Extreme climate index estimation and projection in association with enviro-meteorological parameters using random forest-ARIMA hybrid model over the Vidarbha region, India

被引:5
作者
Kumar, Navneet [1 ,2 ]
Middey, Anirban [1 ,2 ]
机构
[1] CSIR, Natl Environm Engn Res Inst, Nagpur 440020, India
[2] Acad Sci & Innovat Res AcSIR, Ghaziabad 201002, India
关键词
Air pollution; SPI; Extreme climate; ARIMA; Vidarbha region; STANDARDIZED PRECIPITATION INDEX; AIR-QUALITY; DROUGHT; VARIABILITY; SENSITIVITY; PM2.5;
D O I
10.1007/s10661-022-10902-2
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
This study aims to estimate and analyse extreme climate indices such as standardised precipitation index (SPI) coupled with enviro-met (air pollutants and meteorological) parameters over the Vidarbha region from 1980 to 2019. Seasonal SPI, also known as the draught index, is derived from rainfall data using the R language. An attempt is made to determine the best combination of enviro-met on SPI using the random forest (RF) models. The study region is divided into four zones to assess the microclimatic impact on the forecast model. Three sets of data combinations, viz., meteorological and air pollution parameters, are applied for SPI prediction using RF. The autoregressive integrated moving average (ARIMA) model is also used for a future scenario projection. It is observed from the projection results that the drought severity is enhancing with time. The drought severity scale from 1980 to 1989 is found to be between - 1 and 1, but the scale increases from 1990 to 2019 (- 3). From 1990 to 2019, SPI's negative (-) scale has become more prominent in all Vidarbha regions. These trends are indicative of drought severity and will have a significant impact on both life and property.
引用
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页数:23
相关论文
共 45 条
[1]   Comparison of multiple linear and nonlinear regression, autoregressive integrated moving average, artificial neural network, and wavelet artificial neural network methods for urban water demand forecasting in Montreal, Canada [J].
Adamowski, Jan ;
Chan, Hiu Fung ;
Prasher, Shiv O. ;
Ozga-Zielinski, Bogdan ;
Sliusarieva, Anna .
WATER RESOURCES RESEARCH, 2012, 48
[2]  
[Anonymous], 2012, Standardized Precipitation Index User Guide
[3]   Trend analysis of precipitation and drought in the Aegean region, Turkey [J].
Bacanli, Ulker Guner .
METEOROLOGICAL APPLICATIONS, 2017, 24 (02) :239-249
[4]  
Box G. E. P., 1970, Time series analysis, forecasting and control
[5]   Random forests [J].
Breiman, L .
MACHINE LEARNING, 2001, 45 (01) :5-32
[6]   Statistical Uncertainty Estimation Using Random Forests and Its Application to Drought Forecast [J].
Chen, Junfei ;
Li, Ming ;
Wang, Weiguang .
MATHEMATICAL PROBLEMS IN ENGINEERING, 2012, 2012
[7]   Aerosol impacts on warm-cloud microphysics and drizzle in a moderately polluted environment [J].
Chen, Ying-Chieh ;
Wang, Sheng-Hsiang ;
Min, Qilong ;
Lu, Sarah ;
Lin, Pay-Liam ;
Lin, Neng-Huei ;
Chung, Kao-Shan ;
Joseph, Everette .
ATMOSPHERIC CHEMISTRY AND PHYSICS, 2021, 21 (06) :4487-4502
[8]  
Chen-hua Chung B., 2000, J HYDROL ENG
[9]   Short-Term Spatio-Temporal Drought Forecasting Using Random Forests Model at New South Wales, Australia [J].
Dikshit, Abhirup ;
Pradhan, Biswajeet ;
Alamri, Abdullah M. .
APPLIED SCIENCES-BASEL, 2020, 10 (12)
[10]  
Edwards D. C., 1997, 972 COL STAT U DEPT