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Swain SS, Khura TK, Sahoo PK, Chobhe KA, Al-Ansari N, Kushwaha HL, Kushwaha NL, Panda KC, Lande SD, Singh C. Proportional impact prediction model of coating material on nitrate leaching of slow-release Urea Super Granules (USG) using machine learning and RSM technique. Sci Rep 2024; 14:3053. [PMID: 38321086 PMCID: PMC10847469 DOI: 10.1038/s41598-024-53410-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2023] [Accepted: 01/31/2024] [Indexed: 02/08/2024] Open
Abstract
An accurate assessment of nitrate leaching is important for efficient fertiliser utilisation and groundwater pollution reduction. However, past studies could not efficiently model nitrate leaching due to utilisation of conventional algorithms. To address the issue, the current research employed advanced machine learning algorithms, viz., Support Vector Machine, Artificial Neural Network, Random Forest, M5 Tree (M5P), Reduced Error Pruning Tree (REPTree) and Response Surface Methodology (RSM) to predict and optimize nitrate leaching. In this study, Urea Super Granules (USG) with three different coatings were used for the experiment in the soil columns, containing 1 kg soil with fertiliser placed in between. Statistical parameters, namely correlation coefficient, Mean Absolute Error, Willmott index, Root Mean Square Error and Nash-Sutcliffe efficiency were used to evaluate the performance of the ML techniques. In addition, a comparison was made in the test set among the machine learning models in which, RSM outperformed the rest of the models irrespective of coating type. Neem oil/ Acacia oil(ml): clay/sulfer (g): age (days) for minimum nitrate leaching was found to be 2.61: 1.67: 2.4 for coating of USG with bentonite clay and neem oil without heating, 2.18: 2: 1 for bentonite clay and neem oil with heating and 1.69: 1.64: 2.18 for coating USG with sulfer and acacia oil. The research would provide guidelines to researchers and policymakers to select the appropriate tool for precise prediction of nitrate leaching, which would optimise the yield and the benefit-cost ratio.
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Affiliation(s)
- Sidhartha Sekhar Swain
- Division of Agricultural Engineering, ICAR-Indian Agricultural Research Institute, New Delhi, 110012, India
| | - Tapan Kumar Khura
- Division of Agricultural Engineering, ICAR-Indian Agricultural Research Institute, New Delhi, 110012, India
| | - Pramod Kumar Sahoo
- Division of Agricultural Engineering, ICAR-Indian Agricultural Research Institute, New Delhi, 110012, India
| | - Kapil Atmaram Chobhe
- Division of Soil Science and Agricultural Chemistry, ICAR-Indian Agricultural Research Institute, New Delhi, 110012, India
| | - Nadhir Al-Ansari
- Department of Civil, Environmental and Natural Resources Engineering, Lulea University of Technology, 97187, Lulea, Sweden.
| | - Hari Lal Kushwaha
- Division of Agricultural Engineering, ICAR-Indian Agricultural Research Institute, New Delhi, 110012, India
| | - Nand Lal Kushwaha
- Division of Agricultural Engineering, ICAR-Indian Agricultural Research Institute, New Delhi, 110012, India
| | - Kanhu Charan Panda
- Department of Soil Conservation, National PG College (Barhalganj), DDU Gorakhpur University, Gorakhpur, UP, 273402, India
| | - Satish Devram Lande
- Division of Agricultural Engineering, ICAR-Indian Agricultural Research Institute, New Delhi, 110012, India
| | - Chandu Singh
- Division of Genetics, ICAR-Indian Agricultural Research Institute, New Delhi, 110012, India
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Zhao L, Zhao X, Li Y, Shi Y, Zhou H, Li X, Wang X, Xing X. Applicability of hybrid bionic optimization models with kernel-based extreme learning machine algorithm for predicting daily reference evapotranspiration: a case study in arid and semiarid regions, China. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2023; 30:22396-22412. [PMID: 36289123 DOI: 10.1007/s11356-022-23786-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/07/2022] [Accepted: 10/19/2022] [Indexed: 06/16/2023]
Abstract
The accurate prediction of daily reference crop evapotranspiration (ETO) enables effective management decision-making for agricultural water resources; this is crucial for developing water-efficient agriculture. To improve the accuracy of ETO forecasts in data-deficient areas, this study uses a decision tree algorithm (classification and regression tree [CART]) to obtain the effects of various factors on ETO at typical stations in arid and semiarid regions of China. A combination of factors with considerable influence on the model was selected as the input for constructing a kernel-extreme-learning-machine (KELM) daily reference evapotranspiration prediction model, and three bionic optimization algorithms (i.e., sparrow search optimization algorithm, Harris Hawks optimization algorithm, and lion swarm optimization algorithm) were integrated to optimize KELM prediction model parameters and improve the accuracy of daily reference evapotranspiration prediction. The results indicate that temperature (maximum or minimum temperature) is the primary factor influencing ETO, and the range of importance is 0.399-0.554. RH and Ra are also key factors influencing ETO; the hybrid model optimized using the bionic optimization algorithm provides advantages over the independent KELM model, and the SSA-KELM model has the highest accuracy among hybrid models, with a root-mean-square error of 0.408-1.964, R2 of 0.545-0.982, mean absolute error of 0.273-1.086, and Nash-Sutcliffe efficiency coefficient of 0.658-0.967. The top five factors extracted using the CART algorithm are recommended as inputs for constructing the SSA-KELM model for ETO estimation in arid and semiarid regions of China, and this model can also serve as a reference for ETO forecasting in similar regions.
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Affiliation(s)
- Long Zhao
- College of Agricultural Equipment Engineering, Henan University of Science and Technology, Luoyang, 471000, Henan Province, China
| | - Xinbo Zhao
- College of Agricultural Equipment Engineering, Henan University of Science and Technology, Luoyang, 471000, Henan Province, China
| | - Yuanze Li
- College of Agricultural Equipment Engineering, Henan University of Science and Technology, Luoyang, 471000, Henan Province, China
| | - Yi Shi
- College of Agricultural Equipment Engineering, Henan University of Science and Technology, Luoyang, 471000, Henan Province, China
| | - Hanmi Zhou
- College of Agricultural Equipment Engineering, Henan University of Science and Technology, Luoyang, 471000, Henan Province, China
| | - Xiuzhen Li
- College of Horticulture and Plant Protection, Henan University of Science and Technology, Luoyang, 471000, Henan Province, China
| | - Xiaodong Wang
- College of Agriculture, Henan University of Science and Technology, Henan Province, Luoyang, 471000, China
| | - Xuguang Xing
- Key Laboratory for Agricultural Soil and Water Engineering in Arid Area of Ministry of Education, Northwest A&F University, Yangling, 712100, Shaanxi Province, China.
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Jia Y, Wang H, Li P, Su Y, Wang F, Huo S. Particle swarm optimization algorithm with Gaussian exponential model to predict daily and monthly global solar radiation in Northeast China. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2023; 30:12769-12784. [PMID: 36114967 DOI: 10.1007/s11356-022-22934-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/04/2022] [Accepted: 09/04/2022] [Indexed: 06/15/2023]
Abstract
Reliable global solar radiation (Rs) information is crucial for the design and management of solar energy systems for agricultural and industrial production. However, Rs measurements are unavailable in many regions of the world, which impedes the development and application of solar energy. To accurately estimate Rs, particle swarm optimization (PSO) algorithm integrating Gaussian exponential model (GEM) was proposed for estimating daily and monthly global Rs in Northeast China. The PSO-GEM was compared with four other machine learning models and two empirical models to assess its applicability using daily meteorological data from 1997 to 2016 from four stations in Northeast China. The results showed that in different stations, the PSO-GEM with full climatic data as inputs showed the highest accuracy to estimate daily Rs with RMSE, RRMSE, MAE, R2, and Ens values of 1.045-1.719 MJ m-2 d-1, 7.6-12.7%, 0.801-1.283 MJ m-2 d-1, 0.953-0.981, and 0.946-0.977, respectively. The PSO-GEM showed the highest accuracy to estimate monthly Rs with RMSE, RRMSE, MAE, R2, and Ens values of 0.197-0.575 MJ m-2 d-1, 1.5-7.0%, 0.137-0.499 MJ m-2 d-1, 0.999-1, and 0.992-0.999, respectively. Overall, the PSO-GEM had the highest accuracy under different inputs and is recommended for modeling daily and monthly Rs in Northeast China.
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Affiliation(s)
- Yue Jia
- Hebei University of Water Resources and Electric Engineering & Remote Sensing and Smart Water Innovation Center, Cangzhou, 061001, China
- Center for Water Automation and Information Application Technology, Hebei University, Cangzhou, 061001, China
| | - Hui Wang
- Hebei University of Water Resources and Electric Engineering & Remote Sensing and Smart Water Innovation Center, Cangzhou, 061001, China.
- Center for Water Automation and Information Application Technology, Hebei University, Cangzhou, 061001, China.
| | - Pengcheng Li
- Hebei University of Water Resources and Electric Engineering & Remote Sensing and Smart Water Innovation Center, Cangzhou, 061001, China
- Center for Water Automation and Information Application Technology, Hebei University, Cangzhou, 061001, China
| | - Yongjun Su
- Hebei University of Water Resources and Electric Engineering & Remote Sensing and Smart Water Innovation Center, Cangzhou, 061001, China
- Center for Water Automation and Information Application Technology, Hebei University, Cangzhou, 061001, China
| | - Fengchun Wang
- Hebei University of Water Resources and Electric Engineering & Remote Sensing and Smart Water Innovation Center, Cangzhou, 061001, China
- Center for Water Automation and Information Application Technology, Hebei University, Cangzhou, 061001, China
| | - Shuyi Huo
- Hebei University of Water Resources and Electric Engineering & Remote Sensing and Smart Water Innovation Center, Cangzhou, 061001, China
- Center for Water Automation and Information Application Technology, Hebei University, Cangzhou, 061001, China
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