Property Prediction of Organic Donor Molecules for Photovoltaic Applications Using Extremely Randomized Trees

Paul, Arindam; Furmanchuk, Al’ona; Liao, Wei; Choudhary, Alok; Agrawal, Ankit

doi:10.1002/minf.201900038

Cited by 33 publications

(24 citation statements)

References 48 publications

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“…It has the advantages of RF and BDT and seems to be suitable for classification of objects with complex heterogeneity [39]. As a typical classification and regression method, the ERT model is mainly applied to medical science, biology, and atmospheric science [40][41][42]. At present, the model has not been used for the classification of Earth surface features.…”

Section: Introductionmentioning

confidence: 99%

Extracting Information on Rocky Desertification from Satellite Images: A Comparative Study

Xiao-qing

Dong

et al. 2021

Remote Sensing

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Rocky desertification occurs in many karst terrains of the world and poses major challenges for regional sustainable development. Remotely sensed data can provide important information on rocky desertification. In this study, three common open-access satellite image datasets (Sentinel-2B, Landsat-8, and Gaofen-6) were used for extracting information on rocky desertification in a typical karst region (Guangnan County, Yunnan) of southwest China, using three machine-learning algorithms implemented in the Python programming language: random forest (RF), bagged decision tree (BDT), and extremely randomized trees (ERT). Comparative analyses of the three data sources and three algorithms show that: (1) The Sentinel-2B image has the best capability for extracting rocky desertification information, with an overall accuracy (OA) of 85.21% using the ERT method. This can be attributed to the higher spatial resolution of the Sentinel-2B image than that of Landsat-8 and Gaofen-6 images and Gaofen-6’s lack of the shortwave infrared (SWIR) bands suitable for mapping carbonate rocks. (2) The ERT method has the best classification results of rocky desertification. Compared with the RF and BDT methods, the ERT method has stronger randomness in modeling and can effectively identify important feature factors for extracting information on rocky desertification. (3) The combination of the Sentinel-2B images and the ERT method provides an effective, efficient, and free approach to information extraction for mapping rocky desertification. The study can provide a useful reference for effective mapping of rocky desertification in similar karst environments of the world, in terms of both satellite image sources and classification algorithms. It also provides important information on the total area and spatial distribution of different levels of rocky desertification in the study area to support decision making by local governments for sustainable development.

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Section: Introductionmentioning

confidence: 99%

Extracting Information on Rocky Desertification from Satellite Images: A Comparative Study

Xiao-qing

Dong

et al. 2021

Remote Sensing

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“…The results of the AUC curve, MSE criteria, and the number of eroded points in high and very high susceptibility classes indicate that the RF model had a better performance than the SVM and Adaboost models. The ability and accuracy of the RF model in mapping soil erosion susceptibility is mentioned in other studies [86,87]. The three models show different results when it comes to identifying the extent of high and very high soil erosion susceptibilities.…”

Section: Model Outputs and Performancementioning

confidence: 92%

Soil Erosion Susceptibility Mapping in Kozetopraghi Catchment, Iran: A Mixed Approach Using Rainfall Simulator and Data Mining Techniques

Ouri

Golshan

Janizadeh

et al. 2020

Land

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Soil erosion determines landforms, soil formation and distribution, soil fertility, and land degradation processes. In arid and semiarid ecosystems, soil erosion is a key process to understand, foresee, and prevent desertification. Addressing soil erosion throughout watersheds scales requires basic information to develop soil erosion control strategies and to reduce land degradation. To assess and remediate the non-sustainable soil erosion rates, restoration programs benefit from the knowledge of the spatial distribution of the soil losses to develop maps of soil erosion. This study presents Support Vector Machine (SVM), Random Forest (RF), and adaptive boosting (AdaBoost) data mining models to map soil erosion susceptibility in Kozetopraghi watershed, Iran. A soil erosion inventory map was prepared from field rainfall simulation experiments on 174 randomly selected points along the Kozetopraghi watershed. In previous studies, this map has been prepared using indirect methods such as the Universal Soil Loss Equation to assess soil erosion. Direct field measurements for mapping soil erosion susceptibility have so far not been carried out in our study site in the past. The soil erosion rate data generated by simulated rainfall in 1 m2 plots at rainfall rate of 40 mmh−1 was used to develop the soil erosion map. Of the available data, 70% and 30% were randomly classified to calibrate and validate the models, respectively. As a result, the RF model with the highest area under the curve (AUC) value in a receiver operating characteristics (ROC) curve (0.91), and the lowest mean square error (MSE) value (0.09), has the most concordance and spatial differentiation. Sensitivity analysis by Jackknife and IncNodePurity methods indicates that the slope angle is the most important factor within the soil erosion susceptibility map. The RF susceptibility map showed that the areas located in the center and near the watershed outlet have the most susceptibility to soil erosion. This information can be used to support the development of sustainable restoration plans with more accuracy. Our methodology has been evaluated and can be also applied in other regions.

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“…Guertz et al proposed ERT method in 2006 [44], which has been widely applied in various fields [45][46][47][48][49][50][51]. A brief description of how we implemented ERT in this study has been described previously [52,53].…”

Section: Extremely Randomized Treementioning

confidence: 99%

Extremely-randomized-tree-based Prediction of N6-methyladenosine Sites inSaccharomyces cerevisiae

Govindaraj¹,

Subramaniyam

Manavalan

2020

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Introduction: N6-methyladenosine (m6A) is one of the most common post-transcriptional modifications in RNA, which has been related to several biological processes. The accurate prediction of m6A sites from RNA sequences is one of the challenging tasks in computational biology. Several computational methods utilizing machine-learning algorithms have been proposed that accelerate in silico screening of m6A sites, thereby drastically reducing the experimental time and labor costs involved. Methodology: In this study, we proposed a novel computational predictor termed ERT-m6Apred, for the accurate prediction of m6A sites. To identify the feature encodings with more discriminative capability, we applied a two-step feature selection technique on seven different feature encodings and identified the corresponding optimal feature set. Results: Subsequently, performance comparison of the corresponding optimal feature set-based extremely randomized tree model revealed that Pseudo k-tuple composition encoding, which includes 14 physicochemical properties significantly outperformed other encodings. Moreover, ERT-m6Apred achieved an accuracy of 78.84% during cross-validation analysis, which is comparatively better than recently reported predictors. Conclusion: In summary, ERT-m6Apred predicts Saccharomyces cerevisiae m6A sites with higher accuracy, thus facilitating biological hypothesis generation and experimental validations.

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Property Prediction of Organic Donor Molecules for Photovoltaic Applications Using Extremely Randomized Trees

Cited by 33 publications

References 48 publications

Extracting Information on Rocky Desertification from Satellite Images: A Comparative Study

Extracting Information on Rocky Desertification from Satellite Images: A Comparative Study

Soil Erosion Susceptibility Mapping in Kozetopraghi Catchment, Iran: A Mixed Approach Using Rainfall Simulator and Data Mining Techniques

Extremely-randomized-tree-based Prediction of N6-methyladenosine Sites inSaccharomyces cerevisiae

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