Predictive modeling of groundwater nitrate pollution using Random Forest and multisource variables related to intrinsic and specific vulnerability: A case study in an agricultural setting (Southern Spain)

Rodríguez-Galiano, Víctor; Mendes, María Manuela; Garcia-Soldado, Maria Jose; Chica-Olmo, Mario; Ribeiro, Luís

doi:10.1016/j.scitotenv.2014.01.001

Cited by 307 publications

(95 citation statements)

References 70 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Compared with simple decision trees, RFR runs efficiently on high-dimensional data sets, and it is more accurate and robust to noise 18,19 . Besides, RFR has great advantages over traditional intelligent algorithms 18–24 . On the one hand, it has a very fast learning process and can handle a large number of input variables while assessing the importance of variables.…”

Section: Methodsmentioning

confidence: 99%

An approach to predict the height of fractured water-conducting zone of coal roof strata using random forest regression

Zhao

2018

Sci Rep

View full text Add to dashboard Cite

Water inrushes from coal-roof strata account for a great proportion of coal mine accidents, and the height of fractured water-conducting zone (FWCZ) is of significant importance for the safe production of coal mines. A novel and promising model for predicting the height of FWCZ was proposed based on random forest regression (RFR), which is a powerful intelligent machine learning algorithm. RFR has high prediction accuracy and is robust in dealing with the complicated and non-linear problems. Also, it can evaluate the importance of the variables. In this study, the proposed model was applied to Hongliu Coal Mine in Northwest China. 85 field measured samples were collected in total, with 60 samples (70%) used for training and 20 (30%) used for validation. For comparison, a support vector machine (SVM) model was also constructed for the prediction. The results show that the two models are in accordance with the field measured data, and RFR shows a better performance on good tolerance to outliers and noises and efficiently on high-dimensional data sets. It is demonstrated that RFR is more practicable and accurate to predict the height of FWCZ. The achievements will be helpful in preventing and controlling the water inrushes from coal-roof strata, and also can be extended to various engineering applications.

show abstract

Section: Methodsmentioning

confidence: 99%

An approach to predict the height of fractured water-conducting zone of coal roof strata using random forest regression

Zhao

2018

Sci Rep

View full text Add to dashboard Cite

show abstract

“…A regression tree (RT), where each non-leaf node contains a set of decision rules and each leaf node is the outcome of a prediction, is a form of decision tree (DT) (Quinlan 1993;Rodriguez-Galiano et al 2014). …”

Section: Random Forest Regression Algorithm (Rfr)mentioning

confidence: 99%

“…Many studies have applied RF to study groundwater from different aspects, such as its vulnerability to nitrate pollution (Rodriguez-Galiano et al 2014), the dissolution of organic nitrogen in it (Wang et al 2016), and the potential mapping of groundwater (Rahmati et al 2016). Moreover, the RF model has been compared with the SVM in terms of surface water level prediction (Li et al 2015).…”

Section: Introductionmentioning

confidence: 99%

Short-term prediction of groundwater level using improved random forest regression with a combination of random features

et al. 2018

View full text Add to dashboard Cite

To solve the problem where by the available on-site input data are too scarce to predict the level of groundwater, this paper proposes an algorithm to make this prediction called the canonical correlation forest algorithm with a combination of random features. To assess the effectiveness of the proposed algorithm, groundwater levels and meteorological data for the Daguhe River groundwater source field, in Qingdao, China, were used. First, the results of a comparison among three regressors showed that the proposed algorithm is superior in terms of forecasting variations in groundwater level. Second, the results of experiments were used to show the comparative superiority of the proposed method in terms of training time and complexity of parameter optimization. Third, using the proposed algorithm, the highest prediction accuracy was achieved by employing precipitation P(t − 2), temperature T(t), and groundwater level H(t) as the best time lag. This improved random forest regression model yielded higher accuracy in forecasting the variation in groundwater level. The proposed algorithm can also be applied to cases involving low-dimensional data.

show abstract

“…Victor Rodriguez-Galiano et al evaluated groundwater defenselessness to nitrate contamination utilizing Irregular Forest calculation and indicated technique to include a choice way to deal with decrease in the quantity of explicative factors [4].…”

Section: Literature Surveymentioning

confidence: 99%

Adapting Mining into Agriculture Sector with Machine Learning Techniques

Kumar¹,

Bhramaramba²

2017

IJCA

View full text Add to dashboard Cite

show abstract

Predictive modeling of groundwater nitrate pollution using Random Forest and multisource variables related to intrinsic and specific vulnerability: A case study in an agricultural setting (Southern Spain)

Cited by 307 publications

References 70 publications

An approach to predict the height of fractured water-conducting zone of coal roof strata using random forest regression

An approach to predict the height of fractured water-conducting zone of coal roof strata using random forest regression

Short-term prediction of groundwater level using improved random forest regression with a combination of random features

Adapting Mining into Agriculture Sector with Machine Learning Techniques

Contact Info

Product

Resources

About