The effect of kernel optimization in modeling drought phenomenon using computational intelligence (Case study: Sanandaj)

Subject Areas : Applications in earth’s climate change

Jahanbakhsh Mohammadi ¹ , Alireza Vafaeinezhad ² , Saeed Behzadi ³ , Hossein Aghamohammadi ⁴ , Amirhooman Hemmasi ⁵

1 - PhD Student, Department of Remote Sensing and GIS, Faculty of Natural Resources and Environment, Science and Research Branch, Islamic Azad University, Tehran, Iran
2 - Associate Professor, Faculty of Civil, Water and Environmental Engineering, Shahid Beheshti University, Tehran, Iran
3 - Assistant Professor, Faculty of Civil Engineering Shahid Rajaee Teacher Training University, Tehran, Iran
4 - Assistant Professor, Department of Remote Sensing and GIS, Faculty of Natural Resources and Environment, Science and Research Branch, Islamic Azad University, Tehran, Iran
5 - Professor, Natural Resources Engineering, Faculty of Natural Resources and Environment, Tehran science and Research Branch, Islamic Azad University, Tehran, Iran.

Received: 2021-11-04 Accepted : 2022-01-25 Published : 2023-03-21

Keywords: Support Vector Regression, Kernel, Computational Intelligence, Neural network,

Abstract :

Drought is one of the most important natural disasters with devastating and harmful effects in various economic, social, and environmental fields. Due to the repetitive behavior of this phenomenon, if the appropriate solutions are not implemented, its destructive effects can remain in the region for years after its occurrence. Most natural disasters, such as floods, earthquakes, hurricanes, and landslides in the short term, can cause severe financial and human damage to society, but droughts are slow-moving and creepy in nature, and their devastating effects appear gradually and over a longer period of time. Therefore, by modeling drought, it is possible to provide plans for drought preparation and reduce the damage caused by it. In this study, computational intelligence algorithms of Multi-Layer Perceptron neural network, Generalized Regression Neural Network, Support Vector Regression with support kernel, and Support Vector regression with the proposed kernel (Support Vector) Regression New kernel has been used to model the drought using the Standardized Precipitation Index. The modeling results, in most cases, showed better performance of the proposed SVR_N model than other models. The values of RMSE and R2 were 0.093 and 0.991, respectively, and the GRNN, MLP, and SVR models performed better in modeling after SVR_N, respectively. Modeling of drought phenomenon in modeling is supported by vector regression method.

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