Paper Info
Title
Soft-Computing Methodologies for Precipitation Estimation: A Case Study
Abstract
The current paper presents an investigation of the accuracy of soft-computing techniques in precipitation estimation. The monthly precipitation data from 29 synoptic stations in Serbia from 1946 to 2012 are used as a case study. Despite a number of mathematical functions having been proposed for modeling precipitation estimation, the models still have disadvantages such as being very demanding in terms of calculation time. Soft computing can be used as an alternative to the analytical approach, as it offers advantages such as no required knowledge of internal system parameters, compact solutions for multivariable problems, and fast calculation. Because precipitation prediction is a crucial problem, a process which simulates precipitation with two soft-computing techniques was constructed and presented in this paper, namely, adaptive neurofuzzy inference (ANFIS) and support vector regression (SVR). In the current study, polynomial, linear, and radial basis function (RBF) are applied as the kernel function of the SVR to estimate the probability of precipitation. The performance of the proposed optimizers is confirmed with the simulation results. The SVR results are also compared with the ANFIS results. According to the experimental results, enhanced predictive accuracy and capability of generalization can be achieved with the ANFIS approach compared to SVR estimation. The simulation results verify the effectiveness of the proposed optimization strategies.
Year
DOI
Venue
2015
10.1109/JSTARS.2014.2364075
Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal of  
Keywords
Field
DocType
atmospheric precipitation,atmospheric techniques,ad 1946 to 2012,serbia,adaptive neurofuzzy inference,calculation time,mathematical functions,monthly precipitation data,precipitation estimation,radial basis function,soft-computing methodologies,support vector regression,synoptic stations,adaptive neurofuzzy inference (anfis),estimation,precipitation,support vector regression (svr),polynomials,accuracy,support vector machines,kernel,artificial neural networks
Kernel (linear algebra),Data mining,Multivariable calculus,Probability of precipitation,Support vector machine,Adaptive neuro fuzzy inference system,Soft computing,Artificial neural network,Mathematics,Kernel (statistics)
Journal
Volume
Issue
ISSN
8
3
1939-1404
Citations 
PageRank 
References 
4
0.47
11
Authors
7
Name
Order
Citations
PageRank
Shahaboddin Shamshirband151253.36
Milan Gocic2384.39
Dalibor Petkovic322320.91
Hadi Saboohi4706.00
Tutut Herawan560875.21
Miss Laiha Mat Kiah619513.78
S. Akib7191.91