Prediksi Banjir Berdasarkan Indeks Curah Hujan Menggunakan Deep Neural Network (DNN)
 Safira Alya Fafaza (Universitas Dian Nuswantoro, Semarang, Indonesia)
(*)Muhammad Syaifur Rohman  (Universitas Dian Nuswantoro, Semarang, Indonesia)
Ricardus Anggi Pramunendar (Universitas Dian Nuswantoro, Semarang, Indonesia)
Nurul Anisa Sri Winarsih (Universitas Dian Nuswantoro, Semarang, Indonesia)
Galuh Wilujeng Saraswati (Universitas Dian Nuswantoro, Semarang, Indonesia)
Filmada Ocky Saputra (Universitas Dian Nuswantoro, Semarang, Indonesia)
Danny Oka Ratmana (Universitas Dian Nuswantoro, Semarang, Indonesia)
Guruh Fajar Shidik (Universitas Dian Nuswantoro, Semarang, Indonesia)
(*) Corresponding Author Submitted: December 8, 2023; Published: January 9, 2024 |
Abstract
Floods are natural disasters that often occur and are among the most destructive because they have significant economic and social impacts. Accurate flood predictions are essential to manage risk and organize emergency response planning effectively. This research uses Deep Neural Network (DNN) to build a flood forecasting model that relies on rainfall index indicators and captures complex and ever-changing patterns obtained from rainfall index data. Using historical information from flood disaster events in Kerala, India, an analysis was conducted to assess the impact of various factors, particularly in learning rate and optimizer type, on model performance. The experimental results show that the type of optimizer is a crucial factor in determining the model's effectiveness, as shown in the ANOVA statistics with a P-value of 0.008493, much lower than the general threshold of 0.05. This is because this type of optimizer can significantly improve prediction accuracy. With the Adam optimizer type, the learning rate range is between 0.1 and 0.4, showing an accuracy level of up to 100%. However, the choice of learning rate does not significantly impact, indicating that the main emphasis on parameter adjustment should be determined accurately. Therefore, by carrying out appropriate parameter adjustments and thorough validation to find the optimal configuration that can increase accuracy in predicting flood disasters based on rainfall indices, the DNN model has the potential to become a tool that can assist in flood risk planning and management.
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References
A. Mosavi, P. Ozturk, and K. W. Chau, Flood Prediction using Machine Learning Models: Literature Review, Water (Switzerland), vol. 10, no. 11, pp. 140, 2018, doi: 10.3390/w10111536.
C. M. Annur, Ada 3 Ribu Bencana di Indonesia sampai Awal Oktober 2023, Banjir Terbanyak, databoks, 2023. https://databoks.katadata.co.id/datapublish/2023/10/03/ada-3-ribu-bencana-di-indonesia-sampai-awal-oktober-2023-banjir-terbanyak (accessed Nov. 28, 2023).
R. Handika et al., Identifying Environmental Variables in Potential Flood Hazard Areas using Machine Learning Approach at Musi Banyuasin Regency, South Sumatra, IOP Conf. Ser. Earth Environ. Sci., vol. 1201, no. 1, 2023, doi: 10.1088/1755-1315/1201/1/012037.
W. G. Bennett et al., Modelling Compound Flooding: A Case Study from Jakarta, Indonesia, Nat. Hazards, vol. 118, no. 1, pp. 277305, 2023, doi: 10.1007/s11069-023-06001-1.
S. Sankaranarayanan, M. Prabhakar, S. Satish, P. Jain, A. Ramprasad, and A. Krishnan, Flood Prediction based on Weather Parameters using Deep Learning, J. Water Clim. Chang., vol. 11, no. 4, pp. 17661783, 2020, doi: 10.2166/wcc.2019.321.
Y. Zhang, Urban Flood Disaster Prediction Based on K-means Clustering and GRU Network, Proc. - 2022 6th Annu. Int. Conf. Data Sci. Bus. Anal. ICDSBA 2022, pp. 8388, 2022, doi: 10.1109/ICDSBA57203.2022.00045.
S. Poornima and M. Pushpalatha, Prediction of Rainfall using Intensified LSTM based Recurrent Neural Network with Weighted Linear Units, Atmosphere (Basel)., vol. 10, no. 11, 2019, doi: 10.3390/atmos10110668.
S. Aftab, M. Ahmad, N. Hameed, M. S. Bashir, I. Ali, and Z. Nawaz, Rainfall Prediction in Lahore City using Data Mining Techniques, Int. J. Adv. Comput. Sci. Appl., vol. 9, no. 4, pp. 254260, 2018, doi: 10.14569/IJACSA.2018.090439.
S. Triyanto, A. Sunyoto, and M. R. Arief, Analisis Klasifikasi Bencana Banjir Berdasarkan Curah Hujan Menggunakan Algoritma Nave Bayes, JOISIE (Journal Inf. Syst. Informatics Eng., vol. 5, no. 2, pp. 109117, 2021, doi: 10.35145/joisie.v5i2.1785.
S. Naik, A. Verma, S. A. Patil, and A. Hingmire, Flood Prediction using Logistic Regression for Kerala State, Int. J. Eng. Res. Technol., vol. 9, no. 3, pp. 20202022, 2021, [Online]. Available: www.ijert.org
A. Theofilatos, C. Chen, and C. Antoniou, Comparing Machine Learning and Deep Learning Methods for Real-Time Crash Prediction, Transp. Res. Rec., vol. 2673, no. 8, pp. 169178, 2019, doi: 10.1177/0361198119841571.
V. Da Poian et al., Exploratory Data Analysis (EDA) Machine Learning Approaches for Ocean World Analog Mass Spectrometry, Front. Astron. Sp. Sci., vol. 10, no. May, pp. 117, 2023, doi: 10.3389/fspas.2023.1134141.
R. Barriga, M. Romero, H. Hassan, and D. F. Nettleton, Energy Consumption Optimization of a Fluid Bed Dryer in Pharmaceutical Manufacturing Using EDA (Exploratory Data Analysis), Sensors, vol. 23, no. 8, 2023, doi: 10.3390/s23083994.
D. Varma, A. Nehansh, and P. Swathy, Data Preprocessing Toolkit : An Approach to Automate Data Preprocessing, Interantional J. Sci. Res. Eng. Manag., vol. 07, no. 03, pp. 15, 2023, doi: 10.55041/ijsrem18270.
S. Dargan, M. Kumar, M. R. Ayyagari, and G. Kumar, A Survey of Deep Learning and Its Applications: A New Paradigm to Machine Learning, Arch. Comput. Methods Eng., vol. 27, no. 4, pp. 10711092, 2020, doi: 10.1007/s11831-019-09344-w.
D. Sharma, Deep Learning without Tears: A Simple Introduction, Resonance, vol. 25, no. 1, pp. 1532, 2020, doi: 10.1007/s12045-019-0919-9.
T. Sulistyowati, P. PURWANTO, F. Alzami, and R. A. Pramunendar, VGG16 Deep Learning Architecture Using Imbalance Data Methods For The Detection Of Apple Leaf Diseases, Monet. J. Keuang. dan Perbank., vol. 11, no. 1, pp. 4153, 2023, doi: 10.32832/moneter.v11i1.57.
N. Coudray et al., Classification and Mutation Prediction from Non-Small Cell Lung Cancer Histopathology Images Using Deep Learning, Nat. Med., vol. 24, no. 10, pp. 15591567, 2018, doi: 10.1038/s41591-018-0177-5.
S. Mozaffari, O. Y. Al-Jarrah, M. Dianati, P. Jennings, and A. Mouzakitis, Deep Learning-Based Vehicle Behavior Prediction for Autonomous Driving Applications: A Review, IEEE Trans. Intell. Transp. Syst., vol. 23, no. 1, pp. 3347, 2022, doi: 10.1109/TITS.2020.3012034.
Z. Hu, Y. Zhao, and M. Khushi, A Survey of Forex and Stock Price Prediction Using Deep Learning, Appl. Syst. Innov., vol. 4, no. 1, pp. 130, 2021, doi: 10.3390/ASI4010009.
L. Das, A. Sivaram, and V. Venkatasubramanian, Hidden Representations in Deep Neural Networks: Part 2. Regression Problems, Comput. Chem. Eng., vol. 139, p. 106895, 2020, doi: 10.1016/j.compchemeng.2020.106895.
R. Firmansyah and G. F. Shidik, Peningkatan Deep Neural Network pada Kasus Prediksi Diabetes Menggunakan PSO, Techno.com, vol. 22, no. 4, pp. 882892, 2023, doi: https://doi.org/10.33633/tc.v22i4.9209.
D. Muchlinski, Machine Learning and Deep Learning, Elgar Encycl. Technol. Polit., pp. 114118, 2022, doi: 10.4337/9781800374263.machine.learning.deep.
M. Nasiri and H. Rahmani, DENOVA: Predicting Five-Factor Model using Deep Learning based on ANOVA, J. AI Data Min., vol. 9, no. 4, pp. 451463, 2021, doi: 10.22044/JADM.2021.10471.2186.
L. Lin and E. Dobriban, What Causes The Test error? Going Beyond Bias-Variance via ANOVA, J. Mach. Learn. Res., vol. 22, pp. 183, 2021.
S. Sinsomboonthong, Performance Comparison of New Adjusted Min-Max with Decimal Scaling and Statistical Column Normalization Methods for Artificial Neural Network Classification, Int. J. Math. Math. Sci., vol. 2022, 2022, doi: 10.1155/2022/3584406.
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