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DOI	10.1007/s12665-020-08971-y
	Hybrid wavelet packet machine learning approaches for drought modeling
	Das, Prabal; Naganna, Sujay Raghavendra; Deka, Paresh Chandra; Pushparaj, Jagalingam
通讯作者	Naganna, SR
来源期刊	ENVIRONMENTAL EARTH SCIENCES
ISSN	1866-6280
EISSN	1866-6299
出版年	2020
卷号	79 期号:10
英文摘要	Among all the natural disasters, drought has the most catastrophic encroachment on the surrounding and environment. Gulbarga, one of the semi-arid districts of Karnataka state, India receives about 700 mm of average annual rainfall and is drought inclined. In this study, the forecasting of drought for the district has been carried out for a lead time of 1 month and 6 months. The multi-temporal Standardized Precipitation Index (SPI) has been used as the drought quantifying parameter due to the fact that it is calculated on the basis of one simplest parameter, i.e., rainfall and additionally due to its ease of use. The fine resolution daily gridded precipitation data (0.25o x 0.25o) procured from Indian Meteorological Department (IMD) of 21 grid locations within the study area have been used for the analysis. Forecasting of drought plays a significant role in drought preparedness and mitigation plans. With the advent of machine learning (ML) techniques over the past few decades, forecasting of any hydrologic event has become easier and more accurate. However, the use of these techniques for drought forecasting is still obscure. In this study, Artificial Neural Network (ANN) and Support Vector Regression (SVR) techniques have been employed to examine their accuracy in drought forecasting over shorter and longer lead times. Furthermore, two hybrid approaches have been formulated by coupling a data transformation method with each of the aforementioned ML approaches. At the outset, pre-processing of input data (i.e., SPI) has been carried out using Wavelet Packet Transform (WPT) and then used as inputs to ANN and SVR models to induce hybrid WP-ANN and WP-SVR models. The performance of the hybrid models has been evaluated based on the statistical indices such as R-2 (co-efficient of determination), RMSE (Root Mean Square Error), and MAE (Mean Absolute Error). The results showed that the hybrid techniques have better forecast performance than the standalone machine learning approaches. Hybrid WP-ANN model performed relatively better than WP-SVR model for most of the grid locations. Also, the forecasting results deteriorated as the lead time increased from 1 to 6 months.
英文关键词	Drought SPI Machine learning ANN SVR WP-ANN WP-SVR
类型	Article
语种	英语
收录类别	SCI-E
WOS记录号	WOS:000536301000003
WOS关键词	AWASH RIVER-BASIN ; NEURAL-NETWORKS ; INDEX ; TRANSFORMS ; SEVERITY
WOS类目	Environmental Sciences ; Geosciences, Multidisciplinary ; Water Resources
WOS研究方向	Environmental Sciences & Ecology ; Geology ; Water Resources
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/324571
作者单位	[Das, Prabal] Indian Inst Technol ISM, Dept Civil Engn, Dhanbad 826004, Bihar, India; [Naganna, Sujay Raghavendra] Shri Madhwa Vadiraja Inst Technol & Management, Dept Civil Engn, Udupi 574115, Bantakal, India; [Deka, Paresh Chandra] Natl Inst Technol Karnataka, Dept Appl Mech & Hydraul, Mangalore 575025, India; [Pushparaj, Jagalingam] Vellore Inst Technol, Sch Comp Sci & Engn, Vellore 632014, Tamil Nadu, India
推荐引用方式 GB/T 7714	Das, Prabal,Naganna, Sujay Raghavendra,Deka, Paresh Chandra,et al. Hybrid wavelet packet machine learning approaches for drought modeling[J],2020,79(10).
APA	Das, Prabal,Naganna, Sujay Raghavendra,Deka, Paresh Chandra,&Pushparaj, Jagalingam.(2020).Hybrid wavelet packet machine learning approaches for drought modeling.ENVIRONMENTAL EARTH SCIENCES,79(10).
MLA	Das, Prabal,et al."Hybrid wavelet packet machine learning approaches for drought modeling".ENVIRONMENTAL EARTH SCIENCES 79.10(2020).