Investigation spatial-temporal fluctuation ground water level by support vector machine and Kriging Method (Case study: Silakhor plain)

Q: How can I download an article?

To download an article from SID, first log in to the site, search for the article title, and click on the 'Download Article' option.

Q: How can I download an ISI article?

To download an ISI article on SID, enter the keyword or article title in the search bar, view the relevant results, click on the desired article, and select the 'Download Article' option.

Q: How can I access the SID database?

To access the SID database, visit SID.ir, create an account, and log in to access scientific resources.

Q: Is downloading articles from SID free?

Some articles on SID are available for free, while others require payment. Details are specified on the article's page.

Komasi M.; GOUDARZI H.; Behniya A.

مرکز اطلاعات علمی Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

Journal Paper

Paper Information

Journal: Journal of Water and Soil Conservation Year:2017 | Volume:24 | Issue:4 Page(s): 195-209

Download Full-Text

Persian Verion

View:

518

Download:

Cites:

Information Journal Paper

Title

Investigation spatial-temporal fluctuation ground water level by support vector machine and Kriging Method (Case study: Silakhor plain)

Author(s)

Komasi M. | GOUDARZI H. | Behniya A. | Issue Writer Certificate

Keywords

Groundwater levelQ1

KrigingQ2

Silakhor plainQ1

Support vector machineQ1

Predict space-timeQ1

Abstract

Background and Objectives: Since ground water and dwindling water resources is important for the operation of research and modeling is important. Assessment and prediction of Groundwater level to help predict groundwater resources. The use of artificial intelligence methods based on the theory of data mining is used to predict the water table fluctuation. The Support vector machine in artificial intelligence methods and the methods of geostatistical Kriging method has considerable precision in order to predict the time and location of the water table is level. In this study, the combination of Support vector machine and Kriging model as a new way to predict when and where water table fluctuation in the plain area Silakhor is used. In the first phase, modeling when using Support vector machine model data 11 piezometric wells in the region were carried out using Support vector machine and secondly to predict the location of monthly data output of the first stage as an input earth model Statistics were used. Materials and Methods: Data of 11 observation wells in the Silakhor plain data collected in the course of the past ten years in both normal and abnormal SVM model were used as input. Using the software Matlab function algorithm Support vector machine was configured as an input at each stage of a well is this model. Water level in the foreseeable future will be this way for years. At best coefficient of determination obtained for the two wells Sandarkan and Hamyaneh with values 0. 97, the lowest value for Kabadan well with its emphasis on accurately predict the amount of 0. 88 that the water level in using this method. In addition, the maximum error in the calculation of the wells with 0. 2 Keyvareh is due to lack of observation data in interval or lack of access to the region is to read water level. Absolute estimation interpolation and location estimate is a major feature. This means that estimates the quantity of sampling points is equal to the measured value and variance estimate is zero. In fact this model to estimate the amount of variance minimizes unknowns. Thus, the curves are drawn based routing and thus go beyond the boundary drawn. This feature makes the model the spatial distribution of data which are dependent on terrain, Kriging in calculations of high accuracy. Locate underground water level role in reducing the cost of drilling a well in the region. This way you can reach the height of water to be achieved in the region or even decline or rise of water table revealed. Results: The ability of Support vector machine and of course, a relatively new as a useful tool in water resource management to predict fluctuations in Groundwater levels were evaluated in Silakhor plain. According to the accuracy of this method in predicting Groundwater level can be comprehensive and appropriate program management discussion groundwater resources to be expected. In this study, using geostatistical methods in the area of water resources in plain Silakhor predicted with high accuracy are discussed. The most important issue in the analysis of spatial-temporal data to determine the dependence structure of the data. Whatever the choice of models and model are more accurate, the prediction will be more accurate. In terms of predicting when it should be noted that the time for the distance away from the last viewing time predicted prediction accuracy will be reduced. According to the study area in the last ten years has been well observed that in the best case scenarios predicted by the model, the highest coefficient of determination for wells Chughadun (DC = 0. 96), sugar (DC = 0. 94) and Valian (DC = 0. 93) was calculated, which represents a hybrid model to predict Groundwater level is the wells could be decided.

Cites

No record.

References

No record.

Cite

APA: Copy

Komasi, M., GOUDARZI, H., & Behniya, A.. (2017). Investigation spatial-temporal fluctuation ground water level by support vector machine and Kriging Method (Case study: Silakhor plain). JOURNAL OF WATER AND SOIL CONSERVATION (JOURNAL OF AGRICULTURAL SCIENCES AND NATURAL RESOURCES), 24(4 ), 195-209. SID. https://sid.ir/paper/156482/en

Vancouver: Copy

Komasi M., GOUDARZI H., Behniya A.. Investigation spatial-temporal fluctuation ground water level by support vector machine and Kriging Method (Case study: Silakhor plain). JOURNAL OF WATER AND SOIL CONSERVATION (JOURNAL OF AGRICULTURAL SCIENCES AND NATURAL RESOURCES)[Internet]. 2017;24(4 ):195-209. Available from: https://sid.ir/paper/156482/en

IEEE: Copy

M. Komasi, H. GOUDARZI, and A. Behniya, “Investigation spatial-temporal fluctuation ground water level by support vector machine and Kriging Method (Case study: Silakhor plain),” JOURNAL OF WATER AND SOIL CONSERVATION (JOURNAL OF AGRICULTURAL SCIENCES AND NATURAL RESOURCES), vol. 24, no. 4 , pp. 195–209, 2017, [Online]. Available: https://sid.ir/paper/156482/en

Related Journal Papers