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Drought Forecasting: A Review and Assessment of the Hybrid Techniques and Data Pre-processing

Alawsi, MA, Zubaidi, S, Al-Bdairi, NSS, Al-Ansari, N and Hashim, KS (2022) Drought Forecasting: A Review and Assessment of the Hybrid Techniques and Data Pre-processing. Hydrology, 9 (7). ISSN 2306-5338

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Drought is a prolonged period of low precipitation that negatively impacts agriculture, animals, and people. Over the last decades, gradual changes in drought indices have been observed. Therefore, understanding and forecasting drought is essential to avoid its economic impacts and appropriate water resource planning and management. This paper presents a recent literature review, including a brief description of data pre-processing, data-driven modelling strategies (i.e., univariate or multivariate), machine learning algorithms (i.e., advantages and disadvantages), hybrid models, and performance metrics. Combining various prediction methods to create efficient hybrid models has become the most popular use in recent years. Accordingly, hybrid models have been increasingly used for predicting drought. As such, these models will be extensively reviewed, including preprocessing-based hybrid models, parameter optimisation-based hybrid models, and hybridisation of components combination-based with preprocessing-based hybrid models. In addition, using statistical criteria, such as RMSE, MAE, NSE, MPE, SI, BIC, AIC, and AAD, is essential to evaluate the performance of the models.

Item Type: Article
Uncontrolled Keywords: 0502 Environmental Science and Management; 0503 Soil Sciences
Subjects: T Technology > T Technology (General)
T Technology > TD Environmental technology. Sanitary engineering
Divisions: Civil Engineering & Built Environment
Publisher: MDPI
SWORD Depositor: A Symplectic
Date Deposited: 27 Jun 2022 10:23
Last Modified: 27 Jun 2022 10:30
DOI or ID number: 10.3390/hydrology9070115
URI: https://researchonline.ljmu.ac.uk/id/eprint/17159
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