Forecasting monthly rainfall of Sub-Himalayan region of India using parametric and non-parametric modelling approaches

被引：14

作者：

Lama, Achal ^{[1
]}

Singh, K. N. ^{[1
]}

Singh, Herojit ^{[2
]}

Shekhawat, Ravindra ^{[1
]}

Mishra, Pradeep ^{[3
]}

Gurung, Bishal ^{[1
]}

机构：

[1] ICAR Indian Agr Stat Res Inst, New Delhi, India

[2] Bidhan Chandra Krishi Viswavidyalaya, Mohanpur, W Bengal, India

[3] JNKVV, Coll Agr, Powarkheda, MP, India

来源：

MODELING EARTH SYSTEMS AND ENVIRONMENT | 2022年 / 8卷 / 01期

关键词：

Rainfall; Non-parametric; SARIMA; EXPAR; TDNN; ARTIFICIAL NEURAL-NETWORKS; TIME-SERIES MODEL;

D O I：

10.1007/s40808-021-01124-5

中图分类号：

X [环境科学、安全科学];

学科分类号：

08 ; 0830 ;

摘要：

Rainfall being a complex phenomenon governed by various meteorological parameters is difficult to model and forecast with high precision. For hilly regions such as state of Sikkim and adjoining areas of West Bengal, rainfall acts as lifeline. Several parametric models such as seasonal autoregressive integrated moving average (SARIMA) and exponential autoregressive (EXPAR) are very popular and extensively used to model and forecast rainfall. Owning to complex nature of rainfall series, non-parametric time delay neural network (TDNN) model has also gained substantial amount of attention by researchers. This study uses these two broad class of models and applies them to the monthly rainfall of Sub-Himalayan West Bengal and Sikkim. The models were compared based on their forecasting efficiencies and pattern prediction ability.

引用

页码：837 / 845

页数：9

共 13 条

[1] Forecasting monthly rainfall of Sub-Himalayan region of India using parametric and non-parametric modelling approaches
Achal Lama
K. N. Singh
Herojit Singh
Ravindra Shekhawat
Pradeep Mishra
Bishal Gurung
Modeling Earth Systems and Environment, 2022, 8 : 837 - 845
[2] Rainfall Forecasting Using Sub sampling Non-parametric Methods
Pucheta, J.
Rodriguez Rivero, C.
Herrera, M.
Salas, C.
Sauchelli, V.
IEEE LATIN AMERICA TRANSACTIONS, 2013, 11 (01) : 646 - 650
[3] Variability and trends of rainfall using non-parametric approaches: A case study of semi-arid area
Das, Jayanta
Mandal, Tapash
Saha, Piu
Bhattacharya, Sudip Kumar
MAUSAM, 2020, 71 (01): : 33 - 44
[4] Detection of spatiotemporal patterns of rainfall trends, using non-parametric statistical techniques, in Karnataka state, India
Harishnaika N
Shilpa N
S A Ahmed
Environmental Monitoring and Assessment, 2023, 195
[5] Detection of spatiotemporal patterns of rainfall trends, using non-parametric statistical techniques, in Karnataka state, India
Harishnaika, N.
Shilpa, N.
Ahmed, S. A.
ENVIRONMENTAL MONITORING AND ASSESSMENT, 2023, 195 (07)
[6] An assessment of contagion risks in the banking system using non-parametric and Copula approaches
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Nasir, Muhammad Ali
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Duy Duong
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[7] Analyzing and forecasting climate variability in Nainital district, India using non-parametric methods and ensemble machine learning algorithms
Sharma, Yatendra
Sajjad, Haroon
Saha, Tamal Kanti
Bhuyan, Nirsobha
Sharma, Aastha
Ahmed, Raihan
THEORETICAL AND APPLIED CLIMATOLOGY, 2024, 155 (6) : 4749 - 4765
[8] Short-term forecasting of German generation-based CO2 emission factors using parametric and non-parametric time series models
Ostermann A.
Bajrami A.
Bogensperger A.
Energy Informatics, 2024, 7 (01)
[9] A combined influence of surface temperature and daily rainfall to the historical landslides occurred on 7th September 2007 over sub-Himalayan region, India
Somnath Karmakar
Prashant N Tiwari
Tamal Basak
Journal of Earth System Science, 132
[10] A combined influence of surface temperature and daily rainfall to the historical landslides occurred on 7th September 2007 over sub-Himalayan region, India
Karmakar, Somnath
Tiwari, Prashant N.
Basak, Tamal
JOURNAL OF EARTH SYSTEM SCIENCE, 2023, 132 (02)

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