干旱区生态环境知识资源中心(Arid): Application of Hybrid ANN Techniques for Drought Forecasting in the Semi-Arid Region of India

Arid

DOI	10.1007/s10661-023-11631-w
	Application of Hybrid ANN Techniques for Drought Forecasting in the Semi-Arid Region of India
	Wable, Pawan S.; Jha, Madan Kumar; Adamala, Sirisha; Tiwari, Mukesh Kumar; Biswal, Sabinaya
通讯作者	Jha, MK
来源期刊	ENVIRONMENTAL MONITORING AND ASSESSMENT
ISSN	0167-6369
EISSN	1573-2959
出版年	2023
卷号	195 期号:9
英文摘要	The intensity and frequency of diverse hydro-meteorological disasters viz., extreme droughts, severe floods, and cyclones have increasing trends due to unsustainable management of land and water resources, coupled with increasing industrialization, urbanization and climate change. This study focuses on the forecasting of drought using selected Artificial Neural Network (ANN)-based models to enable decision-makers to improve regional water management plans and disaster mitigation/reduction plans. Four ANN models were developed in this study, viz., one conventional ANN model and three hybrid ANN models: (a) Wavelet based-ANN (WANN), (b) Bootstrap based-ANN (BANN), and (c) Wavelet-Bootstrap based-ANN (WBANN). The Standardized Precipitation Evapotranspiration Index (SPEI), the best drought index identified for the study area, was used as a variable for drought forecasting. Three drought indices, such as SPEI-3, SPEI-6 and SPEI-12 respectively representing short-term, intermediate-term, and long-term drought conditions, were forecasted for 1-month to 3-month lead times for six weather stations over the study area. Both statistical and graphical indicators were considered to assess the performance of the developed models. For the hybrid wavelet model, the performance was evaluated for different vanishing moments of Daubechies wavelets and decomposition levels. The best-performing bootstrap-based model was further used for analysing the uncertainty associated with different drought forecasts. Among the models developed for drought forecasting for 1 to 3 months, the performances of the WANN and WBANN models are superior to the simple ANN and BANN models for the SPEI-3, SPEI-6, and SPEI-12 up to the 3-month lead time. The performance of the WANN and WBANN models is the best for SPEI-12 (MAE = 0.091-0.347, NSE = 0.873-0.982) followed by SPEI-6 (MAE = 0.258-0.593; NSE = 0.487-0.848) and SPEI-3 (MAE = 0.332-0.787, NSE = 0.196-0.825) for all the stations up to 3-month lead time. This finding is supported by the WBANN analyze uncertainties as narrower band width for SPEI-12 (0.240-0.898) as compared to SPEI-6 (0.402-1.62) and SPEI-3 (0.474-2.304). Therefore, the WBANN model is recommended for the early warning of drought events as it facilitates the uncertainty analysis of drought forecasting results.
英文关键词	Drought forecasting SPEI ANN Wavelet Bootstrap Hybrid soft computing techniques
类型	Article
语种	英语
收录类别	SCI-E
WOS记录号	WOS:001064031700007
WOS关键词	ARTIFICIAL NEURAL-NETWORKS ; DURATION-FREQUENCY CURVES ; WAVELET TRANSFORMS ; RIVER-BASIN ; STANDARDIZED PRECIPITATION ; EXTREME PRECIPITATION ; UNCERTAINTY ANALYSIS ; PREDICTION ; INDEX ; SEVERITY
WOS类目	Environmental Sciences
WOS研究方向	Environmental Sciences & Ecology
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/396185
推荐引用方式 GB/T 7714	Wable, Pawan S.,Jha, Madan Kumar,Adamala, Sirisha,et al. Application of Hybrid ANN Techniques for Drought Forecasting in the Semi-Arid Region of India[J],2023,195(9).
APA	Wable, Pawan S.,Jha, Madan Kumar,Adamala, Sirisha,Tiwari, Mukesh Kumar,&Biswal, Sabinaya.(2023).Application of Hybrid ANN Techniques for Drought Forecasting in the Semi-Arid Region of India.ENVIRONMENTAL MONITORING AND ASSESSMENT,195(9).
MLA	Wable, Pawan S.,et al."Application of Hybrid ANN Techniques for Drought Forecasting in the Semi-Arid Region of India".ENVIRONMENTAL MONITORING AND ASSESSMENT 195.9(2023).