A Comparative Study of Different Machine Learning Algorithms in Predicting the Content of Ilmenite in Titanium Placer

Maize is a widely grown crop in China, and the relationships between agroclimatic parameters and maize yield are complicated, hence, accurate and timely yield prediction is challenging. Here, climate, satellite data, and meteorological indices were integrated to predict maize yield at the city-level in China from 2000 to 2015 using four machine learning approaches, e.g., cubist, random forest (RF), extreme gradient boosting (Xgboost), and support vector machine (SVM). The climate variables included the diffuse flux of photosynthetic active radiation (PDf), the diffuse flux of shortwave radiation (SDf), the direct flux of shortwave radiation (SDr), minimum temperature (Tmn), potential evapotranspiration (Pet), vapor pressure deficit (Vpd), vapor pressure (Vap), and wet day frequency (Wet). Satellite data, including the enhanced vegetation index (EVI), normalized difference vegetation index (NDVI), and adjusted vegetation index (SAVI) from the Moderate Resolution Imaging Spectroradiometer (MODIS), were used. Meteorological indices, including growing degree day (GDD), extreme degree day (EDD), and the Standardized Precipitation Evapotranspiration Index (SPEI), were used. The results showed that integrating all climate, satellite data, and meteorological indices could achieve the highest accuracy. The highest estimated correlation coefficient (R) values for the cubist, RF, SVM, and Xgboost methods were 0.828, 0.806, 0.742, and 0.758, respectively. The climate, satellite data, or meteorological indices inputs from all growth stages were essential for maize yield prediction, especially in late growth stages. R improved by about 0.126, 0.117, and 0.143 by adding climate data from the early, peak, and late-period to satellite data and meteorological indices from all stages via the four machine learning algorithms, respectively. R increased by 0.016, 0.016, and 0.017 when adding satellite data from the early, peak, and late stages to climate data and meteorological indices from all stages, respectively. R increased by 0.003, 0.032, and 0.042 when adding meteorological indices from the early, peak, and late stages to climate and satellite data from all stages, respectively. The analysis found that the spatial divergences were large and the R value in Northwest region reached 0.942, 0.904, 0.934, and 0.850 for the Cubist, RF, SVM, and Xgboost, respectively. This study highlights the advantages of using climate, satellite data, and meteorological indices for large-scale maize yield estimation with machine learning algorithms.

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“…According to LV et al [57], a regression model was developed at each node for pruning and prediction as follows:…”

Section: Machine Learning Methods For Estimating Maize Yieldmentioning

confidence: 99%

Prediction of Maize Yield at the City Level in China Using Multi-Source Data

et al. 2021

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“…Figure 7c1–c3 illustrates the result of density plots of the residuals. Most of the residuals of the ANN and Cubist models are normally distributed, suggesting that these models fit the data well (Lv et al., 2020). Moreover, the density of the nonlinear models (ANN and Cubist) appears to be more stable than the linear model (MLR).…”

Section: Resultsmentioning

confidence: 99%

Neural network and cubist algorithms to predict fecal coliform content in treated wastewater by multi‐soil‐layering system for potential reuse

et al. 2020

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This study aims to find the most accurate machine learning algorithms as compared to linear regression for prediction of fecal coliform (FC) concentration in the effluent of a multi‐soil‐layering (MSL) system and to identify the input variables affecting FC removal from domestic wastewater. The effluent quality of two different designs of the MSL system was evaluated and compared for several parameters for potential reuse in agriculture. The first system consisted of a single‐stage MSL (MSL‐SS), and the second system consisted of a two‐stage MSL (MSL‐TS). The concentration of FC in the effluent of the MSL‐TS system was estimated by three machine learning algorithms: artificial neural network (ANN), Cubist, and multiple linear regression (MLR). The accuracy of the models was measured by comparing the real and predicted values. Significant (p < .001) improvements were noted for the removal of pollutants by the MSL‐TS system compared with the MSL‐SS system. Overall, the water quality parameters investigated complied with FAO irrigation standards. The predictive performance of the models has been compared and evaluated using several metrics. The results revealed that the ANN model yielded a superior predictive performance (R2 = .953), followed by the Cubist model (R2 = .946) and the MLR technique (R2 = .481). Based on the accurate model (ANN), the degree of influence of each predictor was investigated, and the results show that total suspended solids and pH have proved to be more useful for predicting FC concentrations.

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“…LV et al [4] compared seven machine learning techniques, including random forest (RF), ANN, k-nearest neighbors (kNN), cubist, support vector machine (SVM), stochastic gradient boosting (SGB), and classification and regression tree (CART) to predict ilmenite content in beach placers. Of the 405 beach placer borehole samples, 325 samples were used to build the machine learning models, and 80 remaining samples were used in the models.…”

Section: Artificial Intelligencementioning

confidence: 99%