干旱区生态环境知识资源中心(Arid): Assessment and Prediction of Groundwater using Geospatial and ANN Modeling

Arid

DOI	10.1007/s11269-021-02874-8
	Assessment and Prediction of Groundwater using Geospatial and ANN Modeling
	Dadhich, Ankita P.; Goyal, Rohit; Dadhich, Pran N.
通讯作者	Dadhich, AP (corresponding author), Malaviya Natl Inst Technol, Dept Civil Engn, JLN Marg, Jaipur 302017, Rajasthan, India.
来源期刊	WATER RESOURCES MANAGEMENT
ISSN	0920-4741
EISSN	1573-1650
出版年	2021
英文摘要	In semi-arid regions, the deterioration in groundwater quality and drop in water level upshots the importance of water resource management for drinking and irrigation. Therefore geospatial techniques could be integrated with mathematical models for accurate spatiotemporal mapping of groundwater risk areas at the village level. In the present study, changes in water level, quality patterns, and future trends were analyzed using eight years (2012-2019) groundwater data for 171 villages of the Phagi tehsil, Jaipur district. Kriging interpolation method was used to draw spatial maps for the pre-monsoon season. These datasets were integrated with three different time series forecasting models (Simple Exponential Smoothing, Holt's Trend Method, ARIMA) and Artificial Neural Network models for accurate prediction of groundwater level and quality parameters. Results reveal that the ANN model can describe groundwater level and quality parameters more accurately than the time series forecasting models. The change in groundwater level was observed with more than 4.0 m rise in 81 villages during 2012-2013, whereas ANN predicted results of 2023-2024 predict no rise in water level > 4.0 m. However, based on predicted results of 2024, the water level will drop by more than 6.0 m in 16 villages of Phagi. Assessment of water quality index reveals unfit groundwater in 74% villages for human consumption in 2024. This time series and projected groundwater level and quality at the micro-level can assist decision-makers in sustainable groundwater management.
英文关键词	Groundwater Water quality Geospatial ARIMA Artificial neural network
类型	Article ; Early Access
语种	英语
收录类别	SCI-E
WOS记录号	WOS:000661030500001
WOS关键词	SEMIARID REGION ; QUALITY ; GIS ; REGRESSION ; DRINKING ; INDEX
WOS类目	Engineering, Civil ; Water Resources
WOS研究方向	Engineering ; Water Resources
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/352480
作者单位	[Dadhich, Ankita P.; Goyal, Rohit] Malaviya Natl Inst Technol, Dept Civil Engn, JLN Marg, Jaipur 302017, Rajasthan, India; [Dadhich, Pran N.] Poornima Inst Engn & Technol, Dept Civil Engn, ISI 2, Jaipur 302022, Rajasthan, India
推荐引用方式 GB/T 7714	Dadhich, Ankita P.,Goyal, Rohit,Dadhich, Pran N.. Assessment and Prediction of Groundwater using Geospatial and ANN Modeling[J],2021.
APA	Dadhich, Ankita P.,Goyal, Rohit,&Dadhich, Pran N..(2021).Assessment and Prediction of Groundwater using Geospatial and ANN Modeling.WATER RESOURCES MANAGEMENT.
MLA	Dadhich, Ankita P.,et al."Assessment and Prediction of Groundwater using Geospatial and ANN Modeling".WATER RESOURCES MANAGEMENT (2021).