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DOI	10.1007/s12665-024-11481-w
	Modeling and forecasting rainfall patterns in India: a time series analysis with XGBoost algorithm
发表日期	2024
ISSN	1866-6280
EISSN	1866-6299
起始页码	83
结束页码	6
卷号	83 期号:6
英文摘要	This study utilizes time series analysis and machine learning techniques to model and forecast rainfall patterns across different seasons in India. The statistical models, i.e., autoregressive integrated moving average (ARIMA) and state space model and machine learning models, i.e., Support Vector Machine, Artificial Neural Network and Random Forest Model were developed and their performance was compared against XGBoost, an advanced machine learning algorithm, using training and testing datasets. The results demonstrate the superior accuracy of XGBoost compared to the statistical models in capturing complex nonlinear rainfall patterns. While ARIMA models tend to overfit the training data, state space models prove more robust to outliers in the testing set. Diagnostic checks show the models adequately capture the time series properties. The analysis indicates essential unchanging rainfall patterns in India for 2023-2027, with implications for water resource management and climate-sensitive sectors like agriculture and power generation. Overall, the study highlights the efficacy of modern machine learning approaches like XGBoost for forecasting complex meteorological time series. The framework presented enables rigorous validation and selection of optimal techniques. Further applications of such sophisticated data analysis can significantly enhance planning and research on the Indian monsoons amidst climate change challenges.
英文关键词	Time series; ARIMA models; State space models; Machine learning; XGBoost; Rainfall; Forecasting; Water resource management; Agriculture; Hydroelectric power generation; Climate change; Environmental management
语种	英语
WOS研究方向	Environmental Sciences & Ecology ; Geology ; Water Resources
WOS类目	Environmental Sciences ; Geosciences, Multidisciplinary ; Water Resources
WOS记录号	WOS:001176103300004
来源期刊	ENVIRONMENTAL EARTH SCIENCES
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/288103
作者单位	Damascus University; University of Delhi; Centurion University of Technology & Management; Indian Council of Agricultural Research (ICAR); ICAR - Indian Agricultural Research Institute
推荐引用方式 GB/T 7714	. Modeling and forecasting rainfall patterns in India: a time series analysis with XGBoost algorithm[J],2024,83(6).
APA	(2024).Modeling and forecasting rainfall patterns in India: a time series analysis with XGBoost algorithm.ENVIRONMENTAL EARTH SCIENCES,83(6).
MLA	"Modeling and forecasting rainfall patterns in India: a time series analysis with XGBoost algorithm".ENVIRONMENTAL EARTH SCIENCES 83.6(2024).

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