Model Prediksi Ketinggian Air Disemua Pintu Muka Air Sungai Ciliwung Dengan Menggunakan Metode Long Short-Term Memory

Moh Hasyimi; Lifatin  Nada; Feri  Zain

doi:10.31004/joecy.v5i2.3317

Authors

Moh Hasyimi Universitas Annuqayah
Lifatin Nada Universitas Annuqayah
Feri Zain Universitas Annuqayah

DOI:

https://doi.org/10.31004/joecy.v5i2.3317

Keywords:

Ciliwung, Ketinggian air, LSTM, Prediksi, Time Series

Abstract

Ketinggian air sangat besar efeknya untuk pertimbangan seperti pembangunan, kehidupan sosial, ekonomi, kesehatan, bahkan terhadap infrastrukter dan lainnya. Ketinggian air juga menjadi pertimbangan membuat keputusan berbagai hal yang akan diambil dan dilakukan. Memprediksi ketinggian permukaan air sungai Ciliwung menggunakan data time series dengan memamfaatkan mesin learning adalah solusi yang bisa diambil dengan tujuan membuat model prediksi menggunakan algoritma Long Short-Term Memory (LSTM) dengan optimasi Adam memberikan informasi 10 menit sekali selama 24 jam. Hasil menunjukkan keberhasilan dalam membuat model prediksi ketinggian permukaan air sungai Ciliwung, namun hasil juga menunjukkan bahwa satu jenis data belum bisa dijadikan acuan secara akurat. Evaluasi nilai MSE sebesar 3620.69 dengan 6.829 data latih 1467.35 untuk 2.845 data uji dengan total data 9.963. Metode ini terbukti bekerja dengan baik walaupun menggunakan data yang kurang variatif. Hasil loss dan akurasi prediksi terbukti berhasil namun tidak dianjurkan digunakan sebagai acuan utama membuat keputusan tereakit ketinggian air sungai Ciliwung.

References

Bhar, Kalyan Kumar, and Susmita Bakshi. 2020. “Application of Artificial Neural Network for Predicting Water Levels in Hooghly Estuary, India.” H2Open Journal 3 (1): 401–15.

https://doi.org/10.2166/h2oj.2020.041.

Chang, Z., Yang Zhang, and W. Chen. 2019. “Electricity Price Prediction Based on Hybrid Model of Adam Optimized LSTM Neural Network and Wavelet Transform.” Energy 187: 115804.

https://doi.org/10.1016/j.energy.2019.07.134.

Chen, Wei, Haoyuan Hong, Shaojun Li, Himan Shahabi, Yi Wang, Xiaojing Wang, and Baharin Bin Ahmad. 2019. “Flood Susceptibility Modelling Using Novel Hybrid Approach of Reduced-Error Pruning Trees with Bagging and Random Subspace Ensembles.” Journal of Hydrology 575: 864–73.

https://doi.org/10.1016/j.jhydrol.2019.05.089.

Chen, Yongping, Min Gan, Shunqi Pan, Haidong Pan, Xian Zhu, and Zhengjin Tao. 2020. “Application of Auto-Regressive (AR) Analysis to Improve Short-Term Prediction of Water Levels in the Yangtze Estuary.” Journal of Hydrology 590: 1–11.

https://doi.org/10.1016/j.jhydrol.2020.125386.

Choi, Changhyun, Jungwook Kim, Heechan Han, Daegun Han, and Hung Soo Kim. 2020. “Development of Water Level Prediction Models Using Machine Learning in Wetlands: A Case Study of Upo Wetland in South Korea.” Water (Switzerland) 12 (1): 1–18.

https://doi.org/10.3390/w12010093.

Chorong, Kim, and Kim Chung-Soo. 2021. “Analysis of AI-Based Techniques for Forecasting Water Level According to Rainfall.” Tropical Cyclone Research and Review 10 (4): 223–28.

https://doi.org/10.1016/j.tcrr.2021.12.002.

Gude, Vinayaka, Steven Corns, and Suzanna Long. 2020. “Flood Prediction and Uncertainty Estimation Using Deep Learning.” Water (Switzerland) 12 (3): 1–16.

https://doi.org/10.3390/w12030884.

Hosseiny, Hossein, Foad Nazari, Virginia Smith, and C. Nataraj. 2020. “A Framework for Modeling Flood Depth Using a Hybrid of Hydraulics and Machine Learning.” Scientific Reports 10 (1): 1–14. https://doi.org/10.1038/s41598-020-65232-5.

Karunasingha, Dulakshi Santhusitha Kumari. 2022. “Root Mean Square Error or Mean Absolute Error? Use Their Ratio as Well.” Information Sciences 585: 609–29.

https://doi.org/10.1016/j.ins.2021.11.036.

Kim, Boeun, Kyung Hwan Ryu, and Seongmin Heo. 2022. “Mean Squared Error Criterion for Model-Based Design of Experiments with Subset Selection.” Computers and Chemical Engineering 159: 107667.

https://doi.org/10.1016/j.compchemeng.2022.107667.

Kusudo, Tsumugu, Atsushi Yamamoto, Masaomi Kimura, and Yutaka Matsuno. 2022. “Development and Assessment of Water-Level Prediction Models for Small Reservoirs Using a Deep Learning Algorithm.” Water (Switzerland) 14 (1): 1–16.

https://doi.org/10.3390/w14010055.

Liu, Yu, Hao Wang, Xiaohui Lei, and Hao Wang. 2021. “Real-Time Forecasting of River Water Level in Urban Based on Radar Rainfall: A Case Study in Fuzhou City.” Journal of Hydrology 603: 1–14.

https://doi.org/10.1016/j.jhydrol.2021.126820.

Mislan, A. F.O. Gaffar, Haviluddin, and N. Puspitasari. 2018. “Water Level Prediction of Lake Cascade Mahakam Using Adaptive Neural Network Backpropagation (ANNBP).” IOP Conference Series: Earth and Environmental Science 144 (1): 1–8.

https://doi.org/10.1088/1755-1315/144/1/012009.

Nhu, Viet Ha, Himan Shahabi, Ebrahim Nohani, Ataollah Shirzadi, Nadhir Al-Ansari, Sepideh Bahrami, Shaghayegh Miraki, Marten Geertsema, and Hoang Nguyen. 2020. “Daily Water Level Prediction of Zrebar Lake (Iran): A Comparison between M5p, Random Forest, Random Tree and Reduced Error Pruning Trees Algorithms.” ISPRS International Journal of Geo-Information 9 (8): 1–19.

https://doi.org/10.3390/ijgi9080479.

Nilesh Kumar Rai, and Pradum Shukla, Rabindra Nath Shaw D. Saravanan Labh Kumar. 2022. “Chapter Ten - Rmse And Mape Analysis For Short-Term Solar Irradiance, Solar Energy, And Load Forecasting Using A Recurrent Artificial Neural Network.” Applications of AI and IOT in Renewable Energy, 181–92.

https://doi.org/10.1016/B978-0-323-91699-8.00010-3.

Pan, Mingyang, Hainan Zhou, Jiayi Cao, Yisai Liu, Jiangling Hao, Shaoxi Li, and Chi Hua Chen. 2020. “Water Level Prediction Model Based on GRU and CNN.” IEEE Access 8: 60090–100.

https://doi.org/10.1109/ACCESS.2020.2982433.

Prasad, D. Venkata Vara, Lokeswari Y. Venkataramana, P. Senthil Kumar, G. Prasannamedha, S. Harshana, S. Jahnavi Srividya, K. Harrinei, and Sravya Indraganti. 2022. “Analysis and Prediction of Water Quality Using Deep Learning and Auto Deep Learning Techniques.” Science of The Total Environment 821 (153311): 1–20.

https://doi.org/10.1016/j.scitotenv.2022.153311.

Ren, Tao, Xuefeng Liu, Jianwei Niu, Xiaohui Lei, and Zhao Zhang. 2020. “Real-Time Water Level Prediction of Cascaded Channels Based on Multilayer Perception and Recurrent Neural Network.” Journal of Hydrology 585: 1–14.

https://doi.org/10.1016/j.jhydrol.2020.124783.

Smagulova, Kamilya, and Alex Pappachen James. 2019. “A Survey on LSTM Memristive Neural Network Architectures and Applications.” European Physical Journal: Special Topics 228 (10): 2313–24.

https://doi.org/10.1140/epjst/e2019-900046-x.

Soebroto, Arief Andy, Imam Cholissodin, Randy Cahya Wihandika, Maria Tenika Frestantiya, and Ziya El Arief. 2015. “Prediksi Tinggi Muka Air (TMA) Untuk Deteksi Dini Bencana Banjir Menggunakan SVR-TVIWPSO.” Jurnal Teknologi Informasi Dan Ilmu Komputer 2 (2): 79–86.

https://doi.org/10.25126/jtiik.201522126.

Syarifa, Syifa. 2019. “Pencemaran Sungai Ciliwung Dan Dampak Buruknya Bagi Masyarakat Dki Jakarta.” Jurnal Ilmiah Pendidikan Lingkungan Dan Pembangunan 16 (02): 2–6.

Tsujikura, Hiroki, Kohji Tanaka, and Yasuto Tachikawa. 2016. “Development of a Water Surface Level Prediction Method Affected by River Mouth Sandbar Collapse.” Procedia Engineering 154: 1349–58.

https://doi.org/10.1016/j.proeng.2016.07.491.

Wang, Qi, and Wang Song. 2020. “Machine Learning-Based Water Level Prediction in Lake Erie.” Water (Switzerland) 12 (10): 1–14.

https://doi.org/10.3390/w12102654.

Wibowo, A., P. W. Wiryawan, and N. I. Nuqoyati. 2019. “Optimization of Neural Network for Cancer Micro RNA Biomarkers Classification.” Journal of Physics: Conference Series 1217 (1): 0–8.

https://doi.org/10.1088/1742-6596/1217/1/012124.

Zhang, Jin, Xiaolong Wang, Cheng Zhao, Wei Bai, Jun Shen, Yang Li, Zhisong Pan, and Yexin Duan. 2020. “Application of Cost-Sensitive LSTM in Water Level Prediction for Nuclear Reactor Pressurizer.” Nuclear Engineering and Technology 52 (7): 1429–35.

https://doi.org/10.1016/j.net.2019.12.025.

Zhao, Gang, Bo Pang, Zongxue Xu, and Liyang Xu. 2020. “A Hybrid Machine Learning Framework for Real-Time Water Level Prediction in High Sediment Load Reaches.” Journal of Hydrology 581: 1–10.

https://doi.org/10.1016/j.jhydrol.2019.124422.