Forecasting of Rainfall Using Seasonal Autoregreressive Integrated Moving Average (SARIMA) Aceh, Indonesia
Bibliographic record
Abstract
Climate change which has become increasingly erratic in recent decades has become a problem of global warming.So that it has an impact and influence in changing rainfall patterns.A very volatile climate overall can threaten the success of food production.Information about rainfall patterns is very important to agriculture that relies on rainfall as the main source of irrigation.The purpose of this study is to predict rainfall from all time series based on rainfall data for 15 years, 10 years and 5 years.Prediction results were evaluated using the Nash-Sutcliffe Efficiency (NSE) statistical method, RMSE-Observation Standard Deviation Ratio (RSR) and PBIAS.This research was conducted in Aceh Besar District.Indonesia which coincided with Indrapuri District.Analysis of the data used in this study uses the Seasonal Autoregressive Integrated Moving Average (SARIMA) models.The best prediction results are generated from the use of rainfall time series data onto 5 years for 2013-2017 with the evaluation value of the model obtained is in the "Very Good " category.Prediction models for the best rainfall predictions are (0.0.0) and (0.1.2)12with the respective values of NSE of 0.84, RSR 0.41 and PBIAS -2.8.So as a whole the closest prediction results in the actual values are obtained from time series rainfall data onto the past five years.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".