[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121831-en":3,"doc-seo-121831-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},121831,1649267921044,"Ava Thompson","https://us-avatar.wpscdn.com/avatar/1800007509477c92dfb?_k=1782875107921204101",8,"Research & Report","Predicting groundwater level using traditional and deep machine learning algorithms","Groundwater level prediction supports sustainable water management in arid and semi-arid regions where overexploitation damages groundwater resources. This study evaluates traditional and deep machine learning models for Izeh City, Iran, using three inputs: groundwater extraction rate, rainfall rate, and river flow rate (3 km distance). Convolutional neural network performance is superior, robust to noise and variability, scalable, and yields the highest accuracy (RMSE 0.0558, R2 0.9948). Correlation analyses identify river flow and extraction as key influencing variables.","TYPE Original Research PUBLISHED 16 February 2024 DOI 10.3389/fenvs.2024.1291327  \nOPEN ACCESS  \nEDITED BY  \nSushant K. Singh,  \nCAIES Foundation, India  \nREVIEWED BY  \nSandeep Samantaray,  \nNational Institute of Technology Srinagar, India Nasrin Fathollahzaddeh Attar,  \nUniversity of Tabriz, Iran  \n*CORRESPONDENCE  \nFan Feng,  \n [fanfeng2023@163.com](fanfeng2023@163.com)[ ](fanfeng2023@163.com)Hamzeh Ghorbani,  \n [hamzehghorbani68@yahoo.com](hamzehghorbani68@yahoo.com)  \nRECEIVED 09 September 2023  \nACCEPTED 05 February 2024  \nPUBLISHED 16 February 2024  \nCITATION  \nFeng F, Ghorbani H and Radwan AE (2024), Predicting groundwater level using traditional and deep machine learning algorithms.  \nFront. Environ. Sci. 12:1291327 .  \ndoi: 10.3389/fenvs.2024.1291327  \nCOPYRIGHT  \n© 2024 Feng, Ghorbani and Radwan. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.  \nPredicting groundwater level using traditional and deep machine learning algorithms  \nFan Feng 1*, Hamzeh Ghorbani 2* and Ahmed E. Radwan 3  \n1University of Applied Sciences for Engineering and Economics, Berlin, Germany, 2Young Researchers and Elite Club, Ahvaz Branch, Islamic Azad University, Ahvaz, Iran, 3Faculty of Geography and Geology, Institute of Geological Sciences, Jagiellonian University, Kraków, Poland  \nThis research aims to evaluate various traditional or deep machine learning algorithms for the prediction of groundwater level (GWL) using three key input variables specific to Izeh City in the Khuzestan province of Iran: groundwater extraction rate (E), rainfall rate (R), and river flow rate (P) (with 3 km distance) . Various traditional and deep machine learning (DML) algorithms, including convolutional neural network (CNN), recurrent neural network (RNN), support vector machine (SVM), decision tree (DT), random forest (RF), and generative adversarial network (GAN), were evaluated. The convolutional neural network (CNN) algorithm demonstrated superior performance among all the algorithms evaluated in this study. The CNN model exhibited robustness against noise and variability, scalability for handling large datasets with multiple input variables, and parallelization capabilities for fast processing. Moreover, it autonomously learned and identified data patterns, resulting in fewer outlier predictions. The CNN model achieved the highest accuracy in GWL prediction, with an RMSE of 0. 0558 and an R2 of 0 .9948. It also showed no outlier data predictions, indicating its reliability. Spearman and Pearson correlation analyses revealed that P and E were the dataset ’s most influential variables on GWL. This research has significant implications for water resource management in Izeh City and the Khuzestan province of Iran, aiding in conservation efforts and increasing local crop productivity. The approach can also be applied to predicting GWL in various global regions facing water scarcity due to population growth. Future researchers are encouraged to consider these factors for more accurate GWL predictions. Additionally, the CNN algorithm ’s performance can be further enhanced by incorporating additional input variables.  \nKEYWORDS  \ngroundwater level, deep machine learning, CNN algorithm, prediction, water management  \n1 Introduction  \nThe groundwater level (GWL) is of critical importance, especially in arid and semi-arid countries (Alfarrah and Walraevens, 2018; Bovolo et al., 2009; Priyan, 2021). In many areas, the overexploitation of GWL has led to irreparable damage to the groundwater sources (Alfarrah and Walraevens, 2018; Bovolo et al., 2009; Priyan, 2021). Predicting GWL ","cbCaiaaFskJOvV3h","https://ap.wps.com/l/cbCaiaaFskJOvV3h","pdf",3180995,1,16,"English","en",105,"# Introduction\n## Problem statement\n## Methods and algorithms\n## Results and performance metrics\n## Variable importance and implications\n## Conclusions and future work","[{\"question\":\"Which input variables are used to predict groundwater level in Izeh City?\",\"answer\":\"The study uses groundwater extraction rate (E), rainfall rate (R), and river flow rate (P) measured at a 3 km distance.\"},{\"question\":\"How did the CNN model perform compared with other algorithms?\",\"answer\":\"The CNN achieved the best accuracy, with RMSE of 0.0558 and R2 of 0.9948, and showed no outlier predictions.\"},{\"question\":\"Which variables were found to most influence groundwater level?\",\"answer\":\"Spearman and Pearson correlation analyses indicate that river flow (P) and extraction rate (E) are the most influential variables for groundwater level.\"}]","Predicting groundwater level using traditional and deep machine learning algorithms | PDF",1785807107,40,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"predicting-groundwater-level-using-traditional-and-deep-machine-learning-algorithms","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/predicting-groundwater-level-using-traditional-and-deep-machine-learning-algorithms/121831/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Which input variables are used to predict groundwater level in Izeh City?","Question",{"text":75,"@type":76},"The study uses groundwater extraction rate (E), rainfall rate (R), and river flow rate (P) measured at a 3 km distance.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How did the CNN model perform compared with other algorithms?",{"text":80,"@type":76},"The CNN achieved the best accuracy, with RMSE of 0.0558 and R2 of 0.9948, and showed no outlier predictions.",{"name":82,"@type":73,"acceptedAnswer":83},"Which variables were found to most influence groundwater level?",{"text":84,"@type":76},"Spearman and Pearson correlation analyses indicate that river flow (P) and extraction rate (E) are the most influential variables for groundwater level.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,119,122,127,130,134],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":29,"slug":118},7,"Healthcare","healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":120,"slug":121},30,"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]