Articles | Volume 21, issue 6
https://doi.org/10.5194/npg-21-1159-2014
© Author(s) 2014. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/npg-21-1159-2014
© Author(s) 2014. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
| 
01 Dec 2014
Research article |
| 01 Dec 2014
An improved ARIMA model for precipitation simulations
H. R. Wang
College of Water Sciences, Key Laboratory for Water and Sediment Sciences Ministry of Education, Beijing Normal University, 19 Xinjiekouwai Street, Beijing, 100875, China
College of Water Sciences, Key Laboratory for Water and Sediment Sciences Ministry of Education, Beijing Normal University, 19 Xinjiekouwai Street, Beijing, 100875, China
X. Lin
College of Mathematic Sciences, Beijing Normal University, 19 Xinjiekouwai Street, Beijing, 100875, China
J. Kang
College of Mathematic Sciences, Beijing Normal University, 19 Xinjiekouwai Street, Beijing, 100875, China
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37 citations as recorded by crossref.
- Temporal Hierarchical Reconciliation for Consistent Water Resources Forecasting Across Multiple Timescales: An Application to Precipitation Forecasting M. Jahangir & J. Quilty 10.1029/2021WR031862
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- Spatial rain probabilistic prediction performance using costsensitive learning algorithm A. Saputra et al. 10.1051/e3sconf/202346419001
- Multi‐Scale Fuzzy Inference System for Influent Characteristic Prediction of Wastewater Treatment Z. Cheng et al. 10.1002/clen.201700343
- Study of the nonlinear character of ionospheric signals possessing critical frequency (foF2) at Pakistan air space B. Jan et al. 10.1007/s12517-021-06495-8
- Time series prediction of seasonal precipitation in Iran, using data-driven models: a comparison under different climatic conditions P. Aghelpour et al. 10.1007/s12517-021-06910-0
- A framework for climate change assessment in Mediterranean data-sparse watersheds using remote sensing and ARIMA modeling M. Al Sayah et al. 10.1007/s00704-020-03442-7
- Forecasting the near future of rainfall in humid tropics using the high-resolution satellite precipitation data T. Jia Yi et al. 10.1016/j.pce.2021.103069
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- Statistical analysis of precipitation variations and its forecasting in Southeast Asia using remote sensing images A. Syed et al. 10.3389/fenvs.2022.832427
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- Evaluation of the k-nearest neighbor method for forecasting the influent characteristics of wastewater treatment plant M. Kim et al. 10.1007/s11783-015-0825-7
- Forecasting long-term monthly precipitation using SARIMA models P. Kabbilawsh et al. 10.1007/s12040-022-01927-9
- Multifractality and autoregressive processes of dry spell lengths in Europe: an approach to their complexity and predictability X. Lana et al. 10.1007/s00704-015-1638-0
- A mobile simulation and ARIMA modeling for prediction of air radiation dose rates H. Salh et al. 10.1007/s10967-021-07726-8
- Development of Precipitation Forecast Model Based on Artificial Intelligence and Subseasonal Clustering L. Parviz & K. Rasouli 10.1061/(ASCE)HE.1943-5584.0001862
- Performance assessment of rainfall forecasting models for urban Guwahati City using machine learning techniques and singular spectrum analysis S. Priya Ashok & S. Pekkat 10.2166/wcc.2024.465
- Flood risk analysis of reservoirs based on full-series ARIMA model under climate change B. Yan et al. 10.1016/j.jhydrol.2022.127979
- Long-Term Stochastic Modeling of Monthly Streamflow in River Nile S. Abdelaziz et al. 10.3390/su15032170
- Using deep learning for precipitation forecasting based on spatio-temporal information: a case study W. Li et al. 10.1007/s00382-021-05916-4
- Global Warming in Cameron Highlands: Forecasting its Temperature Level via ARIMA vs ARAR N. Zamri & N. Azmi 10.1088/1742-6596/2084/1/012009
- Spatiotemporal simulation of annual precipitation in the Urmia Lake basin H. Faghih & J. Behmanesh 10.1007/s00477-023-02503-3
- Data-driven models of groundwater salinization in coastal plains G. Felisa et al. 10.1016/j.jhydrol.2015.07.045
- Daily rainfall forecasting for one year in a single run using Singular Spectrum Analysis P. Unnikrishnan & V. Jothiprakash 10.1016/j.jhydrol.2018.04.032
- Time series analysis of climate variables using seasonal ARIMA approach T. Dimri et al. 10.1007/s12040-020-01408-x
- Hyperparameter Optimization of a Parallelized LSTM for Time Series Prediction M. Öztürk 10.1142/S2196888823500033
- Modeling and forecasting rainfall patterns of southwest monsoons in North–East India as a SARIMA process K. Narasimha Murthy et al. 10.1007/s00703-017-0504-2
- Vector‐time‐series‐based back propagation neural network modeling of air quality inside a public transportation bus using available software A. Kadiyala & A. Kumar 10.1002/ep.12273
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- A Short‐Term Regional Precipitation Prediction Model Based on Wind‐Improved Spatiotemporal Convolutional Network Y. Qiu et al. 10.1029/2022EA002411
- Time series analysis of aerosol optical depth over New Delhi using Box–Jenkins ARIMA modeling approach K. Taneja et al. 10.1016/j.apr.2016.02.004
- Vector Time Series‐Based Radial Basis Function Neural Network Modeling of Air Quality Inside a Public Transportation Bus Using Available Software A. Kadiyala & A. Kumar 10.1002/ep.12523
- Otopark etütlerinin farklı istatiksel yöntemler ile analizi ve CBS ile modellenmesi: Erzincan ili örneği Y. MAZLUM et al. 10.17714/gumusfenbil.685894
- Application of Time-series Model to Predict Groundwater Quality Parameters for Agriculture: (Plain Mehran Case Study) M. Mirsanjari & F. Mohammadyari 10.1088/1755-1315/127/1/012012
- Time Series Analyses and Forecasting of Surface Urban Heat Island Intensity Using ARIMA Model in Punjab, Pakistan M. Mehmood et al. 10.3390/land12010142
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- Trend analysis and ARIMA modeling for forecasting precipitation pattern in Wadi Shueib catchment area in Jordan O. Al Balasmeh et al. 10.1007/s12517-018-4205-z
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Latest update: 25 Apr 2024
Short summary
This paper presents an improvement on the conventional ARIMA model for precipitation time-series forecast. The precipitation time series of 12 months is first classified into several clusters. The maxima, minima, and truncation means of each cluster are then predicted using the improved ARIMA models, which are further used to predict the monthly precipitation through a set of regression models. A case study demonstrates that the present approach could increase the forecast accuracy by 21%.
This paper presents an improvement on the conventional ARIMA model for precipitation time-series...
