气候变化领域集成服务门户(CCPortal): Analysis and Forecasting of Temporal Rainfall Variability Over Hundred Indian Cities Using Deep Learning Approaches

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DOI	10.1007/s41748-024-00396-y
	Analysis and Forecasting of Temporal Rainfall Variability Over Hundred Indian Cities Using Deep Learning Approaches
	Singh, Sanjeev; Mukherjee, Asmita; Panda, Jagabandhu; Choudhury, Animesh; Bhattacharyya, Saugat
发表日期	2024
ISSN	2509-9426
EISSN	2509-9434
英文摘要	India, a topographically and meteorologically rich country, has a vast range of rainfall variability. The impacts could be realized across various sectors, including agriculture, industry, tourism, etc. With the increasing impacts of changing climate, more intense extreme rainfall events are expected to trigger severe floods, landslides, etc., in future. Therefore, it is imperative to make a precise prediction so that the intensity of the impacts on life and property could be reduced. Rather than using the computationally expensive conventional numerical modeling, the data driven AI/ML frameworks could be adopted in forecasting rainfall trends and patterns. The present work is an effort in this direction, which uses a monthly accumulated gridded rainfall dataset and a monthly averaged daily mean temperature dataset from 1901 to 2021 with a resolution of 0.5 degrees x 0.5 degrees for the analysis and prediction of yearly rainfall patterns across India through a city-specific approach. Accordingly, comparison of deep learning (DL) algorithms like Long Short Term Memory (LSTM), Bi-directional LSTM (BiLSTM), Gated Recurrent Unit (GRU), and Convolution 1D LSTM (Conv1DLSTM) are performed for long-term rainfall prediction over hundred cities of India. The statistical parameters including Root means square error (RMSE), Mean absolute error (MAE), Coefficient of Determination (R2), and Nash-Sutcliffe efficiency (NSE) are estimated to assess the robustness of the considered DL models and identify the better performing one. The initial results indicated that for univariate forecasting of accumulated monthly rainfall, Conv1DLSTM performed better while for bivariate forecasting, GRU performed better than the others. City-based rainfall trend analysis using the seasonal Mann-Kendall (MK) test suggested increasing trend over northwestern region, decreasing trend over northeastern region and no significant trend over other cities. The DL model-based forecast realized that temporal rainfall variability may be altered in future over some cities, attributable to the changing climate scenario. These models could reasonably capture the low and moderate intensity rainfall variabilities, though the very high intensity scenarios exhibited indifferent results, where the performance of the considered DL frameworks is found to be limited.
英文关键词	Rainfall; ML; DL; LSTM; BiLSTM; GRU; Conv1DLSTM
语种	英语
WOS研究方向	Environmental Sciences & Ecology ; Geology ; Meteorology & Atmospheric Sciences
WOS类目	Environmental Sciences ; Geosciences, Multidisciplinary ; Meteorology & Atmospheric Sciences
WOS记录号	WOS:001205410500001
来源期刊	EARTH SYSTEMS AND ENVIRONMENT
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/305619
作者单位	National Institute of Technology (NIT System); National Institute of Technology Rourkela; Ulster University
推荐引用方式 GB/T 7714	Singh, Sanjeev,Mukherjee, Asmita,Panda, Jagabandhu,et al. Analysis and Forecasting of Temporal Rainfall Variability Over Hundred Indian Cities Using Deep Learning Approaches[J],2024.
APA	Singh, Sanjeev,Mukherjee, Asmita,Panda, Jagabandhu,Choudhury, Animesh,&Bhattacharyya, Saugat.(2024).Analysis and Forecasting of Temporal Rainfall Variability Over Hundred Indian Cities Using Deep Learning Approaches.EARTH SYSTEMS AND ENVIRONMENT.
MLA	Singh, Sanjeev,et al."Analysis and Forecasting of Temporal Rainfall Variability Over Hundred Indian Cities Using Deep Learning Approaches".EARTH SYSTEMS AND ENVIRONMENT (2024).