Article Per Year (5 Year)
p-Index From 2021 - 2026
0.444
P-Index
This Author published in this journals
All Journal Advance Sustainable Science, Engineering and Technology (ASSET)
Siva Kumar Subramaniam
Unknown Affiliation
Chemistry Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Industrial & Manufacturing Engineering

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
A Comparative Analysis of Time-Series Models of ARIMA and Prophet IoT-Based Flood Forecasting in Sungai Melaka Mazran Esro; Siva Kumar Subramaniam; Tuani Ibrahim, Ahamed Fayeez; Yogan Jaya Kumar; Siti Aisyah Anas; Sujatha Rajkumar
Advance Sustainable Science Engineering and Technology Vol. 7 No. 4 (2025): August-October
Publisher : Science and Technology Research Centre Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/asset.v7i4.1048

Abstract

Flood prediction is essential for mitigating disasters, especially in low-lying areas. This study presents an IoT-driven flood forecasting system that utilizes ARIMA and Prophet models to predict water levels in Sungai Melaka, Malaysia. Sensor data collected from an IoT-based flood observatory system was used to train and evaluate both models. Performance analysis based on RMSE and MAPE revealed that while ARIMA captures short-term trends, Prophet outperforms it with a lower MAPE of 6% and RMSE of 5, demonstrating superior accuracy and adaptability. Prophet's advantage lies in its robust seasonality handling, flexible trend adjustments, and ability to incorporate external regressors, making it more effective for real-time flood monitoring. The study also highlights Prophet’s limitations in capturing abrupt water level spikes, suggesting that integrating environmental factors such as rainfall intensity and upstream discharge could enhance predictive accuracy. The findings contribute to the development of AI-driven flood warning systems, supporting urban disaster management strategies.
Enhancing Sustainability in Linear WSNs Using DSDVTRI: A Triple-Interleaving Routing Approach for Pipeline Monitoring Kumaran, Divya Nacciar; Siva Kumar Subramaniam; Ahamed Fayeez Bin Tuani Ibrahim; Vigneswara Rao Gannapathy
Advance Sustainable Science Engineering and Technology Vol. 8 No. 2 (2026): February-April
Publisher : Science and Technology Research Centre Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/asset.v8i2.2620

Abstract

The safety and efficiency of oil and gas pipelines remain critical due to the high risks associated with leaks, pressure surges, and undetected structural damage. WSNs provide an effective solution for real-time monitoring by deploying sensor nodes along the pipeline. However, existing routing protocols such as DSDV and AODV face challenges with congestion, packet loss, and uneven energy consumption in long linear topologies. This study proposes DSDVTRI, a triple-interleaving extension of the DSDV protocol designed to improve data delivery and energy efficiency in linear pipeline networks. Simulations were performed using NS2.35 version across node counts ranging from 20 to 200. The results show that at number of nodes 100, DSDVTRI improves throughput by 14.2% and delivery ratio by 3.05% compared to DSDV, while reducing energy consumption per packet by 6.3%. These findings demonstrate that DSDVTRI enhances performance stability, making it suitable for real-time and energy-efficient pipeline monitoring applications.
Co-Authors Ahamed Fayeez bin Tuani Ibrahim Kumaran, Divya Nacciar Mazran Esro Siti Aisyah Anas Sujatha Rajkumar Tuani Ibrahim, Ahamed Fayeez Vigneswara Rao Gannapathy Yogan Jaya Kumar