{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T01:03:44Z","timestamp":1760231024893,"version":"build-2065373602"},"reference-count":32,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2022,8,25]],"date-time":"2022-08-25T00:00:00Z","timestamp":1661385600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Guizhou Provincial Science and Technology Department Project","award":["[2021] 4","[2021] General 469"],"award-info":[{"award-number":["[2021] 4","[2021] General 469"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>To address the problem of low prediction accuracy of precipitation time series data, an improved overall mean empirical modal decomposition\u2013prediction\u2013reconstruction model (MDPRM) is constructed in this paper. First, the non-stationary precipitation time series are decomposed into multiple decomposition terms by the improved overall mean empirical modal decomposition (MEEMD). Then, a particle swarm optimization support vector machine (PSO-SVM) and convolutional neural network (CNN) and recurrent neural network (RNN) models are used to make predictions according to the characteristics of different decomposition terms. Finally, the prediction results of each decomposition term are superimposed and reconstructed to form the final prediction results. In addition, the application is carried out with the summer precipitation in the Wujiang River basin of Guizhou Province from 1961 to 2018, using the first 38 years of data to train MDPRM and the last 20 years of data to test MDPRM, and comparing with a feedback neural network (BP), a support vector machine (SVM), a particle swarm optimization support vector machine (PSO-SVM), a convolutional neural network (CNN), and a recurrent neural network (RNN), etc. The results show that the mean relative error (MAPE) of the proposed MDPRM is reduced from 0.31 to 0.09, the root mean square error (RMSE) is reduced from 0.56 to 0.30, and the consistency index (\u03b1) is significantly improved from 0.33 to 0.86, which has a higher prediction accuracy. Finally, the trained MDPRM predicts the average summer precipitation in the Wujiang River basin from 2019 to 2028 to be 466.42 mm, the minimum precipitation in 2020 to be 440.94 mm, and the maximum precipitation in 2024 to be 497.94 mm. Based on the prediction results, the agricultural drought level is evaluated using the Z index, which indicates that the summer is normal in the 10-year period. The study provides technical support for the effective guidance of regional water resources\u2019 allocation and scheduling and drought mitigation.<\/jats:p>","DOI":"10.3390\/s22176415","type":"journal-article","created":{"date-parts":[[2022,8,30]],"date-time":"2022-08-30T01:37:55Z","timestamp":1661823475000},"page":"6415","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["MEEMD Decomposition\u2013Prediction\u2013Reconstruction Model of Precipitation Time Series"],"prefix":"10.3390","volume":"22","author":[{"given":"Yongtao","family":"Wang","sequence":"first","affiliation":[{"name":"State Key Laboratory of Advanced Design and Manufacture for Vehicle Body, Hunan University, Lushan South Road, Yuelu District, Changsha 410082, China"},{"name":"Guizhou Institute of Water Resources Science, Guiyang 550002, China"}]},{"given":"Jian","family":"Liu","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Advanced Design and Manufacture for Vehicle Body, Hunan University, Lushan South Road, Yuelu District, Changsha 410082, China"}]},{"given":"Rong","family":"Li","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Advanced Design and Manufacture for Vehicle Body, Hunan University, Lushan South Road, Yuelu District, Changsha 410082, China"}]},{"given":"Xinyu","family":"Suo","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Advanced Design and Manufacture for Vehicle Body, Hunan University, Lushan South Road, Yuelu District, Changsha 410082, China"}]},{"given":"Enhui","family":"Lu","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering, Yangzhou University, Yangzhou 225012, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,8,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"865","DOI":"10.2166\/wcc.2019.271","article-title":"Application of MEEMD-ARIMA combining model for annual runoff prediction in the Lower Yellow River","volume":"11","author":"Zhang","year":"2020","journal-title":"J. Water Clim. Chang."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"2404","DOI":"10.1109\/TSTE.2021.3096554","article-title":"A combined method of improved grey BP neural network and MEEMD-ARIMA for day-ahead wave energy forecast","volume":"12","author":"Wu","year":"2021","journal-title":"IEEE Trans. Sustain. Energy"},{"key":"ref_3","first-page":"1","article-title":"The prediction of gold futures prices at the Shanghai futures exchange based on the MEEMD-CS-Elman model","volume":"11","author":"Wang","year":"2021","journal-title":"SAGE Open"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Song, L., Xie, Q., He, Y., and Dang, P. (2020, January 12\u201314). Ultra-short-term wind power combination forecasting model based on MEEMD-SAE-Elman. Proceedings of the 2020 IEEE 4th Information Technology, Networking, Electronic and Automation Control Conference (ITNEC), Chongqing, China.","DOI":"10.1109\/ITNEC48623.2020.9084768"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Xie, S., Liang, Y., Zheng, Z., and Liu, H. (2017). Combined forecasting method of landslide deformation based on MEEMD, approximate entropy, and WLS-SVM. ISPRS Int. J. Geo-Inf., 6.","DOI":"10.3390\/ijgi6010005"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"102884","DOI":"10.1016\/j.resourpol.2022.102884","article-title":"A novel hybrid model integrating modified ensemble empirical mode decomposition and LSTM neural network for multi-step precious metal prices prediction","volume":"78","author":"Lin","year":"2022","journal-title":"Resour. Policy"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"137117","DOI":"10.1016\/j.scitotenv.2020.137117","article-title":"Carbon price forecasting based on modified ensemble empirical mode decomposition and long short-term memory optimized by improved whale optimization algorithm","volume":"716","author":"Yang","year":"2020","journal-title":"Sci. Total Environ."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Yang, M., Zhu, S., and Li, W. (2022, July 18). Carbon Price Prediction Based on Multi-Factor MEEMD-LSTM Model. Available online: https:\/\/ssrn.com\/abstract=4011664.","DOI":"10.2139\/ssrn.4011664"},{"key":"ref_9","unstructured":"Yu, D., Cheng, J., and Yang, Y. (2007). Hilbert-Huang Transform Method for Mechanical Fault Diagnosis, Science Press."},{"key":"ref_10","first-page":"22","article-title":"Modified EEMD algorithm and its applications","volume":"32","author":"Zheng","year":"2013","journal-title":"J. Vib. Shock"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1321","DOI":"10.1007\/s11269-021-02786-7","article-title":"A Hybrid VMD-SVM Model for Practical Streamflow Prediction Using an Innovative Input Selection Framework","volume":"35","author":"Erhao","year":"2021","journal-title":"Water Resour. Manag."},{"key":"ref_12","first-page":"70","article-title":"Super-short-Time Wind Power Forecasting Based on EEMD-IGSA-LSSVM","volume":"43","author":"Jiang","year":"2016","journal-title":"J. Hunan Univ."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"2311","DOI":"10.1007\/s11269-016-1288-8","article-title":"Monthly Rainfall Forecasting Using EEMD-SVR Based on Phase-Space Reconstruction","volume":"30","author":"Qi","year":"2016","journal-title":"Water Resour. Manag."},{"key":"ref_14","first-page":"275","article-title":"Medium and long-term precipitation prediction based on hybrid model","volume":"45","author":"Li","year":"2018","journal-title":"Comput. Sci."},{"key":"ref_15","first-page":"10","article-title":"Monthly rainfall prediction using wavelet neural network analysis","volume":"27","author":"Ramana","year":"2013","journal-title":"Water Resour. Manag."},{"key":"ref_16","first-page":"700","article-title":"Water quality evaluation model based on artificial swarm algorithm and BP neural network","volume":"6","author":"Su","year":"2012","journal-title":"Chin. J. Environ. Eng."},{"key":"ref_17","first-page":"1473","article-title":"Medium and Long-term Precipitation Prediction Using Wavelet Decomposition-prediction-reconstruction Model","volume":"37","author":"Wang","year":"2022","journal-title":"Water Resour. Manag."},{"key":"ref_18","first-page":"63","article-title":"Prediction of photovoltaic cell output power based on improved particle swarm optimization support vector machine","volume":"3","author":"Wang","year":"2019","journal-title":"Electr. Autom."},{"key":"ref_19","first-page":"77","article-title":"Medium and long-term load forecast based on PSO-RSVR","volume":"37","author":"Zhang","year":"2009","journal-title":"Power Syst. Prot. Control"},{"key":"ref_20","first-page":"104","article-title":"Pollen concentration prediction model based on particle swarm optimization and support vector machine","volume":"39","author":"Zhao","year":"2019","journal-title":"Comput. Appl."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Jiang, L., Tao, Z., Zhu, J., Zhang, J., and Chen, H. (2022). Exploiting PSO-SVM and sample entropy in BEMD for the prediction of interval-valued time series and its application to daily PM2.5 concentration forecasting. Appl. Intell., 1\u20135.","DOI":"10.1007\/s10489-022-03835-3"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Yuming, H., Jiaohong, L., Zhenguo, M., Bing, T., Keqi, Z., and Jianyong, Z. (2022). On Combined PSO-SVM Models in Fault Prediction of Relay Protection Equipment. Circuits Syst. Signal Process., 1\u201317.","DOI":"10.1007\/s00034-022-02056-w"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Yun, F., Dong, H., Liang, C., Weimin, T., and Chao, T. (2022). Feature Selection of XLPE Cable Condition Diagnosis Based on PSO-SVM. Arab. J. Sci. Eng., 1\u201311.","DOI":"10.1007\/s13369-022-07175-9"},{"key":"ref_24","first-page":"23","article-title":"Combined Hydrological Time Series Forecasting Model Based on CNN and MC","volume":"11","author":"Xu","year":"2019","journal-title":"Comput. Mod."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"124889","DOI":"10.1016\/j.energy.2022.124889","article-title":"Forecasting monthly gas field production based on the CNN-LSTM model","volume":"260","author":"Zha","year":"2022","journal-title":"Energy"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"107908","DOI":"10.1016\/j.epsr.2022.107908","article-title":"CNN-LSTM: An efficient hybrid deep learning architecture for predicting short-term photovoltaic power production","volume":"208","author":"Agga","year":"2022","journal-title":"Electr. Power Syst. Res."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Liu, C., Zhu, H., Tang, D., Nie, Q., Li, S., Zhang, Y., and Liu, X. (2022). A transfer learning CNN-LSTM network-based production progress prediction approach in IIoT-enabled manufacturing. Int. J. Prod. Res., 1\u201324.","DOI":"10.1080\/00207543.2022.2056860"},{"key":"ref_28","unstructured":"Jiang, A., Qin, Z., Faulder, D., Cladouhos, T.T., and Jafarpour, B. (2022, January 7\u20139). A Multiscale Recurrent Neural Network Model for Long-Term Prediction of Geothermal Energy Production. Proceedings of the 47th Workshop on Geothermal Reservoir Engineering, Stanford, CA, USA."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Kim, Y.D., and Durlofsky, L. (2022, January 5\u20137). Closed-loop Reservoir Management using a Convolutional\u2013Recurrent Neural Network Proxy for Robust Production Optimization. Proceedings of the ECMOR 2022, The Hague, The Netherlands.","DOI":"10.3997\/2214-4609.202244035"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"6173","DOI":"10.1021\/acs.iecr.2c00757","article-title":"Practicality of Green H2 Economy for Industry and Maritime Sector Decarbonization through Multiobjective Optimization and RNN-LSTM Model Analysis","volume":"61","author":"Kazi","year":"2022","journal-title":"Ind. Eng. Chem. Res."},{"key":"ref_31","first-page":"80","article-title":"Water and sand simulation of yanhe river basin based on particle swarm optimization support vector machine","volume":"23","author":"Li","year":"2015","journal-title":"J. Bas. Sci. Eng."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1473","DOI":"10.1016\/j.aej.2020.04.035","article-title":"Precipitation forecast of the Wujiang River Basin based on artificial bee colony algorithm and backpropagation neural network","volume":"59","author":"Wang","year":"2020","journal-title":"Alex. Eng. J."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/17\/6415\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T00:15:21Z","timestamp":1760141721000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/17\/6415"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,8,25]]},"references-count":32,"journal-issue":{"issue":"17","published-online":{"date-parts":[[2022,9]]}},"alternative-id":["s22176415"],"URL":"https:\/\/doi.org\/10.3390\/s22176415","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2022,8,25]]}}}