English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Received: 12 April 2019 Accepted: 20 July 2019 Rainfall forecast is a hotspot in meteorological studies in the last few years. The key difficulty in forecast accuracy lies in the nonlinearity of rainfall data. Considering the potential of support vector machine (SVM) to solve nonlinear time series, this paper develops an SVM-based model based on the monthly rainfall data from 1901 to 2015 in Northwest India. The forecast accuracies of four different kernels were compared, including linear, polynomial, radial basis function (RBF) and sigmoid kernels. The comparison shows that the SVM-based model with the RBF kernel achieved the smallest root mean squared error (RMSE) at the lookback of 25, C=1, γ =0.01 and ε=0.1. Our research provides an effective tool to predict rainfall in regions with similar meteorological conditions to northwest India.

Modelling of Monthly Rainfall Patterns in the North-West India Using SVM