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在“自然资源管理”中，命中：45条，耗时：小于0.01 秒

1.Predicting Nitrate Concentration and Its Spatial Distribution in Groundwater Resources Using Support Vector Machines (SVMs) Model

作者：Raheleh Arabgol ; Majid Sartaj ; Keyvan Asghari

关键词：Support vector machines ; Nitrate concentration ; Groundwater quality ; Cross ; validation ; Grid search

刊名：Environmental Modeling & Assessment

出版者：Springer Netherlands

年：2016

2.Evaluation of a Two-Stage SVM and Spatial Statistics Methods for Modeling Monthly River Suspended Sediment Load

作者：Vahid Nourani ; Farhad Alizadeh ; Kiyoumars Roushangar

关键词：Suspended sediment load ; Support vector machine ; Spatial statistics ; Ajichay watershed

刊名：Water Resources Management

出版者：Springer Netherlands

年：2016

3.Quantifying the Relative Contribution of Climate and Human Impacts on Runoff Change Based on the Budyko Hypothesis and SVM Model

作者：Shengzhi Huang ; Jianxia Chang ; Qiang Huang ; Yutong Chen…

关键词：The heuristic segmentation method ; The Budyko hypothesis ; Support vector machine ; The wei river basin

刊名：Water Resources Management

出版者：Springer Netherlands

年：2016

4.Support-Vector-Machine-Based Models for Modeling Daily Reference Evapotranspiration With Limited Climatic Data in Extreme Arid Regions

作者：Xiaohu Wen ; Jianhua Si ; Zhibin He ; Jun Wu ; Hongbo Shao…

关键词：Support vector machine ; Reference evapotranspiration modeling ; Limited climatic data ; Extreme arid regions

刊名：Water Resources Management

出版者：Springer Netherlands

年：2015

5.Urban water resources allocation and shortage risk mapping with support vector machine method

作者：Qian Zhang ; Xiujuan Liang ; Zhang Fang ; Tao Jiang ; Yubo Wang ; Lei Wang

关键词：Water shortage ; MIKE ; BASIN ; SVM model ; Risk level assessment

刊名：Natural Hazards

出版者：Springer Netherlands

年：2016

6.A hybrid LS-HE and LS-SVM model to predict time series of precipitable water vapor derived from GPS measurements

作者：M. A. Sharifi ; A. H. Souri

关键词：GPS meteorology ; Least squares harmonic estimation ; Precipitable water vapor ; Least squares support vector machine

刊名：Arabian Journal of Geosciences

出版者：Springer Berlin Heidelberg

年：2015

7.Estimation of Failure Rate in Water Distribution Network Using Fuzzy Clustering and LS-SVM Methods

作者：Mahmut Aydogdu ; Mahmut Firat

关键词：Water distribution network ; Failure rate ; LS ; SVM ; Fuzzy clustering

刊名：Water Resources Management

出版者：Springer Netherlands

年：2015

8.Urmia lake water level fluctuation hydro informatics modeling using support vector machine and conjunction of wavelet and neural network

作者：Mojtaba Noury (1) Hosein Sedghi (2) Hosein Babazedeh (2) Hedayat Fahmi (2)

关键词：Hydro Informatics Modeling ; Urmia Lake ; Support Vector Machine (SVM) ; Neural Wavelet Network (NWN) ; water level fluctuation modeling

刊名：Water Resources

年：2014

9.Quantitative evaluation of mining geo-environmental quality in Northeast China: comprehensive index method and support vector machine models

作者：Xue Jiang ; Wen-xi Lu ; Hai-qing Zhao ; Qing-chun Yang…

关键词：Comprehensive index method ; SVM ; Mining geo ; environment ; Quality evaluation ; Changjitu economic zone

刊名：Environmental Earth Sciences

出版者：Springer Berlin Heidelberg

年：2015

10.A machine learning-based approach to predict the velocity profiles in small streams

作者：Onur Genç ; Ali Dağ

关键词：Velocity profile ; Small stream ; Machine learning ; ANNs ; SVMs ; k ; NN

刊名：Water Resources Management

出版者：Springer Netherlands

年：2016

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