The use of landscape covariates to variability of soil properties in similar estimate soil properties is not suitable topographic and vegetation conditions. for the areas of low relief due to the high A new method wa...The use of landscape covariates to variability of soil properties in similar estimate soil properties is not suitable topographic and vegetation conditions. for the areas of low relief due to the high A new method was implemented to map regional soil texture （in terms of sand, silt and clay contents） by hypothesizing that the change in the land surface diurnal temperature difference （DTD） is related to soil texture in case of a relatively homogeneous rainfall input. To examine this hypothesis, the DTDs from moderate resolution imagine spectroradiometer （MODIS） during a selected time period, i.e., after a heavy rainfall between autumn harvest and autumn sowing, were classified using fuzzy-c-means （FCM） clustering. Six classes were generated, and for each class, the sand （〉 0.05 mm）, silt （0.002-0.05 mm） and clay （〈 0.002 mm） contents at the location of maximum membership value were considered as the typical values of that class. A weighted average model was then used to digitally map soil texture. The results showed that the predicted map quite accurately reflected the regional soil variation. A validation dataset produced estimates of error for the predicted maps of sand, silt and clay contents at root mean of squared error values of 8.4%, 7.8% and 2.3%, respectively, which is satisfactory in a practical context. This study thus provided a methodology that can help improve the accuracy and efficiency of soil texture mapping in plain areas using easily available data sources.展开更多
This paper deals with the estimation of crest settlement in a concrete face rockfill dam （CFRD）, utilizing intelligent methods. Following completion of dam construction, considerable movements of the crest and the b...This paper deals with the estimation of crest settlement in a concrete face rockfill dam （CFRD）, utilizing intelligent methods. Following completion of dam construction, considerable movements of the crest and the body of the dam can develop during the first impoundment of the reservoir. Although there is vast experience worldwide in CFRD design and construction, few accurate experimental relationships are available to predict the settlement in CFRD. The goal is to advance the development of intelligent methods to estimate the subsidence of dams at the design stage. Due to dam zonifieation and uncertainties in material properties, these methods appear to be the appropriate choice. In this study, the crest settlement behavior of CFRDs is analyzed based on compiled data of 24 CFRDs constructed during recent years around the world, along with the utilization of gene ex- pression programming （GEP） and adaptive neuro-fuzzy inference system （ANFIS） methods. In addition, dam height （H）, shape factor （St）, and time （t, time after first operation） are also assessed, being considered major factors in predicting the settlement behavior. From the relationships proposed, the values ofR2 for both equations of GEP （with and without constant） were 0.9603 and 0.9734, and for the three approaches of ANFIS （grid partitioning （GP）, subtractive clustering method （SCM）, and fuzzy c-means clustering （FCM）） were 0.9693, 0.8657, and 0.8848, respectively. The obtained results indicate that the overall behavior evaluated by this approach is consistent with the measured data of other CFRDs.展开更多
Classification systems such as Slope Mass Rating(SMR) are currently being used to undertake slope stability analysis. In SMR classification system, data is allocated to certain classes based on linguistic and experien...Classification systems such as Slope Mass Rating(SMR) are currently being used to undertake slope stability analysis. In SMR classification system, data is allocated to certain classes based on linguistic and experience-based criteria. In order to eliminate linguistic criteria resulted from experience-based judgments and account for uncertainties in determining class boundaries developed by SMR system,the system classification results were corrected using two clustering algorithms, namely K-means and fuzzy c-means(FCM), for the ratings obtained via continuous and discrete functions. By applying clustering algorithms in SMR classification system, no in-advance experience-based judgment was made on the number of extracted classes in this system, and it was only after all steps of the clustering algorithms were accomplished that new classification scheme was proposed for SMR system under different failure modes based on the ratings obtained via continuous and discrete functions. The results of this study showed that, engineers can achieve more reliable and objective evaluations over slope stability by using SMR system based on the ratings calculated via continuous and discrete functions.展开更多
Determining the relatively similar hydrological properties of the watersheds is very crucial in order to readily classify them for management practices such as flood and soil erosion control. This study aimed to ident...Determining the relatively similar hydrological properties of the watersheds is very crucial in order to readily classify them for management practices such as flood and soil erosion control. This study aimed to identify homogeneous hydrological watersheds using remote sensing data in western Iran. To achieve this goal, remote sensing indices including SAVI, LAI, NDMI, NDVI and snow cover, were extracted from MODIS data over the period 2000 to 2015. Then, a fuzzy method was used to clustering the watersheds based on the extracted indices. A fuzzy c-mean(FCM) algorithm enabled to classify 38 watersheds in three homogeneous groups.The optimal number of clusters was determined through evaluation of partition coefficient, partition entropy function and trial and error. The results indicated three homogeneous regions identified by the fuzzy c-mean clustering and remote sensing product which are consistent with the variations of topography and climate of the study area. Inherently,the grouped watersheds have similar hydrological properties and are likely to need similar management considerations and measures.展开更多
This paper presents a fuzzy C- means clustering image segmentation algorithm based on particle swarm optimization, the method utilizes the strong search ability of particle swarm clustering search center. Because the ...This paper presents a fuzzy C- means clustering image segmentation algorithm based on particle swarm optimization, the method utilizes the strong search ability of particle swarm clustering search center. Because the search clustering center has small amount of calculation according to density, so it can greatly improve the calculation speed of fuzzy C- means algorithm. The experimental results show that, this method can make the fuzzy clustering to obviously improve the speed, so it can achieve fast image segmentation.展开更多
基金Supported by the Basic Research Program of Jiangsu Province,China (No. BK2008058)the Knowledge Innovation Program of Chinese Academy of Sciences (No. KZCX2-YW-409)
文摘The use of landscape covariates to variability of soil properties in similar estimate soil properties is not suitable topographic and vegetation conditions. for the areas of low relief due to the high A new method was implemented to map regional soil texture （in terms of sand, silt and clay contents） by hypothesizing that the change in the land surface diurnal temperature difference （DTD） is related to soil texture in case of a relatively homogeneous rainfall input. To examine this hypothesis, the DTDs from moderate resolution imagine spectroradiometer （MODIS） during a selected time period, i.e., after a heavy rainfall between autumn harvest and autumn sowing, were classified using fuzzy-c-means （FCM） clustering. Six classes were generated, and for each class, the sand （〉 0.05 mm）, silt （0.002-0.05 mm） and clay （〈 0.002 mm） contents at the location of maximum membership value were considered as the typical values of that class. A weighted average model was then used to digitally map soil texture. The results showed that the predicted map quite accurately reflected the regional soil variation. A validation dataset produced estimates of error for the predicted maps of sand, silt and clay contents at root mean of squared error values of 8.4%, 7.8% and 2.3%, respectively, which is satisfactory in a practical context. This study thus provided a methodology that can help improve the accuracy and efficiency of soil texture mapping in plain areas using easily available data sources.
文摘This paper deals with the estimation of crest settlement in a concrete face rockfill dam （CFRD）, utilizing intelligent methods. Following completion of dam construction, considerable movements of the crest and the body of the dam can develop during the first impoundment of the reservoir. Although there is vast experience worldwide in CFRD design and construction, few accurate experimental relationships are available to predict the settlement in CFRD. The goal is to advance the development of intelligent methods to estimate the subsidence of dams at the design stage. Due to dam zonifieation and uncertainties in material properties, these methods appear to be the appropriate choice. In this study, the crest settlement behavior of CFRDs is analyzed based on compiled data of 24 CFRDs constructed during recent years around the world, along with the utilization of gene ex- pression programming （GEP） and adaptive neuro-fuzzy inference system （ANFIS） methods. In addition, dam height （H）, shape factor （St）, and time （t, time after first operation） are also assessed, being considered major factors in predicting the settlement behavior. From the relationships proposed, the values ofR2 for both equations of GEP （with and without constant） were 0.9603 and 0.9734, and for the three approaches of ANFIS （grid partitioning （GP）, subtractive clustering method （SCM）, and fuzzy c-means clustering （FCM）） were 0.9693, 0.8657, and 0.8848, respectively. The obtained results indicate that the overall behavior evaluated by this approach is consistent with the measured data of other CFRDs.
文摘Classification systems such as Slope Mass Rating(SMR) are currently being used to undertake slope stability analysis. In SMR classification system, data is allocated to certain classes based on linguistic and experience-based criteria. In order to eliminate linguistic criteria resulted from experience-based judgments and account for uncertainties in determining class boundaries developed by SMR system,the system classification results were corrected using two clustering algorithms, namely K-means and fuzzy c-means(FCM), for the ratings obtained via continuous and discrete functions. By applying clustering algorithms in SMR classification system, no in-advance experience-based judgment was made on the number of extracted classes in this system, and it was only after all steps of the clustering algorithms were accomplished that new classification scheme was proposed for SMR system under different failure modes based on the ratings obtained via continuous and discrete functions. The results of this study showed that, engineers can achieve more reliable and objective evaluations over slope stability by using SMR system based on the ratings calculated via continuous and discrete functions.
文摘Determining the relatively similar hydrological properties of the watersheds is very crucial in order to readily classify them for management practices such as flood and soil erosion control. This study aimed to identify homogeneous hydrological watersheds using remote sensing data in western Iran. To achieve this goal, remote sensing indices including SAVI, LAI, NDMI, NDVI and snow cover, were extracted from MODIS data over the period 2000 to 2015. Then, a fuzzy method was used to clustering the watersheds based on the extracted indices. A fuzzy c-mean(FCM) algorithm enabled to classify 38 watersheds in three homogeneous groups.The optimal number of clusters was determined through evaluation of partition coefficient, partition entropy function and trial and error. The results indicated three homogeneous regions identified by the fuzzy c-mean clustering and remote sensing product which are consistent with the variations of topography and climate of the study area. Inherently,the grouped watersheds have similar hydrological properties and are likely to need similar management considerations and measures.
文摘This paper presents a fuzzy C- means clustering image segmentation algorithm based on particle swarm optimization, the method utilizes the strong search ability of particle swarm clustering search center. Because the search clustering center has small amount of calculation according to density, so it can greatly improve the calculation speed of fuzzy C- means algorithm. The experimental results show that, this method can make the fuzzy clustering to obviously improve the speed, so it can achieve fast image segmentation.
