Improvement of Best First Decision Trees Using Bagging and Dagging Ensembles for Flood Probability Mapping

Yariyan, Peyman; Janizadeh, Saeid; Phong, Tran Van; Nguyen, Huu Duy; Costache, Romulus; Le, Hiep Van; Pham, Binh Thai; Pradhan, Biswajeet; Tiefenbacher, John P.

doi:10.1007/s11269-020-02603-7

Cited by 133 publications

(62 citation statements)

References 35 publications

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“…These indices are determined, as shown in Equations (2) and (3):

where R 2 J demonstrates the determination of the regression coefficient in influential factors j on whole the other influential factors. A tolerance of >0.10 and variance inflation factors (VIF) > 5 illustrate a multicollinearity problem [ 47 , 48 ].…”

Section: Methodsmentioning

confidence: 99%

Comparative Analysis of Artificial Intelligence Models for Accurate Estimation of Groundwater Nitrate Concentration

Band

Janizadeh

Pal

et al. 2020

Sensors

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Prediction of the groundwater nitrate concentration is of utmost importance for pollution control and water resource management. This research aims to model the spatial groundwater nitrate concentration in the Marvdasht watershed, Iran, based on several artificial intelligence methods of support vector machine (SVM), Cubist, random forest (RF), and Bayesian artificial neural network (Baysia-ANN) machine learning models. For this purpose, 11 independent variables affecting groundwater nitrate changes include elevation, slope, plan curvature, profile curvature, rainfall, piezometric depth, distance from the river, distance from residential, Sodium (Na), Potassium (K), and topographic wetness index (TWI) in the study area were prepared. Nitrate levels were also measured in 67 wells and used as a dependent variable for modeling. Data were divided into two categories of training (70%) and testing (30%) for modeling. The evaluation criteria coefficient of determination (R2), mean absolute error (MAE), root mean square error (RMSE), and Nash–Sutcliffe efficiency (NSE) were used to evaluate the performance of the models used. The results of modeling the susceptibility of groundwater nitrate concentration showed that the RF (R2 = 0.89, RMSE = 4.24, NSE = 0.87) model is better than the other Cubist (R2 = 0.87, RMSE = 5.18, NSE = 0.81), SVM (R2 = 0.74, RMSE = 6.07, NSE = 0.74), Bayesian-ANN (R2 = 0.79, RMSE = 5.91, NSE = 0.75) models. The results of groundwater nitrate concentration zoning in the study area showed that the northern parts of the case study have the highest amount of nitrate, which is higher in these agricultural areas than in other areas. The most important cause of nitrate pollution in these areas is agriculture activities and the use of groundwater to irrigate these crops and the wells close to agricultural areas, which has led to the indiscriminate use of chemical fertilizers by irrigation or rainwater of these fertilizers is washed and penetrates groundwater and pollutes the aquifer.

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“…These indices are determined, as shown in Equations (2) and (3):

Section: Methodsmentioning

confidence: 99%

Comparative Analysis of Artificial Intelligence Models for Accurate Estimation of Groundwater Nitrate Concentration

Band

Janizadeh

Pal

et al. 2020

Sensors

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“…It should be mentioned that this institution is the official authority in Iran in the domain of hydrology and water management. The use of flood locations as points is also recommended by the high-quality results and models performances higher than 90%, that were achieved in the previous studies related to the flood susceptibility assessment (Chapi et al 2017;Choubin et al 2019;Bui et al 2019a;Ali et al 2020;Yariyan et al 2020c). Also, after obtaining data about the flood locations, using field research and regional documents, the occurrence of floods in each place was ensured.…”

Section: Flood Inventory Map (Fim)mentioning

confidence: 99%

“…The slope is the most critical factor for flooding due to its direct influence on surface runoff in watersheds. The effect of slope on the occurrence of devastating floods is related to runoff speed (Yariyan et al 2020c). So that, on slopes with high slopes, the speed of water penetration is much lower, this will increase the speed and flow of water.…”

Section: Flood Conditional Factors (Fcfs)mentioning

confidence: 99%

Flood susceptibility mapping using an improved analytic network process with statistical models

Yariyan

Avand

Abbaspour

et al. 2020

Geomatics, Natural Hazards and Risk

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105

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Flooding is a natural disaster that causes considerable damage to different sectors and severely affects economic and social activities. The city of Saqqez in Iran is susceptible to flooding due to its specific environmental characteristics. Therefore, susceptibility and vulnerability mapping are essential for comprehensive management to reduce the harmful effects of flooding. The primary purpose of this study is to combine the Analytic Network Process (ANP) decision-making method and the statistical models of Frequency Ratio (FR), Evidential Belief Function (EBF), and Ordered Weight Average (OWA) for flood susceptibility mapping in Saqqez City in Kurdistan Province, Iran. The frequency ratio method was used instead of expert opinions to weight the criteria in the ANP. The ten factors influencing flood susceptibility in the study area are slope, rainfall, slope length, topographic wetness index, slope aspect, altitude, curvature, distance from river, geology, and land use/land cover. We identified 42 flood points in the area, 70% of which was used for modelling, and the remaining 30% was used to validate the models. The Receiver Operating Characteristic (ROC) curve was used to evaluate the results. The area under the curve obtained from the ROC curve indicates a superior performance of the ANP and EBF hybrid model (ANP-EBF) with 95.1% efficiency compared to the combination of ANP and FR (ANP-FR) with 91% and ANP and OWA (ANP-OWA) with 89.6% efficiency.

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“…The five models namely ROC, AUC, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were objectively compared to characterize the most effective approach. The models were validated employing the receiver operating characteristic (ROC) curves (a graphical plot which controls the model performance in a diagnostic test) (Golkarian et al 2018) and by calculating the area under the ROC curve (AUC) for each model (Chen et al 2017;Chen et al 2019Chen et al , 2020Costache et al 2020;Yariyan et al 2020). It determines the ability of the model to correctly forecast the nonoccurrence of a predefined event (Yesilnacar and Topal 2005).…”

Section: Methods Of Validation and Accuracy Assessmentmentioning

confidence: 99%

Head-cut gully erosion susceptibility modelling based on ensemble Random Forest with oblique decision trees in Fareghan watershed, Iran

Pham

Mukherjee²,

Norouzi

et al. 2020

Geomatics, Natural Hazards and Risk

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Improvement of Best First Decision Trees Using Bagging and Dagging Ensembles for Flood Probability Mapping

Cited by 133 publications

References 35 publications

Comparative Analysis of Artificial Intelligence Models for Accurate Estimation of Groundwater Nitrate Concentration

Comparative Analysis of Artificial Intelligence Models for Accurate Estimation of Groundwater Nitrate Concentration

Flood susceptibility mapping using an improved analytic network process with statistical models

Head-cut gully erosion susceptibility modelling based on ensemble Random Forest with oblique decision trees in Fareghan watershed, Iran

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