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Drought Forecasting...
Drought Forecasting: A Review and Assessment of the Hybrid Techniques and Data Pre-Processing
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- Alawsi, Mustafa A. (författare)
- Department of Building and Construction Techniques-Kut Technical Institute, Middle Technical University, Wasit 52001, Iraq; Department of Civil Engineering, Wasit University, Wasit 52001, Iraq
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- Zubaidi, Salah L. (författare)
- Department of Civil Engineering, Wasit University, Wasit 52001, Iraq
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- Al-Bdairi, Nabeel Saleem Saad (författare)
- Department of Civil Engineering, Wasit University, Wasit 52001, Iraq
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- Al-Ansari, Nadhir, 1947- (författare)
- Luleå tekniska universitet,Geoteknologi
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- Hashim, Khalid (författare)
- Built Environment and Sustainable Technologies (BEST) Research Institute, Liverpool John Moores University, Liverpool L3 3AF, UK; Department of Environment Engineering, Babylon University, Babylon 51001, Iraq
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(creator_code:org_t)
- 2022-06-26
- 2022
- Engelska.
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Ingår i: Hydrology. - : MDPI. - 2306-5338. ; 9:7
- Relaterad länk:
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https://doi.org/10.3...
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https://ltu.diva-por... (primary) (Raw object)
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https://urn.kb.se/re...
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https://doi.org/10.3...
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Abstract
Ämnesord
Stäng
- 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.
Ämnesord
- SAMHÄLLSVETENSKAP -- Ekonomi och näringsliv -- Nationalekonomi (hsv//swe)
- SOCIAL SCIENCES -- Economics and Business -- Economics (hsv//eng)
Nyckelord
- data pre-processing
- drought
- hybrid models
- machine learning
- performance metrics
- Soil Mechanics
- Geoteknik
Publikations- och innehållstyp
- ref (ämneskategori)
- for (ämneskategori)
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