Titlebook: Hydrological Data Driven Modelling; A Case Study Approac Renji Remesan,Jimson Mathew Book 2015 Springer International Publishing Switzerlan

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書目名稱Hydrological Data Driven Modelling影響因子(影響力)




書目名稱Hydrological Data Driven Modelling影響因子(影響力)學(xué)科排名




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書目名稱Hydrological Data Driven Modelling網(wǎng)絡(luò)公開度學(xué)科排名




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書目名稱Hydrological Data Driven Modelling被引頻次學(xué)科排名




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書目名稱Hydrological Data Driven Modelling年度引用學(xué)科排名




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書目名稱Hydrological Data Driven Modelling讀者反饋學(xué)科排名

拍下盜公款

Application of Statistical Blockade in Hydrology, is included in this chapter and the capability of Statistical Blockade is compared with adequately trained Artificial Neural Networks (ANN) and Support Vector Machines (SVM) to get an idea of the accuracy of the Statistical Blockade.

橢圓

Machine Learning and Artificial Intelligence-Based Approaches,ption of the training algorithms used in this book and points out the conceptual advantages of Levenberg–Marquardt (LM) algorithms over Broyden-Fletcher-Goldfarb-Shanno (BFGS) training algorithms and Conjugate Gradient (CG) training algorithms.

COKE

Data Based Solar Radiation Modelling,ther nonlinear intelligent models and other wavelet conjunction models on daily data from the Brue catchment. Towards end of this chapter, we performed the best and useful data modelling approach for the daily solar radiation modelling at the Brue catchment in terms of very simple overall model utility comparison.

傻

Book 2015on of state-of-the-art input selection methods on the basis of detailed and comprehensive experimentation and comparative studies that employ emerging hybrid techniques for modeling and analysis. Advanced computing offers a range of new options for hydrologic modeling with the help of mathematical a

高爾夫

好開玩笑

Data Based Rainfall-Runoff Modelling,ection of the chapter suggests a simple procedure to estimate the utility of different models considering different attributes like uncertainty (in terms of model sensitivity and error) and complexity (in terms of modelling time) and applied to rainfall runoff modelling.

expound

Hydroinformatics and Data-Based Modelling Issues in Hydrology,ing in hydrology, e.g., how much benefit could be gained by increased complexity in data-based models or whether increased complexity adversely affects model performance. The chapter reminds one of the need to evaluate existing hypothetic assumptions on various modeling properties.

爭吵加

Introduction,gorously evaluate these approaches with state-of-art models through detailed and comprehensive experimentation and comparative studies. This chapter also aims to have a quick look into the critical points of current knowledge and or methodological approaches on data based modelling in hydrology and

motivate

Model Data Selection and Data Pre-processing Approaches, pools and deciding upon the optimum data length to make a reliable prediction are the main purposes of these approaches. This section of the book describes the abilities of novel techniques such as Gamma Test (GT), entropy theory (ET), Principle Component Analysis (PCA), cluster analysis (CA), Akai