{
    "article_id": 1464,
    "public_id": "c7b04ec5-0c2d-414f-ab65-f8bd72274e8c",
    "internal_identifier": null,
    "source_identifier": "90:13",
    "status": "published",
    "type": "research_article",
    "title": "Yeraltı Su Seviyesinin Doğrusal Olmayan Otoregresif Sinir Ağı ile Tahmini",
    "title_secondary": null,
    "abstract": "Yeraltı su seviyelerinin izlenmesi ve tahmini, su kaynakları yönetimi için kritik bir öneme sahiptir. İklim değişikliği, nüfus artışı ve aşırı su çıkarma gibi etkenler, yeraltı suyu rezervlerine baskı oluşturabilir. Bu nedenle, yeraltı su seviyelerinin izlenmesi, akiferlerin sağlığının değerlendirilmesine, sorunların tespit edilmesine ve su yönetimi için bilinçli kararlar alınmasına yardımcı olacaktır. Yeraltı su seviyelerinin izlenmesi yeraltı su rezervlerinin kapasitesi, hassas tarım, yağmur suyu yönetimi ve karstik ortamlar gibi özel durumlar için önemlidir. Yapay sinir ağları (YSA) yeraltı suyu seviyesi tahmininde büyük ilgi görmüştür. Bu modeller, geçmiş verileri ve ilgili parametreleri kullanarak gelecekteki su seviyelerini tahmin etmek için kullanılmıştır ve yüksek başarı sağlamaktadır. Bu çalışmada, doğrusal olmayan otoregresif sinir ağı (NAR-NN) yaklaşımı ile AquiferAuser veri setindeki LT2 kuyusu için yeraltı su seviyesinin tahmini gerçekleştirilmiştir. ",
    "language": "tr",
    "english_title": "Prediction of Groundwater Level Using Nonlinear Autoregressive Neural Network",
    "english_abstract": "Monitoring and predicting groundwater levels is critical for water resource management. Factors such as climate change, population growth, and excessive water extraction can put pressure on groundwater reserves. Therefore, monitoring groundwater levels helps assess the health of aquifers, identify problems, and make informed decisions for water management. Groundwater level monitoring is important for the capacity of groundwater reserves, precision agriculture, rainwater management, and specific situations such as karst environments. Artificial neural networks (ANNs) have gained significant interest in groundwater level prediction. These models have been used to predict future water levels using historical data and relevant parameters, achieving high accuracy. In this study, the prediction of the groundwater level for the LT2 well in the AquiferAuser dataset was performed using a nonlinear autoregressive neural network (NAR-NN) approach.",
    "doi": "10.36287/setsci.6.1.021",
    "doi_url": "https://doi.org/10.36287/setsci.6.1.021",
    "resource_url": "https://www.set-science.com/a/c7b04ec5-0c2d-414f-ab65-f8bd72274e8c",
    "pdf_url": "https://www.set-science.com/a/c7b04ec5-0c2d-414f-ab65-f8bd72274e8c/pdf",
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    "authors": [
        {
            "public_id": "76737e45-c03f-4131-9ec8-2ce06e029a26",
            "given": "Ramazan",
            "family": "Şentürk",
            "full": "Ramazan Şentürk",
            "literal": null,
            "orcid": null,
            "orcid_url": null,
            "email": "ramazan.senturk@envest.com.tr",
            "corresponding": true,
            "sequence": "first",
            "affiliations": [
                {
                    "name": "Kayseri Üniversitesi",
                    "department": null,
                    "country": null,
                    "country_code": null,
                    "ror": null,
                    "ror_url": null
                }
            ]
        },
        {
            "public_id": "50ae9550-6b19-448d-928e-bb65e74f3efd",
            "given": "Rıfat",
            "family": "Kurban",
            "full": "Rıfat Kurban",
            "literal": null,
            "orcid": null,
            "orcid_url": null,
            "email": "rifat.kurban@agu.edu.tr",
            "corresponding": false,
            "sequence": "additional",
            "affiliations": [
                {
                    "name": "Abdullah Gül Üniversitesi",
                    "department": null,
                    "country": null,
                    "country_code": null,
                    "ror": null,
                    "ror_url": null
                }
            ]
        },
        {
            "public_id": "7a8ecf6d-4b9b-48ce-8847-7b36e0d7a681",
            "given": "Fatma",
            "family": "Çakıroğlu",
            "full": "Fatma Çakıroğlu",
            "literal": null,
            "orcid": null,
            "orcid_url": null,
            "email": "citil.fatmaa@gmail.com",
            "corresponding": false,
            "sequence": "additional",
            "affiliations": [
                {
                    "name": "Kayseri Üniversitesi",
                    "department": null,
                    "country": null,
                    "country_code": null,
                    "ror": null,
                    "ror_url": null
                }
            ]
        }
    ],
    "keywords": [
        "yeraltı su seviyesi tahmini",
        "yapay sinir ağı",
        "doğrusal olmayan otoregresif sinir ağı"
    ],
    "translations": [
        {
            "language": "en",
            "title": "Prediction of Groundwater Level Using Nonlinear Autoregressive Neural Network",
            "abstract": "Monitoring and predicting groundwater levels is critical for water resource management. Factors such as climate change, population growth, and excessive water extraction can put pressure on groundwater reserves. Therefore, monitoring groundwater levels helps assess the health of aquifers, identify problems, and make informed decisions for water management. Groundwater level monitoring is important for the capacity of groundwater reserves, precision agriculture, rainwater management, and specific situations such as karst environments. Artificial neural networks (ANNs) have gained significant interest in groundwater level prediction. These models have been used to predict future water levels using historical data and relevant parameters, achieving high accuracy. In this study, the prediction of the groundwater level for the LT2 well in the AquiferAuser dataset was performed using a nonlinear autoregressive neural network (NAR-NN) approach.",
            "keywords": [
                "groundwater level estimation",
                "artificial neural network",
                "nonlinear autoregressive neural network"
            ]
        }
    ],
    "subjects": [],
    "subject_names": [],
    "published_date": "2023-12-29",
    "published_year": "2023",
    "received_date": null,
    "accepted_date": null,
    "volume": "15",
    "issue": null,
    "page_first": "59",
    "page_last": "63",
    "article_number": null,
    "conference": {
        "short_name": "BMYZ2023",
        "full_title": "Cognitive Models and Artificial Intelligence Conference",
        "name": "Cognitive Models and Artificial Intelligence Conference",
        "acronym": "BMYZ2023",
        "location": "Ankara, Türkiye",
        "start_date": "2023-10-26",
        "end_date": null,
        "public_id": "dc3a47c3-f4b3-4059-b610-f99aca33d7a0"
    },
    "proceedings": {
        "title": "Cognitive Models and Artificial Intelligence Conference",
        "series_title": "SETSCI Conference Proceedings",
        "publisher": "SET Technology",
        "isbn": "978-605-69925-7-5",
        "eisbn": null,
        "issn": null,
        "eissn": "2687-5527",
        "doi": null,
        "doi_prefix": "10.36287",
        "slug": "setsci-volume-15-2023-legacy-90",
        "public_id": "33fc699d-0ab3-4057-b2e0-644dc89f900d",
        "url": "https://www.set-science.com/p/setsci-volume-15-2023-legacy-90"
    },
    "license": {
        "name": "CC BY 4.0",
        "url": "https://creativecommons.org/licenses/by/4.0/",
        "code": "CC-BY-4.0",
        "open_access": true
    },
    "copyright": {
        "holder": "SET Technology",
        "year": "2023"
    },
    "funding_statement": null,
    "funders": [],
    "references": [
        {
            "order": 1,
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            "doi": null,
            "title": null,
            "authors": null,
            "source": null,
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            "pages": null
        }
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    "history": {
        "published": "2023-12-29"
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}