Victor H. Quej, Crescencio de la Cruz Castillo, J. Almorox, B. Rivera-Hernández
{"title":"基于温度、降雨和相对湿度的人工智能模型在温暖亚湿润环境下每日参考蒸散量预测的评价","authors":"Victor H. Quej, Crescencio de la Cruz Castillo, J. Almorox, B. Rivera-Hernández","doi":"10.36253/ijam-1373","DOIUrl":null,"url":null,"abstract":"Accurate estimation of reference evapotranspiration is essential for agricultural management and water resources engineering applications. In the present study, the ability and precision of three artificial intelligence (AI) models (i.e., Support Vector Machines (SVMs), Adaptive Neuro-Fuzzy Inference System (ANFIS) and Categorical Boosting (CatBoost)) were assessed for estimating daily reference evapotranspiration (ET0) using limited weather data from five locations in a warm sub-humid climate in Mexico. The Penman–Monteith FAO-56 equation was used as a reference target for ET0 values. Three different input combinations were investigated, namely: temperature-based (minimum and maximum air temperature), rainfall-based (minimum air temperature, maximum air temperature and rainfall), and relative humidity-based (minimum air temperature, maximum air temperature and relative humidity). Extraterrestrial radiation values were used in all combinations. The temperature-based AI models were compared with the conventional Hargreaves–Samani (HS) model commonly used to estimate ET0 when only temperature records are available. The goodness of fit for all models was assessed in terms of the coefficient of determination (R2), Nash–Sutcliffe model efficiency coefficient (NSE), root mean square error (RMSE) and mean absolute error (MAE). The results showed that among the AI models evaluated, the SVM models outperformed ANFIS and CatBoost for modeling ET0. Further, the influence of relative humidity and rainfall on the performance of the models was investigated. The analysis indicated that relative humidity significantly improved the accuracy of the models. Finally, the results showed a better response of the temperature-based AI models over the HS method. AI models can be an adequate alternative to conventional models for ET0 modeling.","PeriodicalId":54371,"journal":{"name":"Italian Journal of Agrometeorology-Rivista Italiana Di Agrometeorologia","volume":" ","pages":""},"PeriodicalIF":1.6000,"publicationDate":"2022-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Evaluation of artificial intelligence models for daily prediction of reference evapotranspiration using temperature, rainfall and relative humidity in a warm sub-humid environment\",\"authors\":\"Victor H. Quej, Crescencio de la Cruz Castillo, J. Almorox, B. Rivera-Hernández\",\"doi\":\"10.36253/ijam-1373\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Accurate estimation of reference evapotranspiration is essential for agricultural management and water resources engineering applications. In the present study, the ability and precision of three artificial intelligence (AI) models (i.e., Support Vector Machines (SVMs), Adaptive Neuro-Fuzzy Inference System (ANFIS) and Categorical Boosting (CatBoost)) were assessed for estimating daily reference evapotranspiration (ET0) using limited weather data from five locations in a warm sub-humid climate in Mexico. The Penman–Monteith FAO-56 equation was used as a reference target for ET0 values. Three different input combinations were investigated, namely: temperature-based (minimum and maximum air temperature), rainfall-based (minimum air temperature, maximum air temperature and rainfall), and relative humidity-based (minimum air temperature, maximum air temperature and relative humidity). Extraterrestrial radiation values were used in all combinations. The temperature-based AI models were compared with the conventional Hargreaves–Samani (HS) model commonly used to estimate ET0 when only temperature records are available. The goodness of fit for all models was assessed in terms of the coefficient of determination (R2), Nash–Sutcliffe model efficiency coefficient (NSE), root mean square error (RMSE) and mean absolute error (MAE). The results showed that among the AI models evaluated, the SVM models outperformed ANFIS and CatBoost for modeling ET0. Further, the influence of relative humidity and rainfall on the performance of the models was investigated. The analysis indicated that relative humidity significantly improved the accuracy of the models. Finally, the results showed a better response of the temperature-based AI models over the HS method. AI models can be an adequate alternative to conventional models for ET0 modeling.\",\"PeriodicalId\":54371,\"journal\":{\"name\":\"Italian Journal of Agrometeorology-Rivista Italiana Di Agrometeorologia\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":1.6000,\"publicationDate\":\"2022-07-19\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Italian Journal of Agrometeorology-Rivista Italiana Di Agrometeorologia\",\"FirstCategoryId\":\"97\",\"ListUrlMain\":\"https://doi.org/10.36253/ijam-1373\",\"RegionNum\":4,\"RegionCategory\":\"农林科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"AGRONOMY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Italian Journal of Agrometeorology-Rivista Italiana Di Agrometeorologia","FirstCategoryId":"97","ListUrlMain":"https://doi.org/10.36253/ijam-1373","RegionNum":4,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"AGRONOMY","Score":null,"Total":0}
Evaluation of artificial intelligence models for daily prediction of reference evapotranspiration using temperature, rainfall and relative humidity in a warm sub-humid environment
Accurate estimation of reference evapotranspiration is essential for agricultural management and water resources engineering applications. In the present study, the ability and precision of three artificial intelligence (AI) models (i.e., Support Vector Machines (SVMs), Adaptive Neuro-Fuzzy Inference System (ANFIS) and Categorical Boosting (CatBoost)) were assessed for estimating daily reference evapotranspiration (ET0) using limited weather data from five locations in a warm sub-humid climate in Mexico. The Penman–Monteith FAO-56 equation was used as a reference target for ET0 values. Three different input combinations were investigated, namely: temperature-based (minimum and maximum air temperature), rainfall-based (minimum air temperature, maximum air temperature and rainfall), and relative humidity-based (minimum air temperature, maximum air temperature and relative humidity). Extraterrestrial radiation values were used in all combinations. The temperature-based AI models were compared with the conventional Hargreaves–Samani (HS) model commonly used to estimate ET0 when only temperature records are available. The goodness of fit for all models was assessed in terms of the coefficient of determination (R2), Nash–Sutcliffe model efficiency coefficient (NSE), root mean square error (RMSE) and mean absolute error (MAE). The results showed that among the AI models evaluated, the SVM models outperformed ANFIS and CatBoost for modeling ET0. Further, the influence of relative humidity and rainfall on the performance of the models was investigated. The analysis indicated that relative humidity significantly improved the accuracy of the models. Finally, the results showed a better response of the temperature-based AI models over the HS method. AI models can be an adequate alternative to conventional models for ET0 modeling.
期刊介绍:
Among the areas of specific interest of the journal there are: ecophysiology; phenology; plant growth, quality and quantity of production; plant pathology; entomology; welfare conditions of livestocks; soil physics and hydrology; micrometeorology; modeling, simulation and forecasting; remote sensing; territorial planning; geographical information systems and spatialization techniques; instrumentation to measure physical and biological quantities; data validation techniques, agroclimatology; agriculture scientific dissemination; support services for farmers.