Applying different resampling strategies in machine learning models to predict head-cut gully erosion susceptibility

Wang, Fengjie; Sahana, Mehebub; Pahlevanzadeh, Bahareh; Pal, Subodh Chandra; Shit, Pravat Kumar; Piran, Md. Jalil; Janizadeh, Saeid; Band, Shahab S.; Mosavi, Amir

doi:10.1016/j.aej.2021.04.026

Cited by 45 publications

(11 citation statements)

References 39 publications

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“…Needing at least two predictor variables to run is its disadvantage. Wang et al (2021) have used this model to apply different resampling methods to map gully erosion probability.…”

Section: Methodsmentioning

confidence: 99%

A new conceptual framework for spatial predictive modelling of land degradation in a semiarid area

et al. 2022

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Although land degradation (LD) is known as a severe environmental problem, spatial predictive modelling of this phenomenon remains a challenge. This research aimed to develop a new conceptual framework to predict LD susceptibility based on net primary production (NPP) and machine learning approaches. The annual NPP over the period 2001–2020 were obtained using MOD17A3 and the trend of NPP changes was considered to investigate the occurrence sites of LD within Qazvin Plain, in Qazvin Province, Iran, under a semiarid climate, with an area of about 9500 km2. An inventory map of LD was generated based on the LD study sites. The locations were randomly split‐sampled as training (70%) and testing (30%) datasets to evaluate the efficiency of the built models. Fifteen geo‐environmental factors were considered as LD predictive variables such as altitude, slope, land use, and temperature. Four advanced machine‐learning techniques were performed to model LD susceptibility. Finally, the predictive efficiency of the models was measured utilizing the area under the (ROC) curve Area Under the ROC Curve(AUC) and true skill as statics (TSS). The results indicated that the randomForest (RF), with the AUC = 0.81 and TSS = 0.5, showed the highest efficiency for predicting LD in the Qazvin Plain followed by boosted regression tree (BRT) with AUC = 0.76 and TSS = 0.47, support vector machine (SVM) with AUC = 0.71 and TSS = 0.39, and classification and regression tree (CART) with AUC = 0.63 and TSS = 0.31. The findings illustrated that altitude was the most influential variable within RF, BRT, and SVM while rainfall showed the most important contribution in modelling based on the CART algorithm. This study proposed a new modelling framework that is easily replicable in different contexts for the assessment of LD modelling and analysis.

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“…Needing at least two predictor variables to run is its disadvantage. Wang et al (2021) have used this model to apply different resampling methods to map gully erosion probability.…”

Section: Methodsmentioning

confidence: 99%

A new conceptual framework for spatial predictive modelling of land degradation in a semiarid area

et al. 2022

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“…The advantage of the sigmoid-fitting method is that it can estimate the posterior probabilities well while maintaining the sparsity of the SVM. This is implemented through the Libsvm library function in Matlab to complete the program [ 36 ].…”

Section: Methodsmentioning

confidence: 99%

MIFNN: Molecular Information Feature Extraction and Fusion Deep Neural Network for Screening Potential Drugs

Wang

Li²,

Zhao³

et al. 2022

CIMB

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Molecular property prediction is essential for drug screening and reducing the cost of drug discovery. Current approaches combined with deep learning for drug prediction have proven their viability. Based on the previous deep learning networks, we propose the Molecular Information Fusion Neural Network (MIFNN). The features of MIFNN are as follows: (1) we extracted directed molecular information using 1D-CNN and the Morgan fingerprint using 2D-CNN to obtain more comprehensive feature information; (2) we fused two molecular features from one-dimensional and two-dimensional space, and we used the directed message-passing method to reduce the repeated collection of information and improve efficiency; (3) we used a bidirectional long short-term memory and attention module to adjust the molecular feature information and improve classification accuracy; (4) we used the particle swarm optimization algorithm to improve the traditional support vector machine. We tested the performance of the model on eight publicly available datasets. In addition to comparing the overall classification capability with the baseline model, we conducted a series of ablation experiments to verify the optimization of different modules in the model. Compared with the baseline model, our model achieved a maximum improvement of 14% on the ToxCast dataset. The performance was very stable on most datasets. On the basis of the current experimental results, MIFNN performed better than previous models on the datasets applied in this paper.

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“…For evaluating the regression methods, we split the dataset in data for training (60%), validation (20%) and testing (20%) not random, but always all samples of a single casting together. The dataset for training and validation is extended to a 10,000 × 46 matrix by a bootstrap based sample augmentation by resampling [45][46][47][48]. 45 apparently relevant input parameters, listed in Table 2 except nodularity, were used to predict nodularity by statistical methods.…”

Section: Modelling Methodsmentioning

confidence: 99%

Artificial Intelligence Approaches to Determine Graphite Nodularity in Ductile Iron

Brait

Koppensteiner

Schindelbacher

et al. 2021

JCME

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The complex metallurgical interrelationships in the production of ductile cast iron can lead to enormous diﬀerences in graphite formation and local microstructure by small variations during production. Artiﬁcial intelligence algorithms were used to describe graphite formation, which is inﬂuenced by a variety of metallurgical parameters. Moreover, complex physical relationships in the formation of graphite morphology are also controlled by boundary conditions of processing, the eﬀect of which can hardly be assessed in everyday foundry operations. The inﬂuence of relevant input parameters can be predetermined using artiﬁcial intelligence based on conditions and patterns that occur simultaneously. By predicting the local graphite formation, measures to stabilise production were deﬁned and thereby the accuracy of structure simulations improved. In course of this work, the most important dominating variables, from initial charging to ﬁnal casting, were compiled and analysed with the help of statistical regression methods to predict the nodularity of graphite spheres. We compared the accuracy of the prediction by using Linear Regression, Gaussian Process Regression, Regression Trees, Boosted Trees, Support Vector Machines, Shallow Neural Networks and Deep Neural Networks. As input parameters we used 45 characteristics of the production process consisting of the basic information including the composition of the charge, the overheating time, the type of melting vessel, the type of the inoculant, the fading, and the solidification time. Additionally, the data of several thermal analysis, oxygen activity measurements and the final chemical analysis were included.Initial programme designs using machine learning algorithms based on neural networks achieved encouraging results. To improve the degree of accuracy, this algorithm was subsequently adapted and reﬁned for the nodularity of graphite.

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Applying different resampling strategies in machine learning models to predict head-cut gully erosion susceptibility

Cited by 45 publications

References 39 publications

A new conceptual framework for spatial predictive modelling of land degradation in a semiarid area

A new conceptual framework for spatial predictive modelling of land degradation in a semiarid area

MIFNN: Molecular Information Feature Extraction and Fusion Deep Neural Network for Screening Potential Drugs

Artificial Intelligence Approaches to Determine Graphite Nodularity in Ductile Iron

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