{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,24]],"date-time":"2026-03-24T15:49:24Z","timestamp":1774367364242,"version":"3.50.1"},"reference-count":47,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2023,4,6]],"date-time":"2023-04-06T00:00:00Z","timestamp":1680739200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61573289"],"award-info":[{"award-number":["61573289"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Long-time-series climate prediction is of great significance for mitigating disasters; promoting ecological civilization; identifying climate change patterns and preventing floods, drought and typhoons. However, the general public often struggles with the complexity and extensive temporal range of meteorological data when attempting to accurately forecast climate extremes. Sequence disorder, weak robustness, low characteristics and weak interpretability are four prevalent shortcomings in predicting long-time-series data. In order to resolve these deficiencies, our study gives a novel hybrid spatiotemporal model which offers comprehensive data preprocessing techniques, focusing on data decomposition, feature extraction and dimensionality upgrading. This model provides a feasible solution to the puzzling problem of long-term climate prediction. Firstly, we put forward a Period Division Region Segmentation Property Extraction (PD-RS-PE) approach, which divides the data into a stationary series (SS) for an Extreme Learning Machine (ELM) prediction and an oscillatory series (OS) for a Long Short-term Memory (LSTM) prediction to accommodate the changing trend of data sequences. Secondly, a new type of input-output mapping mode in a three-dimensional matrix was constructed to enhance the robustness of the prediction. Thirdly, we implemented a multi-layer technique to extract features of high-speed input data based on a Deep Belief Network (DBN) and Particle Swarm Optimization (PSO) for parameter searching of a neural network, thereby enhancing the overall system\u2019s learning ability. Consequently, by integrating all the above innovative technologies, a novel hybrid SS-OS-PSO-DBN-ELM-LSTME (SOPDEL) model with comprehensive data preprocessing was established to improve the quality of long-time-series forecasting. Five models featuring partial enhancements are discussed in this paper and three state-of-the-art classical models were utilized for comparative experiments. The results demonstrated that the majority of evaluation indices exhibit a significant optimization in the proposed model. Additionally, a relevant evaluation system showed that the quality of \u201cExcellent Prediction\u201d and \u201cGood Prediction\u201d exceeds 90%, and no data with \u201cBad Prediction\u201d appear, so the accuracy of the prediction process is obviously insured.<\/jats:p>","DOI":"10.3390\/rs15071951","type":"journal-article","created":{"date-parts":[[2023,4,6]],"date-time":"2023-04-06T08:41:52Z","timestamp":1680770512000},"page":"1951","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":21,"title":["A Novel Hybrid Intelligent SOPDEL Model with Comprehensive Data Preprocessing for Long-Time-Series Climate Prediction"],"prefix":"10.3390","volume":"15","author":[{"given":"Zeyu","family":"Zhou","sequence":"first","affiliation":[{"name":"School of Automation, Northwestern Polytechnical University, Shaanxi 710072, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7381-5046","authenticated-orcid":false,"given":"Wei","family":"Tang","sequence":"additional","affiliation":[{"name":"School of Automation, Northwestern Polytechnical University, Shaanxi 710072, China"}]},{"given":"Mingyang","family":"Li","sequence":"additional","affiliation":[{"name":"School of Power and Energy, Northwestern Polytechnical University, Shaanxi 710072, China"}]},{"given":"Wen","family":"Cao","sequence":"additional","affiliation":[{"name":"School of Automation, Northwestern Polytechnical University, Shaanxi 710072, China"}]},{"given":"Zhijie","family":"Yuan","sequence":"additional","affiliation":[{"name":"School of Automation, Northwestern Polytechnical University, Shaanxi 710072, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,4,6]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1023\/A:1014539414591","article-title":"Estimates of the Damage Costs of Climate Change, Part II. 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