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Drought classification using gradient boosting decision tree

Danandeh Mehr Ali

Acta Geophysica

2021

Vol. 69, no. 3

909--918

This paper compares the classification and prediction capabilities of decision tree (DT), genetic programming (GP), and gradient boosting decision tree (GBT) techniques for one-month ahead prediction of standardized precipitation index in Ankara province and standardized precipitation evaporation index in central Antalya region. The evolved models were developed based on multi-station prediction scenarios in which observed (reanalyzed) data from nearby stations (grid points) were used to predict drought conditions in a target location. To tackle the rare occurrence of extreme dry/wet conditions, the drought series at the target location was categorized into three classes of wet, normal, and dry events. The new models were trained and validated using the frst 70% and last 30% of the datasets, respectively. The results demonstrated the promising performance of GBT for meteorological drought classification. It provides better performance than DT and GP in Ankara; however, GP predictions for Antalya were more accurate in the testing period. The results also exhibited that the proposed GP model with a scaled sigmoid function at root can efortlessly classify and predict the number of dry, normal, and wet events in both case studies.

A comparative study of empirical and ensemble machine learning algorithms in predicting air over pressure in open pit coal mine

Nguyen Hoang, Bui Xuan-Nam, Tran Quang-Hieu, Pham Van Hoa, Nguyen Dinh-An, Le Thi Thu Hoa, Le Qui-Thao, Do Ngoc-Hoan, Bao Tran Dinh, Bui Hoang-Bac, Moayedi Hossein

Acta Geophysica

2020

Vol. 68, no. 2

325--336

This study aims to take into account the feasibility of three ensemble machine learning algorithms for predicting blast-induced air over-pressure (AOp) in open-pit mine, including gradient boosting machine (GBM), random forest (RF), and Cubist. An empirical technique was also applied to predict AOp and compared with those of the ensemble models. To employ this study, 146 events of blast were investigated with 80% of the total database (approximately 118 blasting events) being used for developing the models, whereas the rest (20%~28 blasts) were used to validate the models’ accuracy. RMSE, MAE, and R2 were used as performance indices for evaluating the reliability of the models. The fndings revealed that the ensemble models yielded more precise accuracy than those of the empirical model. Of the ensemble models, the Cubist model provided better performance than those of RF and GBM models with RMSE, MAE, and R2 of 2.483, 0.976, and 0.956, respectively, whereas the RF and GBM models provided poorer accuracy with an RMSE of 2.579, 2.721; R2 of 0.953, 0.950, and MAE of 1.103, 1.498, respectively. In contrast, the empirical model was interpreted as the poorest model with an RMSE of 4.448, R2 of 0.872, and MAE of 3.719. In addition, other fndings indicated that explosive charge capacity, spacing, stemming, monitoring distance, and air humidity were the most important inputs for the AOp predictive models using artifcial intelligence.