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

A watershed is a combination of several rivers and tributaries with certain boundaries that function to drain rainwater into a lake or a sea. One of the hydrological data contained in the watershed is discharge data. If there is incomplete discharge data, it must be extended based on historical data. ARIMA and decomposition are methods that can predict time series data. The purposes of this research are to determine the historical discharge patterns of Brantas Sub-basin, to know the discharge forecasting model of Brantas Sub-basin, to know the results of forecasting Brantas Sub-basin discharge, and to compare the accuracy between ARIMA and decomposition methods. The accuracy is obtained by calculating MSE and RMSE values. The best method is a method that has the smallest MSE and RMSE values. The results of the research showed that Brantas Sub-basin discharge data in 2007-2017 has a seasonal pattern. The best ARIMA model is ARIMA (0,0,3)(1,0,1)12 model, while the best decomposition model is the additive decomposition model. The Decomposition method has better accuracy than the ARIMA method in predicting discharge of Brantas Sub-basin.

The forecasting model of discharge at Brantas sub-basin using autoregressive integrated moving average (ARIMA) and decomposition methods