Predicting Roadblock Occurrences Using Machine Learning with AHP for Feature Prioritization and Confusion Matrix Evaluation

Abstract

Roadblocks in Bhutan are common and significant challenges that impact transportation, public safety, and the economy. Predicting these roadblocks is difficult because of the complex interplay between geological, climatic, and topographical factors. This research proposes to develop a predictive model using Adaptive Neuro-Fuzzy Inference System (ANFIS) to create a roadblock susceptibility map of Bhutan. The study incorporates fourteen influencing factors such as rainfall, soil type, elevation, slope, aspect, settlement area, profile curvature, plane curvature, distance to rivers, distance to fault, topographic position index (TPI), Topographic Wetness Index (TWI), Terrain Ruggedness Index (TRI) and Normalized Difference Vegetation Index (NDVI), with roadblock inventory data. These factors were processed using Quantum Geographic Information System (QGIS) to build a geodatabase. The data are split into 70% for training the ANFIS model and 30% for validating the results. The ANFIS model incorporates the neural networks and fuzzy logic principles and it gives better predictions. The performance accuracy, evaluated using the confusion matrix, was 0.8408, indicating good predictive ability. The Analytical Hierarchy Process (AHP) was used to determine the relative weight of each factor, which was then applied in the Weighted Over Method (WOM) to create a susceptibility map. This map, generated for both the entire country and individual districts, can aid in mitigation efforts and serve as a preliminary tool for future infrastructure planning.

Publication DOI: https://doi.org/10.13189/cea.2025.131302
Divisions: College of Business and Social Sciences > Aston Business School
Aston University (General)
Funding Information: The research is partially funded for data collection by ESRC Impact Accelerator Fund managed by Aston University.
Additional Information: Copyright ©2025 by authors, all rights reserved. Authors agree that this article remains permanently open access under the terms of the Creative Commons Attribution License 4.0 International License
Publication ISSN: 2332-1121
Last Modified: 04 Jun 2025 07:23
Date Deposited: 03 Jun 2025 08:05
Full Text Link:
Related URLs: https://www.hrp ... ?id=48&iid=2319 (Publisher URL)
PURE Output Type: Article
Published Date: 2025-06
Accepted Date: 2025-05-21
Authors: Chettri, Nimesh
Aryal, Komal Raj (ORCID Profile 0000-0001-9980-4516)
Wangmo, Ugyen Pelden
Tshering, Karma
Penjor, Tshering
Dema, Yeshi
Norbu, Rigzin
Dema, Karma

Download

[img]

Version: Published Version

License: Creative Commons Attribution


Export / Share Citation


Statistics

Additional statistics for this record