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Estimation of SPEI Meteorological Drought using Machine Learning Algorithms

Mokhtar, Ali (författare)
State Key Laboratory of Soil Erosion and Dryland Farming on the Loess Plateau, Institute of Soil and Water Conservation, Northwest Agriculture and Forestry University, Chinese Academy of Sciences and Ministry of Water Resources, Yangling 712100, China; Department of Agricultural Engineering, Faculty of Agriculture, Cairo University, Giza 12613, Egypt
Jalali, Mohammadnabi (författare)
Department of Water Engineering, Aburaihan Campus, University of Tehran, Tehran, Iran
He, Hongming (författare)
State Key Laboratory of Soil Erosion and Dryland Farming on the Loess Plateau, Institute of Soil and Water Conservation, Northwest Agriculture and Forestry University, Chinese Academy of Sciences and Ministry of Water Resources, Yangling 712100, China; School of Geographic Sciences, East China Normal University, Shanghai 210062, China
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Al-Ansari, Nadhir, 1947- (författare)
Luleå tekniska universitet,Geoteknologi
Elbeltagi, Ahmed (författare)
College of Environmental and Resource Sciences, Zhejiang University, Hangzhou, 310058, China; Agricultural Engineering Department, Faculty of Agriculture, Mansoura University, Mansoura, 35516, Egypt
Alsafadi, Karam (författare)
School of Geographical Sciences, Nanjing University of Information Science and Technology, Nanjing 210044, China
Abdo, Hazem Ghassan (författare)
Geography Department, Faculty of Arts and Humanities, Tartous University, Tartous, Syria
Sammen, Saad Sh. (författare)
Department of Civil Engineering, College of Engineering, University of Diyala, Diyala Governorate, Iraq
Gyasi-Agyei, Yeboah (författare)
School of Engineering and Built Environment, Griffith University, Nathan QLD 4111, Australia
Rodrigo-Comino, Jesús (författare)
Department of Physical Geography, University of Trier, 54296 Trier, Germany; Soil Erosion and Degradation Research Group, Department of Geography, Valencia University, Blasco Ibàñez, 28, 46010 Valencia, Spain
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 (creator_code:org_t)
IEEE, 2021
2021
Engelska.
Ingår i: IEEE Access. - : IEEE. - 2169-3536. ; 9, s. 65503-65523
  • Tidskriftsartikel (refereegranskat)
Abstract Ämnesord
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  • Accurate estimation of drought events is vital for the mitigation of their adverse consequences on water resources, agriculture and ecosystems. Machine learning algorithms are promising methods for drought prediction as they require less time, minimal inputs, and are relatively less complex than dynamic or physical models. In this study, a combination of machine learning with the Standardized Precipitation Evapotranspiration Index (SPEI) is proposed for analysis of drought within a representative case study in the Tibetan Plateau, China, for the period of 1980-2019. Two timescales of 3 months (SPEI-3) and 6 months (SPEI-6) aggregation were considered. Four machine learning models of Random Forest (RF), the Extreme Gradient Boost (XGB), the Convolutional neural network (CNN) and the Long-term short memory (LSTM) were developed for the estimation of the SPEIs. Seven scenarios of various combinations of climate variables as input were adopted to build the models. The best models were XGB with scenario 5 (precipitation, average temperature, minimum temperature, maximum temperature, wind speed and relative humidity) and RF with scenario 6 (precipitation, average temperature, minimum temperature, maximum temperature, wind speed, relative humidity and sunshine) for estimating SPEI-3. LSTM with scenario 4 (precipitation, average temperature, minimum temperature, maximum temperature, wind speed) was relatively better for SPEI-6 estimation. The best model for SPEI-6 was XGB with scenario 5 and RF with scenario 7 (all input climate variables, i.e., scenario 6 + solar radiation). Based on the NSE index, the performances of XGB and RF models are classified as good fits for scenarios 4 to 7 for both timescales. The developed models produced satisfactory results and they could be used as a rapid tool for decision making by water-managers.

Ämnesord

TEKNIK OCH TEKNOLOGIER  -- Samhällsbyggnadsteknik -- Geoteknik (hsv//swe)
ENGINEERING AND TECHNOLOGY  -- Civil Engineering -- Geotechnical Engineering (hsv//eng)

Nyckelord

Drought events
SPEI
Machine learning
Extreme Gradient Boost
Tibetan Plateau
Geoteknik
Soil Mechanics

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