The use of decision tree based predictive models for improving the culvert inspection process

Ce, Gao; Elzarka, Hazem

doi:10.1016/j.aei.2020.101203

Cited by 31 publications

(16 citation statements)

References 32 publications

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“…It is commonly used, easy to implement, and specifically useful for classification and prediction at the same time. Recently, the algorithm has been widely applied by researchers and practitioners for different purposes such as telemarketing for banks [36], retail banking [37], medical data [38], inventory systems [39], and customer purchasing behavior prediction [40]. In this study, the implementation of the DT algorithm is applied to the dataset collected from an e-learning management system's data predicting student performance using different assessment and behavior factors.…”

Section: Decision Treementioning

confidence: 99%

Intelligent Decision Support System for Predicting Student’s E-Learning Performance Using Ensemble Machine Learning

et al. 2021

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Electronic learning management systems provide live environments for students and faculty members to connect with their institutional online portals and perform educational activities virtually. Although modern technologies proactively support these online sessions, students’ active participation remains a challenge that has been discussed in previous research. Additionally, one concern for both parents and teachers is how to accurately measure student performance using different attributes collected during online sessions. Therefore, the research idea undertaken in this study is to understand and predict the performance of the students based on features extracted from electronic learning management systems. The dataset chosen in this study belongs to one of the learning management systems providing a number of features predicting student’s performance. The integrated machine learning model proposed in this research can be useful to make proactive and intelligent decisions according to student performance evaluated through the electronic system’s data. The proposed model consists of five traditional machine learning algorithms, which are further enhanced by applying four ensemble techniques: bagging, boosting, stacking, and voting. The overall F1 scores of the single models are as follows: DT (0.675), RF (0.777), GBT (0.714), NB (0.654), and KNN (0.664). The model performance has shown remarkable improvement using ensemble approaches. The stacking model by combining all five classifiers has outperformed and recorded the highest F1 score (0.8195) among other ensemble methods. The integration of the ML models has improved the prediction ratio and performed better than all other ensemble approaches. The proposed model can be useful for predicting student performance and helping educators to make informed decisions by proactively notifying the students.

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Section: Decision Treementioning

confidence: 99%

Intelligent Decision Support System for Predicting Student’s E-Learning Performance Using Ensemble Machine Learning

et al. 2021

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“…The obtained results are of significant importance for future planning of road culvert maintenance on the mountain road sections. The application of decision-making techniques and the CMOOP algorithm can improve the plans for culvert inspection, and consequently the frequency of road culvert maintenance [70].…”

Section: Results Ad Discussionmentioning

confidence: 99%

Mountain Road-Culvert Maintenance Algorithm

Mandić

Šešlija

Kolaković

et al. 2021

Water

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The objective of this research was to determine the probability of road overtopping occurrence for a road culvert caused by surface runoff from the upstream catchment. A hydrological–hydraulic model was used for the development of an algorithm for road culvert maintenance based on the overtopping occurrence probability (CMOOP algorithm) for small mountain catchments. The hydrological model defines the regression dependence between the runoff hydrograph peak values and the probability of occurrences, whereas the hydraulic model calculates the culvert flow capacity by including in the calculation the level of sediment that culvert is filled with. The relationship between occurrences of overtopping and peak runoff value was defined using the runoff hydrograph transformation model in the accumulation on the upstream side of the road. In addition to the calculation of overtopping occurrence probability for the existing culvert condition, the CMOOP algorithm was used to analyze the impact of rehabilitation and reconstruction works from the perspective of legally based safety criterion for road overtopping occurrence probability (SCROOP). The CMOOP algorithm was appled to 67 concrete culverts located in a mountain road section in the Republic of Serbia. The results show that the application of rehabilitation works on selected culverts will increase the percentage of culverts that satisfy SCROOP from 49.25% to 89.55%, which confirms that the accumulated stone sediment is the main reason for the SCROOP unfulfillment.

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“…Seperti pohon yang membagi titik data ke dalam kelas yang berbeda berdasarkan apakah titik data memenuhi kriteria dalam simpul yang ada. Tujuan utama Decision Tree adalah menggunakan sifat dari titik data pelatihan sebagai kriteria untuk melakukan klasifikasi (Gao & Elzarka, 2021).…”

Section: Klasifikasi Citraunclassified

Pengembangan Model Identifikasi Habitat Bentik Menggunakan Pendekatan Segmentasi Object-Based Image Analysis (OBIA) dan Algoritma Machine Learning (Studi Kasus: Pulau Pari, Kepulauan Seribu)

Purwanto¹,

Ibrahim²,

Ulfa³

et al. 2022

Jurnal Kelautan Nasional

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Identifikasi dan klasifikasi habitat bentik di perairan dangkal menggunakan citra satelit semakin berkembang. Pengembangan model dalam mengidentifikasi objek habitat bentik sangat penting untuk mengisi kebutuhan pemetaan habitat bentik dengan akurasi yang memadai. Penelitian ini bertujuan untuk mengembangkan model identifikasi habitat bentik dari data citra satelit penginderaan jauh menggunakan metode segmentasi berbasis objek dan algoritma klasifikasi machine learning. Tiga pendekatan klasifikasi digital yang digunakan adalah support vector machine (SVM), decision tree (DT) dan random forest (RF). Ketiga algoritma tersebut diterapkan pada hasil segmentasi citra berbasis objek untuk menguji akurasi dari hasil klasifikasi habitat bentik. Data yang digunakan adalah citra satelit resolusi tinggi SPOT 6 yang diakuisisi pada wilayah Gugus Pulau Pari pada tanggal 20 Mei 2020. Hasil penelitian menunjukkan pendekatan segmentasi berbasis objek membentuk pola segmen yang lebih rapat pada obyek heterogen dibandingkan dengan obyek yang relatif homogen (misalnya obyek daratan dan perairan). Algoritma machine learning yang diterapkan pada hasil segmentasi berbasis objek menghasilkan nilai akurasi keseluruhan (overall accuracy) lebih dari 70% untuk setiap algoritma, dengan rincian hasil adalah 75.83% untuk SVM, 74.17% untuk DT, dan 83.33% untuk RF. Dari ketiga algoritma machine learning yang diterapkan pada hasil segmentasi berbasis objek terlihat algoritma RF memiliki nilai akurasi yang paling baik dibandingkan dengan algoritma lainnya.

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The use of decision tree based predictive models for improving the culvert inspection process

Cited by 31 publications

References 32 publications

Intelligent Decision Support System for Predicting Student’s E-Learning Performance Using Ensemble Machine Learning

Intelligent Decision Support System for Predicting Student’s E-Learning Performance Using Ensemble Machine Learning

Mountain Road-Culvert Maintenance Algorithm

Pengembangan Model Identifikasi Habitat Bentik Menggunakan Pendekatan Segmentasi Object-Based Image Analysis (OBIA) dan Algoritma Machine Learning (Studi Kasus: Pulau Pari, Kepulauan Seribu)

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