Flash Flood Susceptibility Modeling Using New Approaches of Hybrid and Ensemble Tree-Based Machine Learning Algorithms

Band, Shahab S.; Janizadeh, Saeid; Pal, Subodh Chandra; Saha, Asish; Chakrabortty, Rabin; Melesse, Assefa M.; Mosavi, Amir

doi:10.3390/rs12213568

Cited by 138 publications

(50 citation statements)

References 73 publications

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“…It means that more machine learning algorithms should be incorporated by the stacking regression method. Therefore, deep learning-based regression methods such as multilayer perceptron (MLP) [75] can be used as a new algorithm in the stacking procedure, while multiple birth SVM regression [76] and parallel RF regression [77] can be respectively used as variants of the SVM and RF regression for further improvement of the model. In addition, stacking a large number of base learners requires a level-2 model to perform the multicollinearity data analysis in the model [78].…”

Section: Discussionmentioning

confidence: 99%

Assessment of Ensemble Learning to Predict Wheat Grain Yield Based on UAV-Multispectral Reflectance

Fei

Hassan

et al. 2021

Remote Sensing

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Grain yield is increasingly affected by climate factors such as drought and heat. To develop resilient and high-yielding cultivars, high-throughput phenotyping (HTP) techniques are essential for precise decisions in wheat breeding. The ability of unmanned aerial vehicle (UAV)-based multispectral imaging and ensemble learning methods to increase the accuracy of grain yield prediction in practical breeding work is evaluated in this study. For this, 211 winter wheat genotypes were planted under full and limited irrigation treatments, and multispectral data were collected at heading, flowering, early grain filling (EGF), and mid-grain filling (MGF) stages. Twenty multispectral vegetation indices (VIs) were estimated, and VIs with heritability greater than 0.5 were selected to evaluate the models across the growth stages under both irrigation treatments. A framework for ensemble learning was developed by combining multiple base models such as random forest (RF), support vector machine (SVM), Gaussian process (GP), and ridge regression (RR). The R2 values between VIs and grain yield between for individual base models were ranged from 0.468 to 0.580 and 0.537 to 0.598 for grain yield prediction in full and limited irrigation treatments across growth stages, respectively. The prediction results of ensemble models were ranged from 0.491 to 0.616 and 0.560 to 0.616 under full and limited irrigation treatments respectively, and were higher than that of the corresponding base learners. Moreover, the grain yield prediction results were observed high at mid grain filling stage under both full (R2 = 0.625) and limited (R2 = 0.628) irrigation treatments through ensemble learning based stacking of four base learners. Further improvements in ensemble learning models can accelerate the use of UAV-based multispectral data for accurate predictions of complex traits like grain yield in wheat.

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Section: Discussionmentioning

confidence: 99%

Assessment of Ensemble Learning to Predict Wheat Grain Yield Based on UAV-Multispectral Reflectance

Fei

Hassan

et al. 2021

Remote Sensing

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“…Therefore, first we have extracting the values of gully and non-gully points from several conditioning factors used in this study and finally VIF and TOL values were calculated by using SPSS 16 package. The following equations were used to calculate TOL and VIF (Arabameri, Karimi-Sangchini, et al 2020;Band, Janizadeh, Chandra Pal, Saha, Chakrabortty, Melesse, et al 2020).…”

Section: Multicollinearity (Mc) Diagnosis and Feature Selectionmentioning

confidence: 99%

Prediction of gully erosion susceptibility mapping using novel ensemble machine learning algorithms

Arabameri

Pal

Costache

et al. 2021

Geomatics, Natural Hazards and Risk

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Spatial modelling of gully erosion at regional level is very relevant for local authorities to establish successful counter-measures and to change land-use planning. This work is exploring and researching the potential of a genetic algorithm-extreme gradient boosting (GE-XGBoost) hybrid computer education solution for spatial mapping of the susceptibility of gully erosion. The new machine learning approach is to combine the extreme gradient boosting machine (XGBoost) and the genetic algorithm (GA). The GA metaheuristic is being used to improve the efficiency of the XGBoost classification approach. A GIS database has been developed that contains recorded instances of gully erosion incidents and 18 conditioning variables. These parameters are used as predictive variables used to assess the condition of non-erosion or erosion in a given region within the Kohpayeh-Sagzi River Watershed research area in Iran. Exploratory results indicate that the proposed GE-XGBoost model is superior to the other benchmark solution with the desired predictive precision (89.56%). Therefore, the newly built model may be a promising method for large-scale mapping of gully erosion susceptibility.

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“…In this research, the erosion potentiality in watershed scale has been estimated with the help of evidential belief function (EBF), spatial logistic regression (SLR) and ensemble of EBF and SLR. The ensemble approach is more optimistic regarding the perdition of different environmental hards that already established by different researchers (Arabameri et al, 2020;Band et al, 2020aBand et al, , 2020b, so we considered this ensemble approach for estimation of erosion potentiality.…”

Section: Approaches For Estimating Erosion Potentialitymentioning

confidence: 99%

Water-induced erosion potentiality and vulnerability assessment in Kangsabati river basin, eastern India

Chakrabortty

Pal

Arabameri

et al. 2021

Environ Dev Sustain

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The large-scale water-induced erosion is one of the most determining elements on land degradation in subtropical monsoon-dominated region. From this large-scale erosion, the fertility of the agricultural land has been decline consequently. So, estimation of the amount of erosion and its accurate prediction is necessary for escaping from this hazardous situation. In this study, the application of evidential belief function (EBF), spatial logistic regression (SLR) and ensemble of EBF and SLR to estimate the erosion potentiality with the help of ArcGIS and Soil and water assessment tool (SWAT). The average annual soil erosion has been estimated with the help of revised universal soil loss equation (RUSLE) and geographical information system (GIS). Apart from this to evaluate the importance of morphotectonic parameters on soil erosion, the correlation between erosion potentiality and average annual soil erosion has been quantified. In large-scale erosion, there is a direct impact of storm rainfall event in monsoon period in the entire subtropical region. Here, in erosion potentiality assessment, the optimal capacity of ensemble EBF-SLR is higher than the single alone methods, i.e., EBF and SLR. So, the mentioned approaches can be applied in soil erosion research in subtropical environment with considering the erosion causal parameters. This type of information can be helpful to the decision-maker and stakeholders to take proper initiative to reducing the rate of erosion. The main task of the future researcher is to implement this method more accurate ways with considering more reliable variables and slight modifications of the approaches in keeping in the view of regional environment.

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Flash Flood Susceptibility Modeling Using New Approaches of Hybrid and Ensemble Tree-Based Machine Learning Algorithms

Cited by 138 publications

References 73 publications

Assessment of Ensemble Learning to Predict Wheat Grain Yield Based on UAV-Multispectral Reflectance

Assessment of Ensemble Learning to Predict Wheat Grain Yield Based on UAV-Multispectral Reflectance

Prediction of gully erosion susceptibility mapping using novel ensemble machine learning algorithms

Water-induced erosion potentiality and vulnerability assessment in Kangsabati river basin, eastern India

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