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P L, D SS. A novel model for rainfall prediction using hybrid stochastic-based Bayesian optimization algorithm. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2023; 30:92555-92567. [PMID: 37493914 DOI: 10.1007/s11356-023-28734-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: 04/18/2023] [Accepted: 07/07/2023] [Indexed: 07/27/2023]
Abstract
Rainfall forecasting is considered one of the key concerns in the meteorological department because it is related strongly to social as well as economic factors. But, because of modern context of climatic conditions and the intense activities of humans, the forecasting procedure of rainfall patterns becomes more problematic. Therefore, this paper proposes a novel timely and reliable rainfall prediction model using a hybrid stochastic Bayesian optimization approach (HS-BOA). The weather dataset containing different meteorological geographical features is provided as input to the introduced prediction method. Hybrid stochastic (HS) specifications are tuned by the Bayesian optimization algorithm (BOA) to upgrade the prediction accuracy. The weather data are initially preprocessed through the pipelines, namely, data separation, missing value prediction, weather condition cod separation, and normalization. After preprocessing, the highly correlated features are removed by correlation matrix using the Pearson correlation coefficient. Then, the most significant features which contribute more to predicting rainfall are selected through the feature selection process. At last, the suggested rainfall forecasting model accurately predicts rainfall using optimized parameters. The experimental analysis is performed, and for the proposed HS-BOA, MAE, RMSE, and COD, values attained for rainfall prediction are 0.513 mm, 59.90 mm, and 40.56 mm respectively. As a result, the proposed HS-BOA approach achieves minimum error rates with increased prediction accuracy than other existing approaches.
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Affiliation(s)
- Lathika P
- Department of Mathematics, Noorul Islam Centre for Higher Education, Kumarakovil, Thuckalay, Tamil Nadu, India.
| | - Sheeba Singh D
- Department of Mathematics, Noorul Islam Centre for Higher Education, Kumarakovil, Thuckalay, Tamil Nadu, India
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Jiang Z, Yang S, Dong S, Pang Q, Smith P, Abdalla M, Zhang J, Wang G, Xu Y. Simulating soil salinity dynamics, cotton yield and evapotranspiration under drip irrigation by ensemble machine learning. FRONTIERS IN PLANT SCIENCE 2023; 14:1143462. [PMID: 37351200 PMCID: PMC10282761 DOI: 10.3389/fpls.2023.1143462] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/13/2023] [Accepted: 05/11/2023] [Indexed: 06/24/2023]
Abstract
Cotton is widely used in textile, decoration, and industry, but it is also threatened by soil salinization. Drip irrigation plays an important role in improving water and fertilization utilization efficiency and ensuring crop production in arid areas. Accurate prediction of soil salinity and crop evapotranspiration under drip irrigation is essential to guide water management practices in arid and saline areas. However, traditional hydrological models such as Hydrus require more variety of input parameters and user expertise, which limits its application in practice, and machine learning (ML) provides a potential alternative. Based on a global dataset collected from 134 pieces of literature, we proposed a method to comprehensively simulate soil salinity, evapotranspiration (ET) and cotton yield. Results showed that it was recommended to predict soil salinity, crop evapotranspiration and cotton yield based on soil data (bulk density), meteorological factors, irrigation data and other data. Among them, meteorological factors include annual average temperature, total precipitation, year. Irrigation data include salinity in irrigation water, soil matric potential and irrigation water volume, while other data include soil depth, distance from dripper, days after sowing (for EC and soil salinity), fertilization rate (for yield and ET). The accuracy of the model has reached a satisfactory level, R2 in 0.78-0.99. The performance of stacking ensemble ML was better than that of a single model, i.e., gradient boosting decision tree (GBDT); random forest (RF); extreme gradient boosting regression (XGBR), with R2 increased by 0.02%-19.31%. In all input combinations, other data have a greater impact on the model accuracy, while the RMSE of the S1 scenario (input without meteorological factors) without meteorological data has little difference, which is -34.22%~19.20% higher than that of full input. Given the wide application of drip irrigation in cotton, we recommend the application of ensemble ML to predict soil salinity and crop evapotranspiration, thus serving as the basis for adjusting the irrigation schedule.
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Affiliation(s)
- Zewei Jiang
- College of Agricultural Science and Engineering, Hohai University, Nanjing, China
| | - Shihong Yang
- College of Agricultural Science and Engineering, Hohai University, Nanjing, China
- State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Hohai University, Nanjing, China
- Cooperative Innovation Center for Water Safety & Hydro Science, Hohai University, Nanjing, China
| | - Shide Dong
- CAS Key Laboratory of Coastal Environmental Processes and Ecological Remediation, Yantai Institute of Coastal Zone Research (YIC), Chinese Academy of Sciences (CAS), Shandong Key Laboratory of Coastal Environmental Processes, YICCAS, Yantai, Shandong, China
- Shandong Saline-Alkali Land Modern Agriculture Company, Dongying, China
| | - Qingqing Pang
- Nanjing Institute of Environmental Sciences, Ministry of Ecology and Environment, Nanjing, China
| | - Pete Smith
- Institute of Biological & Environmental Sciences, University of Aberdeen, Aberdeen, United Kingdom
| | - Mohamed Abdalla
- Institute of Biological & Environmental Sciences, University of Aberdeen, Aberdeen, United Kingdom
| | - Jie Zhang
- College of Agricultural Science and Engineering, Hohai University, Nanjing, China
| | - Guangmei Wang
- CAS Key Laboratory of Coastal Environmental Processes and Ecological Remediation, Yantai Institute of Coastal Zone Research (YIC), Chinese Academy of Sciences (CAS), Shandong Key Laboratory of Coastal Environmental Processes, YICCAS, Yantai, Shandong, China
| | - Yi Xu
- College of Agricultural Science and Engineering, Hohai University, Nanjing, China
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Lučin I, Družeta S, Mauša G, Alvir M, Grbčić L, Lušić DV, Sikirica A, Kranjčević L. Predictive modeling of microbiological seawater quality in karst region using cascade model. THE SCIENCE OF THE TOTAL ENVIRONMENT 2022; 851:158009. [PMID: 35987218 DOI: 10.1016/j.scitotenv.2022.158009] [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: 05/05/2022] [Revised: 08/06/2022] [Accepted: 08/09/2022] [Indexed: 06/15/2023]
Abstract
This paper presents an in-depth analysis of seawater quality measurements during the bathing seasons from year 2009 to 2020 in the city of Rijeka, Croatia. Due to rare occurrences of measurements with less than excellent water quality, considered dataset is deeply imbalanced. Additionally, it incorporates measurements under the influence of submerged groundwater discharges (SGD), which were observed in some bathing locations. These discharges were previously thought to dry up during the summer season and are now suspected to be one of the causes of increased Escherichia coli values. Consequently, and in view of the fact that the accuracy of prediction models can be significantly influenced by temporal and spatial variation of the input data, a novel cascade prediction modeling strategy was proposed. It consists of a sequence of prediction models which tend to identify general environmental conditions which confidently lead to excellent bathing water quality. The proposed model uses environmental features which can rather easily be estimated or obtained from the weather forecast. The model was trained on a highly biased dataset, consisting of data from locations with and without SGD influence, and for the time period spanning extremely dry and warm seasons, extremely wet seasons, as well as normal seasons. To simulate realistic application, the model was tested using temporal and spatial stratification of data. The cascade strategy was shown to be a good approach for reliably detecting environmental parameters which produce excellent water quality. Proposed model is designed as a filter method, where instances classified as less-than-excellent water quality require further analysis. The cascade model provides great flexibility as it can be customized to the particular needs of the investigated area and dataset specifics.
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Affiliation(s)
- Ivana Lučin
- Department of Fluid Mechanics and Computational Engineering, Faculty of Engineering, University of Rijeka, Vukovarska 58, Rijeka 51000, Croatia; Center for Advanced Computing and Modelling, University of Rijeka, Radmile Matejčić 2, Rijeka 51000, Croatia
| | - Siniša Družeta
- Department of Fluid Mechanics and Computational Engineering, Faculty of Engineering, University of Rijeka, Vukovarska 58, Rijeka 51000, Croatia; Center for Advanced Computing and Modelling, University of Rijeka, Radmile Matejčić 2, Rijeka 51000, Croatia
| | - Goran Mauša
- Department of Computer Engineering, Faculty of Engineering, University of Rijeka, Vukovarska 58, Rijeka 51000, Croatia; Center for Advanced Computing and Modelling, University of Rijeka, Radmile Matejčić 2, Rijeka 51000, Croatia
| | - Marta Alvir
- Department of Fluid Mechanics and Computational Engineering, Faculty of Engineering, University of Rijeka, Vukovarska 58, Rijeka 51000, Croatia
| | - Luka Grbčić
- Department of Fluid Mechanics and Computational Engineering, Faculty of Engineering, University of Rijeka, Vukovarska 58, Rijeka 51000, Croatia; Center for Advanced Computing and Modelling, University of Rijeka, Radmile Matejčić 2, Rijeka 51000, Croatia
| | - Darija Vukić Lušić
- Center for Advanced Computing and Modelling, University of Rijeka, Radmile Matejčić 2, Rijeka 51000, Croatia; Department of Environmental Health, Faculty of Medicine, University of Rijeka, Braće Branchetta 20/1, Rijeka 51000, Croatia; Department of Environmental Health, Teaching Institute of Public Health of Primorje-Gorski Kotar County, Krešimirova 52a, Rijeka 51000, Croatia
| | - Ante Sikirica
- Department of Fluid Mechanics and Computational Engineering, Faculty of Engineering, University of Rijeka, Vukovarska 58, Rijeka 51000, Croatia; Center for Advanced Computing and Modelling, University of Rijeka, Radmile Matejčić 2, Rijeka 51000, Croatia
| | - Lado Kranjčević
- Department of Fluid Mechanics and Computational Engineering, Faculty of Engineering, University of Rijeka, Vukovarska 58, Rijeka 51000, Croatia; Center for Advanced Computing and Modelling, University of Rijeka, Radmile Matejčić 2, Rijeka 51000, Croatia.
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Statistics in Hydrology. WATER 2022. [DOI: 10.3390/w14101571] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Statistical methods have a long history in the analysis of hydrological data for designing, planning, infilling, forecasting, and specifying better models to assess scenarios of land use and climate change in catchments [...]
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