An Attempt to Use Non-Linear Regression Modelling Technique in Long-Term Seasonal Rainfall Forecasting for Australian Capital Territory
Abstract
:1. Introduction
2. Study Area and Data Collection
3. Methods
4. Results and Discussion
5. Conclusions and Recommendations
- Cubic function is capable of producing maximum correlation between seasonal rainfall and the climate indices.
- Logarithmic function produces the minimum correlations between seasonal rainfall and the climate indices.
- DMI-SOI based non-linear models are more suitable to predict seasonal rainfall, as they produce higher correlations.
Author Contributions
Funding
Conflicts of Interest
References
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Index | Linear | Quadratic | Cubic | Exponential | Power | Logarithmic |
---|---|---|---|---|---|---|
DMIJun | 0.083 | 0.097 | 0.154 | 0.082 | 0.182 | 0.187 |
NINO3.4Jun | 0.420 * | 0.428 * | 0.436 * | 0.411 * | 0.153 | 0.157 |
NINO3.4Jul | 0.462 * | 0.462 * | 0.495 * | 0.456 * | 0.056 | 0.061 |
NINO3.4Aug | 0.503 * | 0.503 * | 0.524 * | 0.499 * | 0.332 | 0.338 |
SEIOJun | 0.127 | 0.265 | 0.267 | 0.135 | 0.331 | 0.312 |
SEIOJul | 0.208 | 0.280 | 0.323 * | 0.218 | 0.305 | 0.309 |
SEIOAug | 0.236 | 0.242 | 0.243 | 0.232 | 0.099 | 0.098 |
SOIJun | 0.380 * | 0.385 * | 0.389 * | 0.374 * | −0.055 | 0.071 |
SOIJul | 0.407 * | 0.407 * | 0.470 * | 0.405 * | 0.451 | 0.242 |
SOIAug | 0.502 * | 0.517 * | 0.527 * | 0.488 * | 0.002 | 0.002 |
Index | Linear | Quadratic | Cubic | Exponential | Power | Logarithmic |
---|---|---|---|---|---|---|
DMIJun | 0.193 | 0.198 | 0.226 | 0.190 | 0.183 | 0.192 |
NINO3.4Jun | 0.457 * | 0.480 * | 0.507 * | 0.440 * | 0.232 | 0.240 |
NINO3.4Jul | 0.518 * | 0.519 * | 0.573 * | 0.508 * | 0.089 | 0.099 |
NINO3.4Aug | 0.542 * | 0.542 * | 0.589 * | 0.533 * | 0.298 | 0.303 |
SEIOJun | 0.152 | 0.325 * | 0.329 * | 0.166 | 0.279 | 0.246 |
SEIOJul | 0.195 | 0.253 | 0.301 * | 0.204 | 0.226 | 0.230 |
SEIOAug | 0.218 | 0.222 | 0.231 | 0.214 | 0.022 | 0.022 |
SOIJun | 0.350 * | 0.355 * | 0.355 * | 0.344 * | 0.042 | 0.159 |
SOIJul | 0.407 * | 0.407 * | 0.472 * | 0.407 * | 0.518 * | 0.376 |
SOIAug | 0.516 * | 0.532 * | 0.532 * | 0.500 * | 0.071 | 0.070 |
Index | Linear | Quadratic | Cubic | Exponential | Power | Logarithmic |
---|---|---|---|---|---|---|
DMIJun | 0.122 | 0.122 | 0.151 | 0.121 | 0.105 | 0.109 |
NINO3.4Jun | 0.383 * | 0.387 * | 0.391 * | 0.376 * | 0.288 | 0.283 |
NINO3.4Jul | 0.450 * | 0.451 * | 0.474 * | 0.448 * | 0.161 | 0.176 |
NINO3.4Aug | 0.476 * | 0.476 * | 0.510 * | 0.476 * | 0.375 | 0.380 |
SEIOJun | 0.136 | 0.260 | 0.280 | 0.136 | 0.268 | 0.246 |
SEIOJul | 0.216 | 0.235 | 0.280 | 0.221 | 0.231 | 0.236 |
SEIOAug | 0.248 | 0.258 | 0.274 | 0.241 | 0.076 | 0.076 |
SOIJun | 0.267 | 0.274 | 0.274 | 0.262 | −0.097 | 0.019 |
SOIJul | 0.322 * | 0.322 * | 0.399 * | 0.321 * | 0.495 * | 0.320 |
SOIAug | 0.467 * | 0.477 * | 0.478 * | 0.453 * | 0.011 | 0.010 |
Indices Combination | Correlations | ||
---|---|---|---|
Ainslie Tyson St | Tharwa General Store | Huntly | |
SEIOJun–Nino3.4Jun | 0.533 * | 0.620 * | 0.485 * |
SEIOJun–Nino3.4Jul | 0.581 * | 0.675 * | 0.549 * |
SEIOJun–Nino3.4Aug | 0.589 * | 0.661 * | 0.556 * |
SEIOJul–Nino3.4Jun | 0.544 * | 0.597 * | 0.477 * |
SEIOJul–Nino3.4Jul | 0.587 * | 0.646 * | 0.537 * |
SEIOJul–Nino3.4Aug | 0.622 * | 0.629 * | 0.579 * |
SEIOAug–Nino3.4Jun | 0.463 * | 0.537 * | 0.444 * |
SEIOAug–Nino3.4Jul | 0.519 * | 0.597 * | 0.516 * |
SEIOJun–SOIJun | 0.472 * | 0.489 * | 0.400 * |
SEIOJun–SOIJul | 0.589 * | 0.604 * | 0.494 * |
SEIOJun–SOIAug | 0.602 * | 0.616 * | 0.530 * |
SEIOJul–SOIJun | 0.446 * | 0.408 * | 0.338 * |
SEIOJul–SOIAug | 0.619 * | 0.625 * | 0.547 * |
SEIOAug–SOIAug | 0.529 * | 0.544 * | 0.493 * |
DMIJun–SOIJun | 0.545 * | 0.410 * | 0.310 * |
DMIJun–SOIJul | 0.552 * | 0.710 * | 0.456 * |
DMIJun–SOIAug | 0.659 * | 0.564 * | 0.507 * |
Parameters | Ainslie Tyson St | Tharwa General Store | Huntly |
---|---|---|---|
R | 0.91 | 0.71 | 0.86 |
RMSE | 15.62 | 10.68 | 23.08 |
MAE | 14.2 | 9.0 | 20.9 |
d | 0.71 | 0.82 | 0.56 |
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Hossain, I.; Esha, R.; Alam Imteaz, M. An Attempt to Use Non-Linear Regression Modelling Technique in Long-Term Seasonal Rainfall Forecasting for Australian Capital Territory. Geosciences 2018, 8, 282. https://doi.org/10.3390/geosciences8080282
Hossain I, Esha R, Alam Imteaz M. An Attempt to Use Non-Linear Regression Modelling Technique in Long-Term Seasonal Rainfall Forecasting for Australian Capital Territory. Geosciences. 2018; 8(8):282. https://doi.org/10.3390/geosciences8080282
Chicago/Turabian StyleHossain, Iqbal, Rijwana Esha, and Monzur Alam Imteaz. 2018. "An Attempt to Use Non-Linear Regression Modelling Technique in Long-Term Seasonal Rainfall Forecasting for Australian Capital Territory" Geosciences 8, no. 8: 282. https://doi.org/10.3390/geosciences8080282