干旱区生态环境知识资源中心(Arid): A Theoretical Approach for Forecasting Different Types of Drought Simultaneously, Using Entropy Theory and Machine-Learning Methods

Arid

DOI	10.3390/ijgi9120701
	A Theoretical Approach for Forecasting Different Types of Drought Simultaneously, Using Entropy Theory and Machine-Learning Methods
	Aghelpour, Pouya; Mohammadi, Babak; Biazar, Seyed Mostafa; Kisi, Ozgur; Sourmirinezhad, Zohreh
通讯作者	Mohammadi, B (corresponding author), Hohai Univ, Coll Hydrol & Water Resources, Nanjing 210098, Peoples R China.
来源期刊	ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION
EISSN	2220-9964
出版年	2020
卷号	9 期号:12
英文摘要	Precipitation deficit can affect different natural resources such as water, soil, rivers and plants, and cause meteorological, hydrological and agricultural droughts. Multivariate drought indexes can theoretically show the severity and weakness of various drought types simultaneously. This study introduces an approach for forecasting joint deficit index (JDI) and multivariate standardized precipitation index (MSPI) by using machine-learning methods and entropy theory. JDI and MSPI were calculated for the 1-12 months' time window (JDI(1-12) and MSPI1-12), using monthly precipitation data. The methods implemented for forecasting are group method of data handling (GMDH), generalized regression neural network (GRNN), least squared support vector machine (LSSVM), adaptive neuro-fuzzy inference system (ANFIS) and ANFIS optimized with three heuristic optimization algorithms, differential evolution (DE), genetic algorithm (GA) and particle swarm optimization (PSO) as meta-innovative methods (ANFIS-DE, ANFIS-GA and ANFIS-PSO). Monthly precipitation, monthly temperature and previous amounts of the index's values were used as inputs to the models. Data from 10 synoptic stations situated in the widest climatic zone of Iran (extra arid-cold climate) were employed. Optimal model inputs were selected by gamma test and entropy theory. The evaluation results, which were given using mean absolute error (MAE), root mean squared error (RMSE) and Willmott index (WI), show that the machine learning and meta-innovative models can present acceptable forecasts of general drought's conditions. The algorithms DE, GA and PSO, could improve the ANFIS's performance by 39.4%, 38.7% and 22.6%, respectively. Among all the applied models, the GMDH shows the best forecasting accuracy with MAE = 0.280, RMSE = 0.374 and WI = 0.955. In addition, the models could forecast MSPI better than JDI in the majority of cases (stations). Among the two methods used to select the optimal inputs, it is difficult to select one as a better input selector, but according to the results, more attention can be paid to entropy theory in drought studies.
英文关键词	entropy theory drought forecasting multivariate standardized precipitation index joint deficit index machine learning heuristic optimization algorithms
类型	Article
语种	英语
开放获取类型	gold
收录类别	SCI-E
WOS记录号	WOS:000602050000001
WOS关键词	SUPPORT VECTOR REGRESSION ; STANDARDIZED PRECIPITATION ; CLIMATE INDEXES ; NEURAL-NETWORK ; DATA SELECTION ; RIVER-BASIN ; PREDICTION ; OPTIMIZATION ; ALGORITHMS ; MODELS
WOS类目	Computer Science, Information Systems ; Geography, Physical ; Remote Sensing
WOS研究方向	Computer Science ; Physical Geography ; Remote Sensing
来源机构	河海大学
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/369228
作者单位	[Aghelpour, Pouya; Sourmirinezhad, Zohreh] Bu Ali Sina Univ, Fac Agr, Dept Water Engn, Hamadan 6517838695, Hamadan, Iran; [Mohammadi, Babak] Hohai Univ, Coll Hydrol & Water Resources, Nanjing 210098, Peoples R China; [Biazar, Seyed Mostafa] Univ Tabriz, Fac Agr, Dept Water Engn, Tabriz 5166616471, Iran; [Kisi, Ozgur] Ilia State Univ, Dept Civil Engn, GE-0162 Tbilisi, Georgia; [Kisi, Ozgur] Duy Tan Univ, Inst Res & Dev, Da Nang 550000, Vietnam
推荐引用方式 GB/T 7714	Aghelpour, Pouya,Mohammadi, Babak,Biazar, Seyed Mostafa,et al. A Theoretical Approach for Forecasting Different Types of Drought Simultaneously, Using Entropy Theory and Machine-Learning Methods[J]. 河海大学,2020,9(12).
APA	Aghelpour, Pouya,Mohammadi, Babak,Biazar, Seyed Mostafa,Kisi, Ozgur,&Sourmirinezhad, Zohreh.(2020).A Theoretical Approach for Forecasting Different Types of Drought Simultaneously, Using Entropy Theory and Machine-Learning Methods.ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION,9(12).
MLA	Aghelpour, Pouya,et al."A Theoretical Approach for Forecasting Different Types of Drought Simultaneously, Using Entropy Theory and Machine-Learning Methods".ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION 9.12(2020).