{"title":"Qanat discharge prediction using a comparative analysis of machine learning methods","authors":"Saeideh Samani, Meysam Vadiati, Ozgur Kisi, Leyla Ghasemi, Reza Farajzadeh","doi":"10.1007/s12145-024-01409-0","DOIUrl":null,"url":null,"abstract":"<p>The Qanat (also known as kariz) is one of the significant water resources in many arid and semiarid regions. The present research aims to use machine learning techniques for Qanat discharge (QD) prediction and find a practical model that predicts QD well. Gene expression programming (GEP), artificial neural network (ANN), group method of data handling (GMDH), least-square support vector machine (LSSVM) and adaptive neuro-fuzzy inference system (ANFIS), are employed to predict one-, two-, and five-months time-step ahead QD in an unconfined aquifer. QD for one, two, and three lag-times (QD<sub>t−1</sub>, QD<sub>t−2</sub>, QD<sub>t−3</sub>), QD for adjacent Qanat, the main meteorological components (T<sub>t</sub>, ET<sub>t</sub>, P<sub>t</sub>) and GWL for one, two, and three lag-times are utilized as input dataset to accomplish accurate QD prediction. The GMDH model, according to its best results, had promising accuracy in predicting multi-step ahead monthly QD, followed by the LSSVM, ANFIS, ANN and GEP, respectively.</p>","PeriodicalId":49318,"journal":{"name":"Earth Science Informatics","volume":"39 1","pages":""},"PeriodicalIF":2.7000,"publicationDate":"2024-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Earth Science Informatics","FirstCategoryId":"89","ListUrlMain":"https://doi.org/10.1007/s12145-024-01409-0","RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
引用次数: 0
Abstract
The Qanat (also known as kariz) is one of the significant water resources in many arid and semiarid regions. The present research aims to use machine learning techniques for Qanat discharge (QD) prediction and find a practical model that predicts QD well. Gene expression programming (GEP), artificial neural network (ANN), group method of data handling (GMDH), least-square support vector machine (LSSVM) and adaptive neuro-fuzzy inference system (ANFIS), are employed to predict one-, two-, and five-months time-step ahead QD in an unconfined aquifer. QD for one, two, and three lag-times (QDt−1, QDt−2, QDt−3), QD for adjacent Qanat, the main meteorological components (Tt, ETt, Pt) and GWL for one, two, and three lag-times are utilized as input dataset to accomplish accurate QD prediction. The GMDH model, according to its best results, had promising accuracy in predicting multi-step ahead monthly QD, followed by the LSSVM, ANFIS, ANN and GEP, respectively.
期刊介绍：
The Earth Science Informatics [ESIN] journal aims at rapid publication of high-quality, current, cutting-edge, and provocative scientific work in the area of Earth Science Informatics as it relates to Earth systems science and space science. This includes articles on the application of formal and computational methods, computational Earth science, spatial and temporal analyses, and all aspects of computer applications to the acquisition, storage, processing, interchange, and visualization of data and information about the materials, properties, processes, features, and phenomena that occur at all scales and locations in the Earth system’s five components (atmosphere, hydrosphere, geosphere, biosphere, cryosphere) and in space (see "About this journal" for more detail). The quarterly journal publishes research, methodology, and software articles, as well as editorials, comments, and book and software reviews. Review articles of relevant findings, topics, and methodologies are also considered.
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