF)and boosted regression trees(BRT)in a case study to investigate groundwater potential using three absence datasets i.e.,the APG,random,and selection of absence samples(SAS)toolbox.The BRT-APG and RF-APG had the area under receiver operating curve(AUC)values of 0.947 and 0.942,while BRT and RF had weaker per-formances with the SAS and Random datasets.This effect resulted in AUC improvements for BRT and RF by 7.2,and 9.7％from the Random dataset,and AUC improvements for BRT and RF by 6.1,and 5.4％from the SAS dataset,respectively.The APG also impacted the importance of the input factors and the pattern of the groundwater potential maps,which proves the importance of absence points in environmental bin-ary issues.The proposed APG toolbox could be easily applied in other environmental hazards such as landslides,floods,and gully erosion,and land subsidence. 相似文献
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Approaches for delineating landslide hazard areas using different training sites in an advanced artificial neural network model 总被引：10，自引：0，他引：10
The current paper presents landslide hazard analysis around the Cameron area, Malaysia, using advanced artificial neural networks
with the help of Geographic Information System (GIS) and remote sensing techniques. Landslide locations were determined in
the study area by interpretation of aerial photographs and from field investigations. Topographical and geological data as
well as satellite images were collected, processed, and constructed into a spatial database using GIS and image processing.
Ten factors were selected for landslide hazard including: 1) factors related to topography as slope, aspect, and curvature;
2) factors related to geology as lithology and distance from lineament; 3) factors related to drainage as distance from drainage;
and 4) factors extracted from TM satellite images as land cover and the vegetation index value. An advanced artificial neural
network model has been used to analyze these factors in order to establish the landslide hazard map. The back-propagation
training method has been used for the selection of the five different random training sites in order to calculate the factor’s
weight and then the landslide hazard indices were computed for each of the five hazard maps. Finally, the landslide hazard
maps (five cases) were prepared using GIS tools. Results of the landslides hazard maps have been verified using landslide
test locations that were not used during the training phase of the neural network. Our findings of verification results show
an accuracy of 69%, 75%, 70%, 83% and 86% for training sites 1, 2, 3, 4 and 5 respectively. GIS data was used to efficiently
analyze the large volume of data, and the artificial neural network proved to be an effective tool for landslide hazard analysis.
The verification results showed sufficient agreement between the presumptive hazard map and the existing data on landslide
areas. 相似文献
7.
Estimating groundwater vulnerability to pollution using a modified DRASTIC model in the Kerman agricultural area,Iran 总被引：7，自引：4，他引：3
Aminreza Neshat Biswajeet Pradhan Saied Pirasteh Helmi Zulhaidi Mohd Shafri 《Environmental Earth Sciences》2014,71(7):3119-3131
Groundwater contamination from intensive fertilizer application affects conservation areas in a plain. The DRASTIC model can be applied in the evaluation of groundwater vulnerability to such pollution. The main purpose of using the DRASTIC model is to map groundwater susceptibility to pollution in different areas. However, this method has been used in various areas without modification, thereby disregarding the effects of pollution types and their characteristics. Thus, this technique must be standardized and be approved for applications in aquifers and particular types of pollution. In this study, the potential for the more accurate assessment of vulnerability to pollution is achieved by correcting the rates of the DRASTIC parameters. The new rates were calculated by identifying the relationships among the parameters with respect to the nitrate concentration in groundwater. The methodology was implemented in the Kerman plain in the southeastern region of Iran. The nitrate concentration in water from underground wells was tested and analyzed in 27 different locations. The measured nitrate concentrations were used to associate and correlate the pollution in the aquifer to the DRASTIC index. The Wilcoxon rank-sum nonparametric statistical test was applied to determine the relationship between the index and the measured pollution in Kerman plain. Also, the weights of the DRASTIC parameters were modified through the sensitivity analysis. Subsequently, the rates and weights were computed. The results of the study revealed that the modified DRASTIC model performs more efficiently than the traditional method for nonpoint source pollution, particularly in agricultural areas. The regression coefficients showed that the relationship between the vulnerability index and the nitrate concentration was 82 % after modification and 44 % before modification. This comparison indicated that the results of the modified DRASTIC of this region are better than those of the original method. 相似文献
8.
Maher Ibrahim Sameen Biswajeet Pradhan 《Journal of the Indian Society of Remote Sensing》2017,45(3):395-405
Information on highways is an essential input for various geospatial applications, including car navigation, forensic analysis on highway geometries, and intelligent transportation systems. Semi-automatic and automatic extractions of highways are critical for the regular updating of municipal databases and for highway maintenance. This study presents a semi-automatic data processing approach for extracting highways from high-resolution airborne LiDAR height information and aerial orthophotos. The method was developed based on two data sets. Experimental results for the first testing site showed that the accuracy of the proposed method for highway extraction was 74.50 % for completeness and 73.13 % for correctness. Meanwhile, the completeness and correctness for the second testing site were 71.20 and 70.72 %, respectively. The proposed method was compared with an object-based approach on a different data set. The accuracy for highway extraction of the object-based approach was 64.29 % for completeness and 63.11 % for correctness, whereas that of the proposed method was 67.14 % for completeness and 65.08 % for correctness. This research aims to promote semi-automatic highway extraction from LiDAR data and orthophotos by proposing a new approach and a multistep post-processing technique. The proposed method provides an accurate final output that is valuable for a wide range of geospatial applications. 相似文献
9.
Regional prediction of landslide hazard using probability analysis of intense rainfall in the Hoa Binh province, Vietnam 总被引：6，自引：2，他引：4
Dieu Tien Bui Biswajeet Pradhan Owe Lofman Inge Revhaug Øystein B. Dick 《Natural Hazards》2013,66(2):707-730
The main objective of this study is to assess regional landslide hazards in the Hoa Binh province of Vietnam. A landslide inventory map was constructed from various sources with data mainly for a period of 21 years from 1990 to 2010. The historic inventory of these failures shows that rainfall is the main triggering factor in this region. The probability of the occurrence of episodes of rainfall and the rainfall threshold were deduced from records of rainfall for the aforementioned period. The rainfall threshold model was generated based on daily and cumulative values of antecedent rainfall of the landslide events. The result shows that 15-day antecedent rainfall gives the best fit for the existing landslides in the inventory. The rainfall threshold model was validated using the rainfall and landslide events that occurred in 2010 that were not considered in building the threshold model. The result was used for estimating temporal probability of a landslide to occur using a Poisson probability model. Prior to this work, five landslide susceptibility maps were constructed for the study area using support vector machines, logistic regression, evidential belief functions, Bayesian-regularized neural networks, and neuro-fuzzy models. These susceptibility maps provide information on the spatial prediction probability of landslide occurrence in the area. Finally, landslide hazard maps were generated by integrating the spatial and the temporal probability of landslide. A total of 15 specific landslide hazard maps were generated considering three time periods of 1, 3, and 5 years. 相似文献
10.
利用光学遥感数据、GIS及人工神经网络模型分析区域滑坡灾害(英文) 总被引：3，自引：0，他引：3
Biswajeet Pradhan Saro Lee 《地学前缘》2007,14(6):143-152
用光学遥感数据和地理信息系统(GIS)分析了马来西亚Selangor地区的滑坡灾害。通过遥感图像解译和野外调查,在研究区内确定出滑坡发生区。通过GIS和图像处理,建立了一个集地形、地质和遥感图像等多种信息的空间数据库。滑坡发生的因素主要为:地形坡度、地形方位、地形曲率及与排水设备距离;岩性及与线性构造距离;TM图像解译得到的植被覆盖情况;Landsat图像解译得到的植被指数;降水量。通过建立人工神经网络模型对这些因素进行分析后得到滑坡灾害图:由反向传播训练方法确定每个因素的权重值,然后用该权重值计算出滑坡灾害指数,最后用GIS工具生成滑坡灾害图。用遥感解译和野外观测确定出的滑坡位置资料验证了滑坡灾害图,准确率为82.92%。结果表明推测的滑坡灾害图与滑坡实际发生区域足够吻合。 相似文献