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

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Fältnamn	Indikatorer	Metadata
000		03788naa a2200457 4500
001		oai:DiVA.org:ltu-91927
003		SwePub
008		220627s2022 \| \|\|\|\|\|\|\|\|\|\|\|000 \|\|eng\|
024	7	a https://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-919272 URI
024	7	a https://doi.org/10.3390/hydrology90701152 DOI
040		a (SwePub)ltu
041		a engb eng
042		9 SwePub
072	7	a ref2 swepub-contenttype
072	7	a for2 swepub-publicationtype
100	1	a Alawsi, Mustafa A.u Department of Building and Construction Techniques-Kut Technical Institute, Middle Technical University, Wasit 52001, Iraq; Department of Civil Engineering, Wasit University, Wasit 52001, Iraq4 aut
245	1 0	a Drought Forecasting: A Review and Assessment of the Hybrid Techniques and Data Pre-Processing
264		c 2022-06-26
264	1	b MDPI,c 2022
338		a electronic2 rdacarrier
500		a Validerad;2022;Nivå 2;2022-06-27 (joosat);
520		a 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.
650	7	a SAMHÄLLSVETENSKAPx Ekonomi och näringslivx Nationalekonomi0 (SwePub)502012 hsv//swe
650	7	a SOCIAL SCIENCESx Economics and Businessx Economics0 (SwePub)502012 hsv//eng
653		a data pre-processing
653		a drought
653		a hybrid models
653		a machine learning
653		a performance metrics
653		a Soil Mechanics
653		a Geoteknik
700	1	a Zubaidi, Salah L.u Department of Civil Engineering, Wasit University, Wasit 52001, Iraq4 aut
700	1	a Al-Bdairi, Nabeel Saleem Saadu Department of Civil Engineering, Wasit University, Wasit 52001, Iraq4 aut
700	1	a Al-Ansari, Nadhir,d 1947-u Luleå tekniska universitet,Geoteknologi4 aut0 (Swepub:ltu)nadhir
700	1	a Hashim, Khalidu Built Environment and Sustainable Technologies (BEST) Research Institute, Liverpool John Moores University, Liverpool L3 3AF, UK; Department of Environment Engineering, Babylon University, Babylon 51001, Iraq4 aut
710	2	a Department of Building and Construction Techniques-Kut Technical Institute, Middle Technical University, Wasit 52001, Iraq; Department of Civil Engineering, Wasit University, Wasit 52001, Iraqb Department of Civil Engineering, Wasit University, Wasit 52001, Iraq4 org
773	0	t Hydrologyd : MDPIg 9:7q 9:7x 2306-5338
856	4	u https://doi.org/10.3390/hydrology9070115y Fulltext
856	4	u https://ltu.diva-portal.org/smash/get/diva2:1677224/FULLTEXT01.pdfx primaryx Raw objecty fulltext:print
856	4 8	u https://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-91927
856	4 8	u https://doi.org/10.3390/hydrology9070115

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