FFM: Flood Forecasting Model Using Federated Learning

Abstract

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. INDEX TERMS hydraulic, meteorological, flood forecasting system, federated learning, feed-forward neural network.FIGURE 1. Natural hazards percentage as per occurrence in Pakistan during 2021 [6]

Enhancing Flood Resilience: Streamflow Forecasting and Inundation Modeling in Pakistan

“…Consequently, the incidence of floods has surged both in frequency and intensity. This trend is evident in Figure 1, showcasing Pakistan's elevated flood occurrence rate compared to other natural disasters [1]. The year 2021 saw floods surpassing all other calamities in South Asian countries [1], underlining the prominence of flooding as a critical issue in the region as shown in Figure 1.…”

“…This trend is evident in Figure 1, showcasing Pakistan's elevated flood occurrence rate compared to other natural disasters [1]. The year 2021 saw floods surpassing all other calamities in South Asian countries [1], underlining the prominence of flooding as a critical issue in the region as shown in Figure 1. Annually, a staggering average of USD 300 billion worth of damages and the consequential societal repercussions have spurred researchers around the globe to earnestly address the pressing issue of flooding [2].…”

Impact of climatic anomalies and reservoir induced seismicity on earthquake generation using Federated Learning

“…[30] focused on focal mechanisms and studied the characteristics of stress field caused by reservoir induced seismicity. [31] applied ensembling approach to characterize induced seismicity. [32] presented a framework to model surface deformation and forecast induced seismicity for reservoir operations.…”

A Distributed Approach to Meteorological Predictions: Addressing Data Imbalance in Precipitation Prediction Models through Federated Learning and GANs

“…Their model improves scalability and ensures privacy, which is essential for users of farming devices. Farooq et al [32] proposed a federated learning model using Long Short-term Memory (LSTM) neural networks to predict flood, outperforming traditional LSTM models. In this work, we propose using a deep imbalanced learning model to classify weather data stored in 9 weather stations across Australia.…”