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Watershed Planning within a Quantitative Scenario Analysis Framework

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Published on: July 24, 2016

Forecasting drought using neural network approaches with transformed time series data.

O Ozan Evkaya¹, Fatma Sevinç Kurnaz²

¹Research Center for ORSTAT, KU Leuven, Leuven, Belgium.

Journal of Applied Statistics

|June 16, 2022

Summary

This summary is machine-generated.

This study enhances drought forecasting by using advanced machine learning models. Incorporating wavelet transformations with Nonlinear Auto-Regressive with External Input Neural Networks (NARX-NN) significantly improves drought index prediction accuracy.

Keywords:

ANN Drought index SPI machine learning nonlinear auto-regressive wavelet

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Area of Science:

Environmental Science
Data Science
Climate Science

Background:

Droughts are increasingly frequent and costly global disasters, exacerbated by climate change.
Effective early warning systems are crucial for mitigating drought impacts.
Machine learning models offer advanced alternatives to traditional statistical methods for drought prediction.

Purpose of the Study:

To forecast drought conditions for a specific weather station in the Marmara Region.
To compare the performance of different machine learning models for drought index forecasting.
To evaluate the benefit of using wavelet transformations in drought prediction models.

Main Methods:

Calculated the Standardized Precipitation Index (SPI) for Bursa station.
Utilized time series data and its wavelet transformation as inputs for Neural Network (NN) models.
Compared Nonlinear Auto-Regressive (NARX) and NARX Neural Network (NARX-NN) models using Goodness-of-Fit (GOF) tests.

Main Results:

The Nonlinear Auto-Regressive with External Input Neural Network (NARX-NN) model demonstrated superior performance in forecasting the drought index.
Integrating wavelet transformation of the data with the NARX-NN model further enhanced prediction accuracy.
Model performance was validated using a comprehensive set of Goodness-of-Fit tests.

Conclusions:

The study confirms the effectiveness of machine learning, particularly NARX-NN, for drought forecasting.
Wavelet transformation is a valuable technique for improving the accuracy of drought index predictions.
The findings support the development of more robust early warning systems for drought management.