TY - JOUR
T1 - Prediction of commonly used drought indices using support vector regression powered by chaotic approach
AU - Yeşilköy, Özlem Baydaroğlu
AU - Koçak, Kasım
AU - Şaylan, Levent
N1 - Publisher Copyright:
© 2020 Ö. Baydaroğlu Yeşilköy, K. Koçak, L. Şaylan.
PY - 2020
Y1 - 2020
N2 - An effective water resources management requires accurate predictions of possible risks. Drought is one of the most devastating phenomena that has a certain risk of occurrence. Understanding the variability of the drought indices is of great importance in determining the spatiotemporal behavior of the drought phenomenon. Moreover, determination of the variability and short-term prediction of the drought indices enables us to take necessary steps in hydrological and agricultural issues. In this study, drought indices have been predicted via Support Vector Regression, SVR. This method originated from a linear regression method in a high dimensional feature space. SVR necessitates a special input matrix. In this study, this matrix has been constructed on the basis of Chaotic Approach, CA. Commonly used drought indices are used in the prediction stage. These indices consist of monthly Palmer Drought Severity Index, PDSI, Palmer Hydrological Drought Index, PHDI, Palmer Z-Index, ZNDX, Modified Palmer Drought Severity Index, PMDI, and Standard Precipitation Index, SPI. One-step ahead prediction has been realized for a 36-month period. Most results show that predictions of the drought indices using SVR are quite promising.
AB - An effective water resources management requires accurate predictions of possible risks. Drought is one of the most devastating phenomena that has a certain risk of occurrence. Understanding the variability of the drought indices is of great importance in determining the spatiotemporal behavior of the drought phenomenon. Moreover, determination of the variability and short-term prediction of the drought indices enables us to take necessary steps in hydrological and agricultural issues. In this study, drought indices have been predicted via Support Vector Regression, SVR. This method originated from a linear regression method in a high dimensional feature space. SVR necessitates a special input matrix. In this study, this matrix has been constructed on the basis of Chaotic Approach, CA. Commonly used drought indices are used in the prediction stage. These indices consist of monthly Palmer Drought Severity Index, PDSI, Palmer Hydrological Drought Index, PHDI, Palmer Z-Index, ZNDX, Modified Palmer Drought Severity Index, PMDI, and Standard Precipitation Index, SPI. One-step ahead prediction has been realized for a 36-month period. Most results show that predictions of the drought indices using SVR are quite promising.
KW - Drought indices
KW - Machine learning
KW - Phase space reconstruction
KW - Prediction
UR - http://www.scopus.com/inward/record.url?scp=85101131614&partnerID=8YFLogxK
U2 - 10.13128/ijam-970
DO - 10.13128/ijam-970
M3 - Article
AN - SCOPUS:85101131614
SN - 1824-8705
VL - 2020
SP - 65
EP - 76
JO - Italian Journal of Agrometeorology
JF - Italian Journal of Agrometeorology
IS - 2
ER -