{"_buckets": {"deposit": "60352f71-870c-4be4-856f-29ec9cf70292"}, "_deposit": {"created_by": 11, "id": "1189", "owners": [11], "pid": {"revision_id": 0, "type": "depid", "value": "1189"}, "status": "published"}, "_oai": {"id": "oai:repository.naro.go.jp:00001189", "sets": ["44", "360"]}, "author_link": ["1103", "1477", "5225", "936", "1749", "5224"], "item_10002_biblio_info_7": {"attribute_name": "書誌情報", "attribute_value_mlt": [{"bibliographicIssueDates": {"bibliographicIssueDate": "2019-03-30", "bibliographicIssueDateType": "Issued"}, "bibliographicPageEnd": "80", "bibliographicPageStart": "71", "bibliographicVolumeNumber": "3", "bibliographic_titles": [{"bibliographic_title": "農研機構研究報告 農村工学研究部門"}, {"bibliographic_title": "Bulletin of the NARO, Rural Engineering", "bibliographic_titleLang": "en"}]}]}, "item_10002_description_5": {"attribute_name": "抄録", "attribute_value_mlt": [{"subitem_description": "Owing to global climate change, crop plantation-kind changes, and expensive costs on the operation for drainage- facilities in low-land crop fields, the managers for the drainage facilities (e.g., pump stations) have to consider efficient and flexible operations. Real-time numerical predictions on water level and flow at a monitoring point may contribute to the optimized operation of the drainage system for flood controls. Our study performed the development of a real- time prediction system on water level and discharge at a pumping station during heavy rainfall events using an artificial neural network (ANN) model. The system was applied to an actual paddy field, whose area is 179 ha, including a drainage pump that is connected to a river, and a pond that stabilizes water level. The rainfall events were provided by the Japan Meteorological Agency. The ANN model requires numerous data sets, usually observed in the filed, but our observed data were insufficient. Instead, we extended the rainfall data by including the artificial two-year and ten-year probability rainfall events because of the creation of heavy rainfall-event data. We ran a runoff model to generate discharges and water levels based on the artificial rainfall. The input data for the ANN model consisted of rainfall, water levels, and discharges. The output consisted of water levels and discharges. After the ANN-model machine learning, the model provided reasonable predictions of water levels within 10% error against the runoff model results in 30 minutes and two hours using the k-fold cross validation. We performed a new test about shorter machine-learning data, in which the 10-year probability rainfall event was excluded. The ANN-model prediction was approximately 10% reduction at the maximum peak for two hours behind, compared with the original ANN-model prediction.", "subitem_description_type": "Abstract"}, {"subitem_description": "近年の温暖化の影響や農作物の作付け変更への対応，また，排水施設の運転コストを抑制するために，効率的・柔軟    的な排水施設の運用が必要である。とくに洪水時において，モニタリング地点の水位や流れをリアルタイムで予測でき    れば，排水システムの最適な運用が実現できる。本研究の目的は，人工ニューラルネットワーク（ANN）モデルを利用して，豪雨時の排水機場遊水池へ流れ込む流量とその水位をリアルタイムで予測可能なシステムを構築することである。このシステムは，179 haの面積を有し，排水機場，水位調整を行う遊水池を持つ水田地域に適用された。ANN モデルへの入力は，降雨量・水位・ポンプ排水量である。不十分な学習データを補うために，気象庁アメダスからの降雨データを基にして，2年と 10年確率降雨イベントを含む人工降雨データを生成し，水位・流量データは，この降雨データを入力値として計算された排水解析モデルの出力結果を利用した。ANN モデルの出力は水位・流量である。10回の交差検証法を用いて ANN モデルの水位予測の検証を行い，30分と 2時間後の水位予測は 10％以内のエラーが得られた。また，10年確率降雨イベントについて，その学習の有無の比較では，2時間後の最大水位の予測は約  10％の差異が見られた。", "subitem_description_type": "Abstract"}]}, "item_10002_identifier_registration": {"attribute_name": "ID登録", "attribute_value_mlt": [{"subitem_identifier_reg_text": "10.24514/00001157", "subitem_identifier_reg_type": "JaLC"}]}, "item_10002_publisher_8": {"attribute_name": "出版者", "attribute_value_mlt": [{"subitem_publisher": "国立研究開発法人 農業・食品産業技術総合研究機構 "}, {"subitem_publisher": "National Agriculture and Food Research Organization (NARO) "}]}, "item_10002_relation_14": {"attribute_name": "DOI", "attribute_value_mlt": [{"subitem_relation_type": "isIdenticalTo", "subitem_relation_type_id": {"subitem_relation_type_id_text": "10.24514/00001157", "subitem_relation_type_select": "DOI"}}]}, "item_10002_source_id_9": {"attribute_name": "ISSN", "attribute_value_mlt": [{"subitem_source_identifier": "2432-7883", "subitem_source_identifier_type": "ISSN"}]}, "item_10002_version_type_20": {"attribute_name": "著者版フラグ", "attribute_value_mlt": [{"subitem_version_resource": "http://purl.org/coar/version/c_970fb48d4fbd8a85", "subitem_version_type": "VoR"}]}, "item_creator": {"attribute_name": "著者", "attribute_type": "creator", "attribute_value_mlt": [{"creatorNames": [{"creatorName": "木村, 延明"}, {"creatorName": "キムラ, ノブアキ", "creatorNameLang": "ja-Kana"}, {"creatorName": "KIMURA, Nobuaki ", "creatorNameLang": "en"}], "nameIdentifiers": [{"nameIdentifier": "5224", "nameIdentifierScheme": "WEKO"}, {"nameIdentifier": "40706842", "nameIdentifierScheme": "e-Rad", "nameIdentifierURI": "https://kaken.nii.ac.jp/ja/search/?qm=40706842"}, {"nameIdentifier": "nkimura3", "nameIdentifierScheme": "researchmap", "nameIdentifierURI": "http://researchmap.jp/nkimura3"}, {"nameIdentifier": "0000-0001-7399-987X", "nameIdentifierScheme": "ORCID", "nameIdentifierURI": "http://orcid.org/0000-0001-7399-987X"}]}, {"creatorNames": [{"creatorName": "中田, 達"}, {"creatorName": "ナカダ, トオル", "creatorNameLang": "ja-Kana"}, {"creatorName": "NAKADA, Toru", "creatorNameLang": "en"}], "nameIdentifiers": [{"nameIdentifier": "1477", "nameIdentifierScheme": "WEKO"}, {"nameIdentifier": "10584336", "nameIdentifierScheme": "e-Rad", "nameIdentifierURI": "https://kaken.nii.ac.jp/ja/search/?qm=10584336"}, {"nameIdentifier": "nkd1ttt", "nameIdentifierScheme": "researchmap", "nameIdentifierURI": "http://researchmap.jp/nkd1ttt"}]}, {"creatorNames": [{"creatorName": "安瀬地, 一作"}, {"creatorName": "アゼチ, イッサク", "creatorNameLang": "ja-Kana"}, {"creatorName": "AZECHI, Issaku", "creatorNameLang": "en"}], "nameIdentifiers": [{"nameIdentifier": "936", "nameIdentifierScheme": "WEKO"}, {"nameIdentifier": "10720732", "nameIdentifierScheme": "e-Rad", "nameIdentifierURI": "https://kaken.nii.ac.jp/ja/search/?qm=10720732"}, {"nameIdentifier": "paz", "nameIdentifierScheme": "researchmap", "nameIdentifierURI": "http://researchmap.jp/paz"}]}, {"creatorNames": [{"creatorName": "関島, 建志"}, {"creatorName": "セキジマ, ケンジ", "creatorNameLang": "ja-Kana"}, {"creatorName": "SEKIJIMA, Kemji", "creatorNameLang": "en"}], "nameIdentifiers": [{"nameIdentifier": "1749", "nameIdentifierScheme": "WEKO"}, {"nameIdentifier": "10782211", "nameIdentifierScheme": "e-Rad", "nameIdentifierURI": "https://kaken.nii.ac.jp/ja/search/?qm=10782211"}, {"nameIdentifier": "sekijima", "nameIdentifierScheme": "researchmap", "nameIdentifierURI": "http://researchmap.jp/sekijima"}]}, {"creatorNames": [{"creatorName": "桐, 博英"}, {"creatorName": "キリ, ヒロヒデ", "creatorNameLang": "ja-Kana"}, {"creatorName": "KIRI, Hirohide", "creatorNameLang": "en"}], "nameIdentifiers": [{"nameIdentifier": "1103", "nameIdentifierScheme": "WEKO"}, {"nameIdentifier": "60360385", "nameIdentifierScheme": "e-Rad", "nameIdentifierURI": "https://kaken.nii.ac.jp/ja/search/?qm=60360385"}, {"nameIdentifier": "read0004554", "nameIdentifierScheme": "researchmap", "nameIdentifierURI": "http://researchmap.jp/read0004554"}]}, {"creatorNames": [{"creatorName": "馬場, 大地"}, {"creatorName": "ババ, ダイチ", "creatorNameLang": "ja-Kana"}, {"creatorName": "BABA, Daichi", "creatorNameLang": "en"}], "nameIdentifiers": [{"nameIdentifier": "5225", "nameIdentifierScheme": "WEKO"}]}]}, "item_files": {"attribute_name": "ファイル情報", "attribute_type": "file", "attribute_value_mlt": [{"accessrole": "open_date", "date": [{"dateType": "Available", "dateValue": "2019-03-07"}], "displaytype": "detail", "download_preview_message": "", "file_order": 0, "filename": "nire-bulletin003_06.pdf", "filesize": [{"value": "2.3 MB"}], "format": "application/pdf", "future_date_message": "", "is_thumbnail": false, "licensefree": "© 国立研究開発法人 農業・食品産業技術総合研究機構 \nNational Agriculture and Food Research Organization, Japan\n", "licensetype": "license_free", "mimetype": "application/pdf", "size": 2300000.0, "url": {"label": "nire-bulletin003_06.pdf", "url": "https://repository.naro.go.jp/record/1189/files/nire-bulletin003_06.pdf"}, "version_id": "c06f31e2-f673-410d-9ba1-9c206bc8f0a9"}]}, "item_keyword": {"attribute_name": "キーワード", "attribute_value_mlt": [{"subitem_subject": "人工ニューラルネットワークモデル", "subitem_subject_scheme": "Other"}, {"subitem_subject": "排水解析モデル", "subitem_subject_scheme": "Other"}, {"subitem_subject": "排水施設", "subitem_subject_scheme": "Other"}, {"subitem_subject": "低平地", "subitem_subject_scheme": "Other"}, {"subitem_subject": "水位予測", "subitem_subject_scheme": "Other"}, {"subitem_subject": "Artificial neural network model", "subitem_subject_language": "en", "subitem_subject_scheme": "Other"}, {"subitem_subject": "Runoff model", "subitem_subject_language": "en", "subitem_subject_scheme": "Other"}, {"subitem_subject": "Drainage facility", "subitem_subject_language": "en", "subitem_subject_scheme": "Other"}, {"subitem_subject": "Low land", "subitem_subject_language": "en", "subitem_subject_scheme": "Other"}, {"subitem_subject": "Water level prediction", "subitem_subject_language": "en", "subitem_subject_scheme": "Other"}]}, "item_language": {"attribute_name": "言語", "attribute_value_mlt": [{"subitem_language": "jpn"}]}, "item_resource_type": {"attribute_name": "資源タイプ", "attribute_value_mlt": [{"resourcetype": "departmental bulletin paper", "resourceuri": "http://purl.org/coar/resource_type/c_6501"}]}, "item_title": "人工ニューラルネットワークモデルを利用した排水機場遊水池の水位予測に関する研究", "item_titles": {"attribute_name": "タイトル", "attribute_value_mlt": [{"subitem_title": "人工ニューラルネットワークモデルを利用した排水機場遊水池の水位予測に関する研究"}, {"subitem_title": "Prediction on Water Levels in a Wet Pond for a Drainage System Using an Artificial Neural Network Model", "subitem_title_language": "en"}]}, "item_type_id": "10002", "owner": "11", "path": ["44", "360"], "permalink_uri": "https://doi.org/10.24514/00001157", "pubdate": {"attribute_name": "公開日", "attribute_value": "2019-03-07"}, "publish_date": "2019-03-07", "publish_status": "0", "recid": "1189", "relation": {}, "relation_version_is_last": true, "title": ["人工ニューラルネットワークモデルを利用した排水機場遊水池の水位予測に関する研究"], "weko_shared_id": -1}