{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,28]],"date-time":"2026-02-28T18:08:55Z","timestamp":1772302135604,"version":"3.50.1"},"reference-count":89,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2022,8,3]],"date-time":"2022-08-03T00:00:00Z","timestamp":1659484800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100007446","name":"King Khalid University","doi-asserted-by":"publisher","award":["RGP.2\/43\/43"],"award-info":[{"award-number":["RGP.2\/43\/43"]}],"id":[{"id":"10.13039\/501100007446","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>Rainfall is a primary factor for agricultural production, especially in a rainfed agricultural region. Its accurate prediction is therefore vital for planning and managing farmers\u2019 plantations. Rainfall plays an important role in the symmetry of the water cycle, and many hydrological models use rainfall as one of their components. This paper aimed to investigate the applicability of six machine learning (ML) techniques (i.e., M5 model tree: (M5), random forest: (RF), support vector regression with polynomial (SVR-poly) and RBF kernels (SVR- RBF), multilayer perceptron (MLP), and long-short-term memory (LSTM) in predicting for multiple-month ahead of monthly rainfall. The experiment was set up for two weather gauged stations located in the Thale Sap Songkhla basin. The model development was carried out by (1) selecting input variables, (2) tuning hyperparameters, (3) investigating the influence of climate variables on monthly rainfall prediction, and (4) predicting monthly rainfall with multi-step-ahead prediction. Four statistical indicators including correlation coefficient (r), mean absolute error (MAE), root mean square error (RMSE), and overall index (OI) were used to assess the model\u2019s effectiveness. The results revealed that large-scale climate variables, particularly sea surface temperature, were significant influence variables for rainfall prediction in the tropical climate region. For projections of the Thale Sap Songkhla basin as a whole, the LSTM model provided the highest performance for both gauged stations. The developed predictive rainfall model for two rain gauged stations provided an acceptable performance: r (0.74), MAE (86.31 mm), RMSE (129.11 mm), and OI (0.70) for 1 month ahead, r (0.72), MAE (91.39 mm), RMSE (133.66 mm), and OI (0.68) for 2 months ahead, and r (0.70), MAE (94.17 mm), RMSE (137.22 mm), and OI (0.66) for 3 months ahead.<\/jats:p>","DOI":"10.3390\/sym14081599","type":"journal-article","created":{"date-parts":[[2022,8,3]],"date-time":"2022-08-03T23:33:01Z","timestamp":1659569581000},"page":"1599","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":34,"title":["Long-Short Term Memory Technique for Monthly Rainfall Prediction in Thale Sap Songkhla River Basin, Thailand"],"prefix":"10.3390","volume":"14","author":[{"given":"Nureehan","family":"Salaeh","sequence":"first","affiliation":[{"name":"Center of Excellence in Sustainable Disaster Management, School of Engineering and Technology, Walailak University, Nakhon Si Thammarat 80161, Thailand"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9847-2177","authenticated-orcid":false,"given":"Pakorn","family":"Ditthakit","sequence":"additional","affiliation":[{"name":"Center of Excellence in Sustainable Disaster Management, School of Engineering and Technology, Walailak University, Nakhon Si Thammarat 80161, Thailand"}]},{"given":"Sirimon","family":"Pinthong","sequence":"additional","affiliation":[{"name":"Center of Excellence in Sustainable Disaster Management, School of Engineering and Technology, Walailak University, Nakhon Si Thammarat 80161, Thailand"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3467-8704","authenticated-orcid":false,"given":"Mohd Abul","family":"Hasan","sequence":"additional","affiliation":[{"name":"Civil Engineering Department, College of Engineering, King Khalid University, Abha 61421, Saudi Arabia"}]},{"given":"Saiful","family":"Islam","sequence":"additional","affiliation":[{"name":"Civil Engineering Department, College of Engineering, King Khalid University, Abha 61421, Saudi Arabia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8427-5965","authenticated-orcid":false,"given":"Babak","family":"Mohammadi","sequence":"additional","affiliation":[{"name":"Department of Physical Geography and Ecosystem Science, Lund University, S\u00f6lvegatan 12, SE-223 62 Lund, Sweden"}]},{"given":"Nguyen Thi Thuy","family":"Linh","sequence":"additional","affiliation":[{"name":"Institute of Applied Technology, Thu Dau Mot University, Thu Dau Mot 75000, Vietnam"}]}],"member":"1968","published-online":{"date-parts":[[2022,8,3]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"867","DOI":"10.2166\/nh.2016.212","article-title":"Incorporating large-scale atmospheric variables in long-term seasonal rainfall forecasting using artificial neural networks: An application to the Ping Basin in Thailand","volume":"48","author":"Babel","year":"2017","journal-title":"Hydrol. 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