FFM: Flood Forecasting Model Using Federated Learning
Floods are one of the most common natural disasters that occur frequently causing massive damage to property, agriculture, economy and life. Flood prediction offers a huge challenge for researchers struggling to predict floods since long time. In this article, flood forecasting model using federated...
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| Format: | Article |
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IEEE
2023-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10058950/ |
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| author | Muhammad Shoaib Farooq Rabia Tehseen Junaid Nasir Qureshi Uzma Omer Rimsha Yaqoob Hafiz Abdullah Tanweer Zabihullah Atal |
| author_facet | Muhammad Shoaib Farooq Rabia Tehseen Junaid Nasir Qureshi Uzma Omer Rimsha Yaqoob Hafiz Abdullah Tanweer Zabihullah Atal |
| author_sort | Muhammad Shoaib Farooq |
| collection | DOAJ |
| description | Floods are one of the most common natural disasters that occur frequently causing massive damage to property, agriculture, economy and life. Flood prediction offers a huge challenge for researchers struggling to predict floods since long time. In this article, flood forecasting model using federated learning technique has been proposed. Federated Learning is the most advanced technique of machine learning (ML) that guarantees data privacy, ensures data availability, promises data security, and handles network latency trials inherent in prediction of floods by prohibiting data to be transferred over the network for model training. Federated Learning technique urges for onsite training of local data models, and focuses on transmission of these local models on the network instead of sending huge data set towards central server for local model aggregation and training of global data model at the central server. In this article, the proposed model integrates locally trained models of eighteen clients, investigates at which station flooding is about to happen and generates flood alert towards a specific client with five days lead time. A local feed forward neural network (FFNN) model is trained at the client station where the flood has been expected. Flood forecasting module of local FFNN model predicts the expected water level by taking multiple regional parameters as input. The dataset of five different rivers and barrages has been collected from 2015 to 2021 considering four aspects including snow melting, rainfall-runoff, flow routing and hydrodynamics. The proposed flood forecasting model has successfully predicted previous floods happened in the selected zone during 2010 to 2015 with 84 % accuracy. |
| format | Article |
| id | doaj-art-0178271e303b4f09af464ddd7011dd5b |
| institution | OA Journals |
| issn | 2169-3536 |
| language | English |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-0178271e303b4f09af464ddd7011dd5b2025-08-20T02:33:55ZengIEEEIEEE Access2169-35362023-01-0111244722448310.1109/ACCESS.2023.325289610058950FFM: Flood Forecasting Model Using Federated LearningMuhammad Shoaib Farooq0https://orcid.org/0000-0002-4095-8868Rabia Tehseen1Junaid Nasir Qureshi2https://orcid.org/0000-0001-8508-6912Uzma Omer3https://orcid.org/0000-0002-8479-026XRimsha Yaqoob4Hafiz Abdullah Tanweer5Zabihullah Atal6https://orcid.org/0000-0001-7502-5534Department of Computer Science, University of Management and Technology, Lahore, PakistanDepartment of Computer Science, University of Central Punjab, Lahore, PakistanDepartment of Computer Science, Bahria University Lahore Campus, Lahore, PakistanDepartment of Computer Science, University of Education, Lahore, PakistanInstitute of Electrical, Electronics and Computer Engineering, University of the Punjab, Lahore, PakistanDepartment of Computer Science, University of Management and Technology, Lahore, PakistanDepartment of Computer Science, Kardan University, Kabul, AfghanistanFloods are one of the most common natural disasters that occur frequently causing massive damage to property, agriculture, economy and life. Flood prediction offers a huge challenge for researchers struggling to predict floods since long time. In this article, flood forecasting model using federated learning technique has been proposed. Federated Learning is the most advanced technique of machine learning (ML) that guarantees data privacy, ensures data availability, promises data security, and handles network latency trials inherent in prediction of floods by prohibiting data to be transferred over the network for model training. Federated Learning technique urges for onsite training of local data models, and focuses on transmission of these local models on the network instead of sending huge data set towards central server for local model aggregation and training of global data model at the central server. In this article, the proposed model integrates locally trained models of eighteen clients, investigates at which station flooding is about to happen and generates flood alert towards a specific client with five days lead time. A local feed forward neural network (FFNN) model is trained at the client station where the flood has been expected. Flood forecasting module of local FFNN model predicts the expected water level by taking multiple regional parameters as input. The dataset of five different rivers and barrages has been collected from 2015 to 2021 considering four aspects including snow melting, rainfall-runoff, flow routing and hydrodynamics. The proposed flood forecasting model has successfully predicted previous floods happened in the selected zone during 2010 to 2015 with 84 % accuracy.https://ieeexplore.ieee.org/document/10058950/Hydraulicmeteorologicalflood forecasting systemfederated learningfeed-forward neural network |
| spellingShingle | Muhammad Shoaib Farooq Rabia Tehseen Junaid Nasir Qureshi Uzma Omer Rimsha Yaqoob Hafiz Abdullah Tanweer Zabihullah Atal FFM: Flood Forecasting Model Using Federated Learning IEEE Access Hydraulic meteorological flood forecasting system federated learning feed-forward neural network |
| title | FFM: Flood Forecasting Model Using Federated Learning |
| title_full | FFM: Flood Forecasting Model Using Federated Learning |
| title_fullStr | FFM: Flood Forecasting Model Using Federated Learning |
| title_full_unstemmed | FFM: Flood Forecasting Model Using Federated Learning |
| title_short | FFM: Flood Forecasting Model Using Federated Learning |
| title_sort | ffm flood forecasting model using federated learning |
| topic | Hydraulic meteorological flood forecasting system federated learning feed-forward neural network |
| url | https://ieeexplore.ieee.org/document/10058950/ |
| work_keys_str_mv | AT muhammadshoaibfarooq ffmfloodforecastingmodelusingfederatedlearning AT rabiatehseen ffmfloodforecastingmodelusingfederatedlearning AT junaidnasirqureshi ffmfloodforecastingmodelusingfederatedlearning AT uzmaomer ffmfloodforecastingmodelusingfederatedlearning AT rimshayaqoob ffmfloodforecastingmodelusingfederatedlearning AT hafizabdullahtanweer ffmfloodforecastingmodelusingfederatedlearning AT zabihullahatal ffmfloodforecastingmodelusingfederatedlearning |