Decision Tree Approach for Soil Liquefaction Assessment

Gandomi, Amir H.; Fridline, Mark; Roke, David A.

doi:10.1155/2013/346285

Cited by 41 publications

(19 citation statements)

References 23 publications

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“…The relationship between ag355 and wind forcing, for example, could be strongly non-linear and involve complex interactions that could not be explained by commonly-used statistical modeling approaches. Classification (categorical dependent variable) and regression (numerical dependent variable) trees are the modern statistical techniques for exploring and modeling such a complexity in data [45] and have been widely used in a variety of fields such as medicine [46], agriculture [47], engineering [48] and ecology [49]. Trees explain the variation of a single dependent variable corresponding to one or more explanatory variables by splitting data recursively based on the most influential independent variable.…”

Section: Discussionmentioning

confidence: 99%

Seasonal Variation of Colored Dissolved Organic Matter in Barataria Bay, Louisiana, Using Combined Landsat and Field Data

Joshi

D’Sa

2015

Remote Sensing

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Abstract:Coastal bays, such as Barataria Bay, are important transition zones between the terrigenous and marine environments that are also optically complex due to elevated amounts of particulate and dissolved constituents. Monthly field data collected over a period of 15 months in 2010 and 2011 in Barataria Bay were used to develop an empirical band ratio algorithm for the Landsat-5 TM that showed a good correlation with the Colored Dissolved Organic Matter (CDOM) absorption coefficient at 355 nm (ag355) (R 2 = 0.74). Landsatderived CDOM maps generally captured the major details of CDOM distribution and seasonal influences, suggesting the potential use of Landsat imagery to monitor biogeochemistry in coastal water environments. An investigation of the seasonal variation in ag355 conducted using Landsat-derived ag355 as well as field data suggested the strong influence of seasonality in the different regions of the bay with the marine end members (lower bay) experiencing generally low but highly variable ag355 and the freshwater end members (upper bay) experiencing high ag355 with low variability. Barataria Bay experienced a significant increase in ag355 during the freshwater release at the Davis Pond Freshwater Diversion (DPFD) following the Deep Water Horizon oil spill in 2010 and following the Mississippi River (MR) flood conditions in 2011, resulting in a weak linkage to salinity in comparison to the other seasons. Tree based statistical analysis showed the influence of high river flow conditions, high-and low-pressure systems that appeared to control ag355 by ~28%, 29% and 43% of the time duration over the study period at the marine end member just outside the bay. An analysis of CDOM variability in 2010 revealed OPEN ACCESSRemote Sens. 2015, 7 12479 the strong influence of the MR in controlling CDOM abundance in the lower bay during the high flow conditions, while strong winds associated with cold fronts significantly increase CDOM abundance in the upper bay, thus revealing the important role these events play in the CDOM dynamics of the bay.

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Section: Discussionmentioning

confidence: 99%

Seasonal Variation of Colored Dissolved Organic Matter in Barataria Bay, Louisiana, Using Combined Landsat and Field Data

Joshi

D’Sa

2015

Remote Sensing

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“…In this study, two decision-tree structures, CHAID and C&RT, were used and compared with ANN and MVR analysis. Decision-tree algorithms have been used before for solution of some geotechnical problems and gave satisfactory results [22,42,57], but have never been applied to tunnel excavation studies and not compared with ANN and MVR before. However, it should be kept in mind that there are no statistical methods which are mutually exclusive to each other.…”

Section: Statistical Prediction Model Parameters and Analysesmentioning

confidence: 99%

Prediction of tunnel wall convergences for NATM tunnels which are excavated in weak-to-fair-quality rock masses using decision-tree technique and rock mass strength parameters

Satıcı¹,

Topal

2020

SN Appl. Sci.

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Tunnel wall convergences should be predicted before the excavation and should be determined accurately in order to ensure safe and economic tunnel excavation media. For this to be possible, rock mass behaviors in the tunnel should also be estimated prior to excavation. This could be ensured by site investigation studies, numerical models and predictive statistics. In this study, the development of convergences during a tunnel excavation in Turkey, excavated by NATM technique, was evaluated by using statistical prediction techniques for weak-to-fair-quality rock masses. For this aim, actual tunnel wall convergence data and rock mass strength parameters were used. For prediction of tunnel wall convergence, multivariable regression analysis, artificial neural networks (ANNs), classification and regression tree (CHAID) and Chisquare automatic interaction detection (C&RT) methods were used and prediction results were compared to each other in terms of superiority and practicality. For this aim, 112 tunnel sections were used for prediction model and 30 different tunnel sections were used for validation. According to the obtained statistical prediction findings, it is seen that C&RT and ANN methods provide good prediction of tunnel wall convergences. However, C&RT method was found more practical in field use when compared to ANN. The results have shown that overburden thickness is the most effective parameter on tunnel convergence when compared to C rm , Φ rm , E rm , RMR and Q. However, the best way for determination of tunnel wall convergences is to use in situ measurements, but in case of the lack of in situ measurement instruments, the suggested probability-based statistical approach is proven to be very effective and practical. Ultimate convergence level for any cross section with similar geological and geotechnical parameters to those analyzed before can be predicted by using the suggested method in this study. Yet, it should be kept in mind that the findings of this study are limited with the data used in this study. Although the aim of this study is to ensure a different point of view for determination of convergences in case of lack of convergence measurements and field data, by adding up more convergence measurements and rock mass strength data to the proposed statistical method, prediction power of this method can be improved and then this method can be used as a practical tool for the prediction of tunnel convergences. Besides, the user-friendly and opento-development structure of this study can be a very useful tool for the geotechnical engineers, engineering geologist and mining engineers, if it can be developed by more field data.

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“…Chi-square Automatic Interaction Detector (CHAID) decision tree analysis is a data mining technique which can be demonstrate the relationship between split variables and related factors in homogeneous population subgroups [54]. Moreover, CHAID enables one to deal with whole variables, partition consecutive data effectively, and make decision trees by using a forward stopping or pruning rule [55,56].…”

Section: CImentioning

confidence: 99%

Predictors of Delirium Incidence, Time to Onset, and Recurrence in a Mixed Medical-surgical ICU Population: A Secondary Analysis Using Cox and CHIAD Decision Tree Modeling

Moghadam

Miller

Rahimi–Bashar

et al. 2020

Preprint

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Background: To address whether in intensive care unit (ICU) patients, which factors correlate with development of delirium (primary outcome), as well as more rapid delirium onset and recurrence (secondary outcomes).Methods: A retrospective secondary analysis of 4,200 patients was collected from two academic medical centers. Delirium was assessed with the Confusion Assessment Method for the Intensive Care Unit (CAM-ICU) in all patients. Univariate and multivariate Cox models, logistic regression analysis, and Chi-square Automatic Interaction Detector (CHAID) decision tree modeling were used to explore delirium risk factors.Results: Increased delirium risk as associated with exposed only to artificial light (AL) hazard ratio (HR) 1.84 (95% CI: 1.66-2.044, P<0.001), physical restraint application 1.11 (95% CI: 1.001-1.226, P=0.049), and high nursing care requirements (>8 hours per 8-hour shift) 1.18 (95% CI: 1.048-1.338, P=0.007). Delirium incidence was inversely associated with greater family engagement 0.092 (95% CI: 0.014-0.596, P=0.012), low staff burnout and anticipated turnover scores 0.093 (95% CI: 0.014-0.600, P=0.013), non-ICU length-of-stay (LOS)<15 days 0.725 (95% CI: 0.655-0.804, P<0.001), and ICU LOS ≤15 days 0.509 (95% CI: 0.456-0.567, P<0.001). CHAID modelling indicated that AL exposure and age <65 years conveyed a high risk of delirium incidence, whereas SOFA score ≤11, APACHE IV score >15 and natural light (NL) exposure were associated with moderate risk, and female sex were associated with low risk. More rapid time to delirium onset correlated with baseline sleep disturbance (P=0.049), high nursing care requirements (P=0.019), and prolonged ICU and non-ICU hospital LOS (P<0.001). Delirium recurrence correlated with age>65 years (HR 2.198; %95 CI: 1.101-4.388, P=0.026) and high nursing care requirements (HR 1.978, 95% CI: 1.096-3.569), with CHAID modeling identifying AL exposure (P<0.001) and age >65 years (P=0.032) as predictive variables.Conclusion: Development of ICU delirium correlated with application of physical restraints, high nursing care requirements, prolonged ICU and non-ICU LOS, exposure exclusively to AL (rather than natural), less family engagement, and greater staff burnout and anticipated turnover scores. ICU delirium occurred more rapidly in patients with baseline sleep disturbance, and recurrence correlated with presence of delirium on ICU admission, exclusive AL exposure, and high nursing care requirements.

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Decision Tree Approach for Soil Liquefaction Assessment

Cited by 41 publications

References 23 publications

Seasonal Variation of Colored Dissolved Organic Matter in Barataria Bay, Louisiana, Using Combined Landsat and Field Data

Seasonal Variation of Colored Dissolved Organic Matter in Barataria Bay, Louisiana, Using Combined Landsat and Field Data

Prediction of tunnel wall convergences for NATM tunnels which are excavated in weak-to-fair-quality rock masses using decision-tree technique and rock mass strength parameters

Predictors of Delirium Incidence, Time to Onset, and Recurrence in a Mixed Medical-surgical ICU Population: A Secondary Analysis Using Cox and CHIAD Decision Tree Modeling

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