New input selection procedure for machine learning methods in estimating daily global solar radiation

Biazar, Seyed Mostafa; Rahmani, Vahid; Isazadeh, Mohammad; Kişi, Özgür; Dinpashoh, Yagob

doi:10.1007/s12517-020-05437-0

Cited by 37 publications

(8 citation statements)

References 78 publications

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“…For soil microbial biomass carbon, using the chloroform fumigation K2SO4 extraction method, put 95 ml of a fresh soil sample equivalent to 5 g of dry soil (2 mm sieve) into a small white bottle, and then put the small white bottle into vacuum drying in the box. At the same time, put 3 beakers containing ethanol-free chloroform into an appropriate amount of silica sand to prevent the waterfall from boiling, and then put them into a beaker of dilute NaOH solution and a small beaker of distilled water for fumigation for 24 h (Biazar et al 2020). At the same time, the same soil weight was weighed as a control group.…”

Section: Determination Of Basic Soil Properties and Activated Carbon Componentsmentioning

confidence: 99%

RETRACTED ARTICLE: Mountain soil characteristics and agrotourism management optimization based on distributed collaboration

Zhanhui

2021

Arab J Geosci

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This article uses the distributed collaborative neural network process for analysis. It turns out that if there are dishonest participants, they will not get any data. The dishonest participants did not contribute to the training process throughout the training process, but they could get the training results of others. In order to understand the seasonal dynamics of soil organic carbon components and mineralization in different altitudes of alpine shrub meadows, the effects of different mountain soil carbon components on soil organic carbon mineralization were discussed, and the relationship between soil carbon components and soil physical and chemical properties was analyzed. This plays an important role in the development of dynamic changes of soil organic carbon in alpine mountain shrub meadows at different altitudes. In this study, a combination of field investigation and indoor analysis was used. By taking the soil of 3,800 m, 4000 m, and 4200 m semi-shady slope and semi-sun slope as the object of this research, it explored the different heights, seasons, and indoor cultivation conditions. The characteristics of mountain soil carbon minerals under the changing conditions of soil organic carbon pool. In this article, we take leisure agriculture and rural tourism development and management optimization as the research goal of this time, based on the sustainable development theory of leisure agriculture and rural tourism development. Using literature methods and on-site surveys, the research results of domestic and foreign scholars are collected, starting from the concept of leisure agriculture and rural tourism, and using the past experience of domestic and foreign leisure agriculture and leisure rural tourism development to analyze the current domestic and foreign leisure agriculture and the relationship between rural tourism development.

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Section: Determination Of Basic Soil Properties and Activated Carbon Componentsmentioning

confidence: 99%

RETRACTED ARTICLE: Mountain soil characteristics and agrotourism management optimization based on distributed collaboration

Zhanhui

2021

Arab J Geosci

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“…The GT is a powerful tool for identifying the best input scenarios that are applied in various fields, such as determining the inputs for predicting groundwater (Azadi et al, 2020), determining the inputs for modeling evaporation (Malik et al, 2020a), input selection for predicting solar radiation (Biazar et al, 2020), and selection of the best inputs for evapotranspiration (Seifi and Riahi, 2020). Assume a set of data based on the following equation:…”

Section: Improved Gamma Testmentioning

confidence: 99%

Inclusive Multiple Model Using Hybrid Artificial Neural Networks for Predicting Evaporation

Ehteram

Panahi

Ahmed

et al. 2022

Front. Environ. Sci.

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Predicting evaporation is essential for managing water resources in basins. Improvement of the prediction accuracy is essential to identify adequate inputs on evaporation. In this study, artificial neural network (ANN) is coupled with several evolutionary algorithms, i.e., capuchin search algorithm (CSA), firefly algorithm (FFA), sine cosine algorithm (SCA), and genetic algorithm (GA) for robust training to predict daily evaporation of seven synoptic stations with different climates. The inclusive multiple model (IMM) is then used to predict evaporation based on established hybrid ANN models. The adjusting model parameters of the current study is a major challenge. Also, another challenge is the selection of the best inputs to the models. The IMM model had significantly improved the root mean square error (RMSE) and Nash Sutcliffe efficiency (NSE) values of all the proposed models. The results for all stations indicated that the IMM model and ANN-CSA could outperform other models. The RMSE of the IMM was 18, 21, 22, 30, and 43% lower than those of the ANN-CSA, ANN-SCA, ANN-FFA, ANN-GA, and ANN models in the Sharekord station. The MAE of the IMM was 0.112 mm/day, while it was 0.189 mm/day, 0.267 mm/day, 0.267 mm/day, 0.389 mm/day, 0.456 mm/day, and 0.512 mm/day for the ANN-CSA, ANN-SCA, and ANN-FFA, ANN-GA, and ANN models, respectively, in the Tehran station. The current study proved that the inclusive multiple models based on improved ANN models considering the fuzzy reasoning had the high ability to predict evaporation.

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“…Over time, for optimizing the nonlinear problems, the ML models, including the support vector machine (SVM), have been utilized in numerous fields such as for predicting the penetration rate of tunnel-boring machines [33], solar radiation prediction [34], streamflow forecasting [35], landslide hazard modelling [36][37][38], seawater level simulation [39], forecasting electric load [40], and infiltration simulation [41,42]. The SVM approach was recommended by Vapnik [43] and derived from statistical learning theory to solve classification and regression problems [44].…”

Section: Support Vector Machinementioning

confidence: 99%

Evaluation of Machine Learning versus Empirical Models for Monthly Reference Evapotranspiration Estimation in Uttar Pradesh and Uttarakhand States, India

et al. 2022

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Reference evapotranspiration (ETo) plays an important role in agriculture applications such as irrigation scheduling, crop simulation, water budgeting, and reservoir operations. Therefore, the accurate estimation of ETo is essential for optimal utilization of available water resources on regional and global scales. The present study was conducted to estimate the monthly ETo at Nagina (Uttar Pradesh State) and Pantnagar (Uttarakhand State) stations by employing the three ML (machine learning) techniques including the SVM (support vector machine), M5P (M5P model tree), and RF (random forest) against the three empirical models (i.e., Valiantzas-1: V-1, Valiantzas-2: V-2, Valiantzas-3: V-3). Three different input combinations (i.e., C-1, C-2, C-3) were formulated by using 8-year (2009–2016) climatic data of wind speed (u), solar radiation (Rs), relative humidity (RH), and mean air temperature (T) recorded at both stations. The predictive efficacy of ML and the empirical models was evaluated based on five statistical indicators i.e., CC (correlation coefficient), WI (Willmott index), EC (efficiency coefficient), RMSE (root mean square error), and MAE (mean absolute error) presented through a heatmap along with graphical interpretation (Taylor diagram, time-series, and scatter plots). The results showed that the SVM-1 model corresponding to the C-1 input combination outperformed the other ML and empirical models at both stations. Moreover, the SVM-1 model had the lowest MAE (0.076, 0.047 mm/month) and RMSE (0.110, 0.063 mm/month), and highest EC (0.995, 0.999), CC (0.998, 0.999), and WI (0.999, 1.000) values during validation period at Nagina and Pantnagar stations, respectively, and closely followed by the M5P model. Consequently, the ML model (i.e., SVM) was found to be more robust, and reliable in monthly ETo estimation and can be used as a promising alternative to empirical models at both study locations.

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New input selection procedure for machine learning methods in estimating daily global solar radiation

Cited by 37 publications

References 78 publications

RETRACTED ARTICLE: Mountain soil characteristics and agrotourism management optimization based on distributed collaboration

RETRACTED ARTICLE: Mountain soil characteristics and agrotourism management optimization based on distributed collaboration

Inclusive Multiple Model Using Hybrid Artificial Neural Networks for Predicting Evaporation

Evaluation of Machine Learning versus Empirical Models for Monthly Reference Evapotranspiration Estimation in Uttar Pradesh and Uttarakhand States, India

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