Application of Support Vector Machine, Random Forest, and Genetic Algorithm Optimized Random Forest Models in Groundwater Potential Mapping

Naghibi, Seyed Amir; Ahmadi, Kourosh; Daneshi, Alireza

doi:10.1007/s11269-017-1660-3

Cited by 341 publications

(125 citation statements)

References 31 publications

Supporting

Mentioning

121

Contrasting

Unclassified

Order By: Relevance

“…Under the bootstrapping method, the data during the training phase are selected randomly and independently to develop an RF model, and the data that are not involved in the selection process are named "out-of-bag" [39]. The capacity of the random forest has been approved by several engineering problems such as [40,41]. During this process, the out-of-bag data are changed and the prediction error is measured to estimate the importance of input variables [41,42].…”

Section: Random Forest Modelmentioning

confidence: 99%

Prediction of Risk Delay in Construction Projects Using a Hybrid Artificial Intelligence Model

Yaseen‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬

Ali

Salih

et al. 2020

Sustainability

View full text Add to dashboard Cite

Project delays are the major problems tackled by the construction sector owing to the associated complexity and uncertainty in the construction activities. Artificial Intelligence (AI) models have evidenced their capacity to solve dynamic, uncertain and complex tasks. The aim of this current study is to develop a hybrid artificial intelligence model called integrative Random Forest classifier with Genetic Algorithm optimization (RF-GA) for delay problem prediction. At first, related sources and factors of delay problems are identified. A questionnaire is adopted to quantify the impact of delay sources on project performance. The developed hybrid model is trained using the collected data of the previous construction projects. The proposed RF-GA is validated against the classical version of an RF model using statistical performance measure indices. The achieved results of the developed hybrid RF-GA model revealed a good resultant performance in terms of accuracy, kappa and classification error. Based on the measured accuracy, kappa and classification error, RF-GA attained 91.67%, 87% and 8.33%, respectively. Overall, the proposed methodology indicated a robust and reliable technique for project delay prediction that is contributing to the construction project management monitoring and sustainability.Sustainability 2020, 12, 1514 2 of 14 in terms of litigation, dispute and arbitration [5]. Delays are caused by many sources and factors such as the owner [6,7], designer [3,8], contractor [4,7], materials [4,7], project [7,8], labor [9] and external factors [3,10]. Literature ReviewThe prediction of project delay based on internal and external sources can help project managers to provide an accurate forecast of the project schedule, and this can assist a proactive management approach in the construction project [11]. Construction projects are dynamic and complex, included a huge number of project stockholders, feedback processes and non-linear relationships [12]. The existence of a delay problem is related to interdependent factors that affect the construction project and the complexity and uncertainty of construction activities. Thus, providing of an efficient tool for analyzing delay factors is key for estimating an accurate duration in construction projects [11].By recalling previous studies, Chan (2001) used regression analysis to identify time-cost relationships for building projects in Malaysia [2]. This approach was developed for managers and owners to estimate the average time that is required for project delivery. Chan and Chan (2004) performed multiple regression exercises to analyze data related to the time performance of construction projects [13]. The results indicated that multiple regression was used as a useful method to predict time performance in construction projects. Rezaie et al. (2007) used Monte Carlo analysis to investigate the effects of uncertainties on the schedule performance [14]. The results revealed that this method is a good tool to simulate the relationship of uncertainties of construct...

show abstract

Section: Random Forest Modelmentioning

confidence: 99%

Prediction of Risk Delay in Construction Projects Using a Hybrid Artificial Intelligence Model

Yaseen‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬

Ali

Salih

et al. 2020

Sustainability

View full text Add to dashboard Cite

show abstract

“…Many researchers recognized that the occurrence of landslides and forest fires is influenced by various aspects that involve human activities and climate conditions 11,12 . Several methods for spatially modelling landslides and forest fires have been developed 13,14 .…”

mentioning

confidence: 99%

“…It produces an accurate classifier with an internal unbiased estimate of generalizability during the forest building processes 25 . It makes no statistical assumptions, and it is characterized by high prediction performance 13,26 .…”

mentioning

confidence: 99%

Assessing and mapping multi-hazard risk susceptibility using a machine learning technique

Pourghasemi

Kariminejad

Amiri

et al. 2020

Sci Rep

141

View full text Add to dashboard Cite

the aim of the current study was to suggest a multi-hazard probability assessment in fars province, Shiraz City, and its four strategic watersheds. At first, we construct maps depicting the most effective factors on floods (12 factors), forest fires (10 factors), and landslides (10 factors), and used the Boruta algorithm to prioritize the impact of each respective factor on the occurrence of each hazard. Subsequently, flood, landslides, and forest fire susceptibility maps prepared using a Random Forest (RF) model in the R statistical software. Results indicate that 42.83% of the study area are not susceptible to any hazards, while 2.67% of the area is at risk of all three hazards. The results of the multi-hazard map in Shiraz City indicate that 25% of Shiraz city is very susceptible to flooding, while 16% is very susceptible to landslide occurrences. for four strategic watersheds, it is notable that in the Dorodzan Watershed, landslides and floods are the most important hazards; whereas, flood occurrences cover the largest area of the Maharlou Watershed. In contrast, the Tashk-Bakhtegan Watershed is so sensible to floods and landslides, respectively. Finally, in the Ghareaghaj Watershed, forest fire ranks as the strongest hazard, followed by floods. The validation results indicate an AUC of 0.834, 0.939, and 0.943 for the flood, landslide, and forest fire susceptibility maps, respectively. Also, other accuracy measures including, specificity, sensitivity, TSS, CCI, and Gini coefficient confirmed results of the AUC values. These results allow us to forecast the spatial behavior of such multi-hazard events, and researchers and stakeholders alike can apply them to evaluate hazards under various mitigation scenarios.The Sendai Framework, with its comprehensive vision, recommends more efforts to decrease disaster risk and increase sustainable development. Especially communities who are increasingly susceptible to natural hazards should adhere to these guidelines and plan accordingly. In this regard, the multi-hazard approach is often used in risk reduction projects and studies addressing risks associated with human activities or climate change on a regional and local scale 1 . It is obvious that introducing a universal set of multi-hazard assessment techniques is of fundamental importance for reducing disaster risk, and constitutes a valuable asset to share with other stakeholders, including the private sectors, local governments, and other stakeholders.The use of the term multi-hazard in the current research is related to the objective of risk reduction among natural hazards, including flood, landslides, and forest fires, in a specified spatial distribution in this study 2,3 . Recently, susceptibility modeling approaches related to single processes have advanced considerably for river floods 4 and landslides 5-7 . However, there is still neither a common terminology nor a uniform conceptual approach for analyzing multiple hazards in conjunction. This is not unexpected because multi-hazard analyses are not the sum of ...

show abstract

“…Nevertheless, RF have the same limitation of NN and SVM since they cannot be directly interpreted, thus favoring accuracy over interpretability. Considering that recent studies in different contexts have shown RF outperform techniques such as neural networks and support vector machines (e.g., Liu et al 2013;Naghibi et al 2017), we have also adopted RF for comparison purposes.…”

Section: Modelingmentioning

confidence: 99%

Unfolding the characteristics of incentivized online reviews

Costa

Guerreiro

Moro

et al. 2019

Journal of Retailing and Consumer Services

View full text Add to dashboard Cite

Application of Support Vector Machine, Random Forest, and Genetic Algorithm Optimized Random Forest Models in Groundwater Potential Mapping

Cited by 341 publications

References 31 publications

Prediction of Risk Delay in Construction Projects Using a Hybrid Artificial Intelligence Model

Prediction of Risk Delay in Construction Projects Using a Hybrid Artificial Intelligence Model

Assessing and mapping multi-hazard risk susceptibility using a machine learning technique

Unfolding the characteristics of incentivized online reviews

Contact Info

Product

Resources

About