A machine learning model for drought tracking and forecasting using remote precipitation data and a standardized precipitation index from arid regions

被引：45

作者：

Bouaziz, Moncef ^{[1
,2
]}

Medhioub, Emna ^{[1
,2
]}

Csaplovisc, Elmar ^{[2
]}

机构：

[1] Univ Sfax, Natl Sch Engn Sfax, Sfax, Tunisia

[2] Tech Univ Dresden, Fac Environm Sci, Inst Photogrammetry & Remote Sensing, Helmholtzstr 10, D-01069 Dresden, Germany

来源：

JOURNAL OF ARID ENVIRONMENTS | 2021年 / 189卷

关键词：

Drought; Standardized precipitation index; CHIRPS; Drought forecast; Extreme learning machine; ARTIFICIAL NEURAL-NETWORKS; FUZZY INFERENCE SYSTEM; RIVER-BASIN; TIME-SERIES; OPTIMIZATION; QUEENSLAND; PREDICTION;

D O I：

10.1016/j.jaridenv.2021.104478

中图分类号：

Q14 [生态学（生物生态学）];

学科分类号：

071012 ; 0713 ;

摘要：

Drought is a catastrophe that impacts agriculture and causes economic and social damage. An effective monitoring and forecasting system is needed to assess the extent of droughts and to mitigate their effects at both spatial and temporal levels. To this end, we used a Standardized Precipitation Index (SPI) in various timescales to classify and track drought events based on CHIRPS rainfall data for the period between 1981 and 2019. Three models (M1, M2, M3) were then tested for annual drought prediction (SPI_12) using precipitation data and the lagged SPI as input variables. Extreme Learning Machine algorithms displayed rapid drought prediction, with high accuracy on different timescales (0.7-0.8 R-2).

引用

页数：9

共 45 条

[1] Input selection and optimisation for monthly rainfall forecasting in Queensland, Australia, using artificial neural networks [J].

Abbot, John ;

Marohasy, Jennifer .

ATMOSPHERIC RESEARCH, 2014, 138 :166-178

[2] Application of artificial neural networks to rainfall forecasting in Queensland, Australia [J].

Abbot, John ;

Marohasy, Jennifer .

ADVANCES IN ATMOSPHERIC SCIENCES, 2012, 29 (04) :717-730

[3]

Adebola A.O., 2019, CLIMATE CHANGE, V5, P188

[4]

[Anonymous], 2013, MITIG ADAPT STRATEGI

[5] Adaptive Neuro-Fuzzy Inference System for drought forecasting [J].

Bacanli, Ulker Guner ;

Firat, Mahmut ;

Dikbas, Fatih .

STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT, 2009, 23 (08) :1143-1154

[6]

Byun HR, 1999, J CLIMATE, V12, P2747, DOI 10.1175/1520-0442(1999)012<2747:OQODSA>2.0.CO

[7]

[8] Near-infrared reflectance spectroscopy-principal components regression analyses of soil properties [J].

Chang, CW ;

Laird, DA ;

Mausbach, MJ ;

Hurburgh, CR .

SOIL SCIENCE SOCIETY OF AMERICA JOURNAL, 2001, 65 (02) :480-490

[9] Flood Forecasting Based on an Improved Extreme Learning Machine Model Combined with the Backtracking Search Optimization Algorithm [J].

Chen, Lu ;

Sun, Na ;

Zhou, Chao ;

Zhou, Jianzhong ;

Zhou, Yanlai ;

Zhang, Junhong ;

Zhou, Qing .

WATER, 2018, 10 (10)

[10] Evaluation of Tropical Rainfall Measuring Mission (TRMM) satellite precipitation products for drought monitoring over the middle and lower reaches of the Yangtze River Basin, China [J].

Chen, Shaodan ;

Zhang, Liping ;

Zhang, Yanjun ;

Guo, Mengyao ;

Liu, Xin .

JOURNAL OF GEOGRAPHICAL SCIENCES, 2020, 30 (01) :53-67

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