연구동향 분석

머신러닝 기법을 활용한 논 순용수량 예측

김수진 배승종 장민원 2022년

논문상세정보
인용/피인용
' 머신러닝 기법을 활용한 논 순용수량 예측' 의 주제별 논문영향력
논문영향력 선정 방법
논문영향력 요약
주제
  • Irrigation water requirement
  • artificial neural network
  • machine learning
  • paddy field
  • random forest
동일주제 총논문수 논문피인용 총횟수 주제별 논문영향력의 평균
1,197 0

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' 머신러닝 기법을 활용한 논 순용수량 예측' 의 참고문헌

  • 행정-정책 의사결정에서 머신러닝(machine learning) 방법론 도입의 정책적 함의: 기계의 한계와 증거기반 의사결정(evidence-based decision-making)
    김병조 [2020]
  • 통합물관리 정책실현을 위한 전력산업 벤치마킹 연구
    김동현 [2020]
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    박창근 [2020]
  • 논벼에 대한 Penman-Monteith와 FAO Modified Penman 공식의 작물 계수 산정
    유승환 [2006]
  • 기후변화가 논 필요수량에 미치는 영향
    윤동균 [2011]
  • 고해상도 기후시나리오를 이용한 논용수 수요량 및 단위용수량의 기후변화 영향 분석
    유승환 [2012]
  • Toward digitalization of smart maintenance for water infrastructures
    Park, D. S. [2021]
  • The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation
  • The Difference between water management theory and reality for agricultural water
    Park, T. S. [2022]
  • Temporal and spatial variations of evapotranspiration for spring wheat in the Shiyang river basin in northwest China
    Ling Tong [2007]
  • Support vector regression modelling of an aerobic granular sludge in sequential batch reactor
  • Spatially distributing monthly reference evapotranspiration and pan evaporation considering topographic influences
  • Scikit-learn user guide 1.1.2
  • RunoffCurve Number : Has It Reached Maturity?
  • Review on Methods to Fix Number of Hidden Neurons in Neural Networks
  • Reforming agricultural water policy for integrated water resources management
    Cho, W. J. [2020]
  • Random search for hyperparameter optimization
  • Random forests
  • Random Forests for Regression as a Weighted Sum of ${k}$ -Potential Nearest Neighbors
  • Quantification of water requirement of some major crops under semi-arid climate in Turkey
  • Process Variable Importance Analysis by Use of Random Forests in a Shapley Regression Framework
  • Optimal hyperparameters for random forest to predict leakage current alarm on premises
  • On hyperparameter optimization of machine learning algorithms: Theory and practice
    Li Yang [2020]
  • Modelling the Crop Water Requirement Using FAO-CROPWAT and Assessment of Water Resources for Sustainable Water Resource Management: A Case Study in Palakkad District of Humid Tropical Kerala, India
  • Machine learning predictive models for mineral prospectivity: An evaluation of neural networks, random forest, regression trees and support vector machines
  • Machine Learning-Based Boosted Regression Ensemble Combined with Hyperparameter Tuning for Optimal Adaptive Learning
  • Landslide susceptibility mapping using the slope unit for Southeastern Helong City, Jilin Province, China : A comparison of ANN and SVM
    Yu, C. [2020]
  • Landslide susceptibility mapping based on the germinal center optimization algorithm and support vector classification
    Xia, D. [2022]
  • Land cover mapping with emphasis to burnt area delineation using co-orbital ALI and Landsat TM imagery
  • Introduction to neural networks with java
    Heaton, J. [2008]
  • Improving spatial agreement in machine learning-based landslide susceptibility mapping
  • How many hidden layers and nodes?
  • Hands-on machine learning with Scikitlearn, Keras, and TensorFlow
    Géron, A. [2019]
  • Estimation of regional irrigation water requirements and water balance in Xinjiang, China during 1995–2017
    Yinbo Li [2020]
  • Estimation method and improvement of agricultural water demand
    Lee, K. H [2007]
  • Estimation irrigation water requirements with derived crop coefficients for upland and paddy crops in ChiaNan Irrigation Association, Taiwan
  • Effects on Net Irrigation Water Requirement of Joint Distribution of Precipitation and Reference Evapotranspiration
    Feilong Jie [2022]
  • Climate change impacts on irrigation water requirement, crop water productivity and rice yield in the Songkhram River Basin, Thailand
  • Artificial neural networks performance in WIG20 index options pricing
    Wysocki, M. [2022]
  • Artificial neural networks based optimization techniques : A review
  • An overview of statistical learning theory
  • An introduction to recursive partitioning : Rationale, application, and characteristics of classification and regression trees, bagging, and random forests
    Strobl, C. [2009]
  • An assessment of the effectiveness of a random forest classifier for land-cover classification
  • A comparative study of artificial neural networks in predicting blast-induced air-blast overpressure at Deo Nai open-pit coal mine, Vietnam
  • A Comparative Study of Different Machine Learning Algorithms in Predicting the Content of Ilmenite in Titanium Placer
    Yingli LV [2020]