Deep learning versus gradient boosting machine for pan evaporation prediction

Malik, Anurag; Saggi, Mandeep Kaur; Rehman, Sufia; Sahana, Mehebub; İnyurt, Samed; Bhatia, Amandeep Singh; Farooque, Aitazaz A.; Oudah, Atheer Y.; Yaseen‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬, Zaher Mundher

doi:10.1080/19942060.2022.2027273

Cited by 54 publications

(13 citation statements)

References 76 publications

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“…“Boosting” refers to an iterative process that integrates multiple individual learners to form a series of weak learners into strong learners, thereby reducing model generalization errors and improving model prediction accuracy. It can be used for mathematical problems such as classification and regression [ 160 ]. At the same time, gradient boosting is mostly constructed by decision trees, also known as gradient-boosting decision trees, which have good fitting ability for linear and nonlinear data, can handle continuous and discrete data, and have high prediction accuracy and strong generalization ability.…”

Section: Machine-learning Algorithmsmentioning

confidence: 99%

Collision Cross Section Prediction Based on Machine Learning

Wang

Jiang

et al. 2023

Molecules

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Ion mobility-mass spectrometry (IM-MS) is a powerful separation technique providing an additional dimension of separation to support the enhanced separation and characterization of complex components from the tissue metabolome and medicinal herbs. The integration of machine learning (ML) with IM-MS can overcome the barrier to the lack of reference standards, promoting the creation of a large number of proprietary collision cross section (CCS) databases, which help to achieve the rapid, comprehensive, and accurate characterization of the contained chemical components. In this review, advances in CCS prediction using ML in the past 2 decades are summarized. The advantages of ion mobility-mass spectrometers and the commercially available ion mobility technologies with different principles (e.g., time dispersive, confinement and selective release, and space dispersive) are introduced and compared. The general procedures involved in CCS prediction based on ML (acquisition and optimization of the independent and dependent variables, model construction and evaluation, etc.) are highlighted. In addition, quantum chemistry, molecular dynamics, and CCS theoretical calculations are also described. Finally, the applications of CCS prediction in metabolomics, natural products, foods, and the other research fields are reflected.

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Section: Machine-learning Algorithmsmentioning

confidence: 99%

Collision Cross Section Prediction Based on Machine Learning

Wang

Jiang

et al. 2023

Molecules

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“…‖𝑡 𝜙𝑤‖ (10) where t=(t1, …., tN),w=(w0,…, wN) and 𝛷 is the N*(N+1) "design" matrix with Φ 𝐾 𝑥 , 𝑥 and Φ 1. Overfitting frequently occurs when w and 𝜎 in Equation ( 6) are estimated with maximum probability.…”

Section: Relevance Vector Machine (Rvm)mentioning

confidence: 99%

“…Additionally, it has much application value in agriculture water needs management, monitoring and effective water resource utilization, and drought forecasting, among others [9,10]. Potential evapotranspiration is used for calculating the drought index (standardized precipitation evapotranspiration index), which is essential in drought assessment.…”

Section: Introductionmentioning

confidence: 99%

Modeling Potential Evapotranspiration by Improved Machine Learning Methods Using Limited Climatic Data

Mostafa

Kisi

Adnan

et al. 2023

Water

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Modeling potential evapotranspiration (ET0) is an important issue for water resources planning and management projects involving droughts and flood hazards. Evapotranspiration, one of the main components of the hydrological cycle, is highly effective in drought monitoring. This study investigates the efficiency of two machine-learning methods, random vector functional link (RVFL) and relevance vector machine (RVM), improved with new metaheuristic algorithms, quantum-based avian navigation optimizer algorithm (QANA), and artificial hummingbird algorithm (AHA) in modeling ET0 using limited climatic data, minimum temperature, maximum temperature, and extraterrestrial radiation. The outcomes of the hybrid RVFL-AHA, RVFL-QANA, RVM-AHA, and RVM-QANA models compared with single RVFL and RVM models. Various input combinations and three data split scenarios were employed. The results revealed that the AHA and QANA considerably improved the efficiency of RVFL and RVM methods in modeling ET0. Considering the periodicity component and extraterrestrial radiation as inputs improved the prediction accuracy of the applied methods.

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“…The recent estimation of reference evapotranspiration based on machine learning modeling, e.g. H2O-Deep Learning,Distributed Random Forest,Gradient Boosting Machine and Generalized Linear Model [ 107 ], Ensemble Extreme Machine Learning, Multi-layer Perceptrons-Neural Network, Support Vector Machine [ 108 ], Quantum Matrix Product State [ 111 ], CNN-LSTM and Conv-LSTM used for combine the features and modeling of ET [ 109 ], Deep learning versus gradient boosting used for predicting the pan evaporation [ 112 ],…”

Section: Literature Of Irrigation Schedulingmentioning

confidence: 99%

A Survey Towards Decision Support System on Smart Irrigation Scheduling Using Machine Learning approaches

Saggi

Jain

2022

Arch Computat Methods Eng

Self Cite

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From last decade, Big data analytics and machine learning is a hotspot research area in the domain of agriculture. Agriculture analytics is a data intensive multidisciplinary problem. Big data analytics becomes a key technology to perform analysis of voluminous data. Irrigation water management is a challenging task for sustainable agriculture. It depends on various parameters related to climate, soil and weather conditions. For accurate estimation of requirement of water for a crop a strong modeling is required. This paper aims to review the application of big data based decision support system framework for sustainable water irrigation management using intelligent learning approaches. We examined how such developments can be leveraged to design and implement the next generation of data, models, analytics and decision support tools for agriculture irrigation water system. Moreover, water irrigation management need to rapidly adapt state-of-the-art using big data technologies and ICT information technologies with the focus of developing application based on analytical modeling approach. This study introduces the area of research, including a irrigation water management in smart agriculture, the crop water model requirement, and the methods of irrigation scheduling, decision support system, and research motivation.

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Deep learning versus gradient boosting machine for pan evaporation prediction

Cited by 54 publications

References 76 publications

Collision Cross Section Prediction Based on Machine Learning

Collision Cross Section Prediction Based on Machine Learning

Modeling Potential Evapotranspiration by Improved Machine Learning Methods Using Limited Climatic Data

A Survey Towards Decision Support System on Smart Irrigation Scheduling Using Machine Learning approaches

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