Modeling and forecasting rainfall patterns in India: a time series analysis with XGBoost algorithm

被引:0
作者
Pradeep Mishra
Abdullah Mohammad Ghazi Al Khatib
Shikha Yadav
Soumik Ray
Achal Lama
Binita Kumari
Divya Sharma
Ramesh Yadav
机构
[1] Jawaharlal Nehru Krishi Vishwavidyalaya,College of Agriculture
[2] Damascus University,Department of Banking and Insurance, Faculty of Economics
[3] University of Delhi,Department of Geography, Miranda House
[4] Centurion University of Technology and Management,Department of Agricultural Economics
[5] ICAR,Department of Fisheries
[6] Indian Agricultural Statistics Research Institute,undefined
[7] Rashtriya Kisan (P.G.) College,undefined
[8] Central Institute of Coastal Engineering for Fisheries,undefined
[9] Ministry of Fisheries,undefined
[10] Animal Husbandry and Dairying,undefined
[11] Government of India,undefined
[12] Ministry of Micro,undefined
[13] Small and Medium Enterprises,undefined
来源
Environmental Earth Sciences | 2024年 / 83卷
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;
D O I
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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.
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