Designing Deep-Based Learning Flood Forecast Model With ConvLSTM Hybrid Algorithm

Abstract

Efficient, robust, and accurate early flood warning is a pivotal decision support tool that can help save lives and protect the infrastructure in natural disasters. This research builds a hybrid deep learning (ConvLSTM) algorithm integrating the predictive merits of Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) Network to design and evaluate a flood forecasting model to forecast the future occurrence of flood events. Derived from precipitation dataset, the work adopts a Flood Index (), in form of a mathematical representation, to capture the gradual depletion of water resources over time, employed in a flood monitoring system to determine the duration, severity, and intensity of any flood situation. The newly designed predictive model utilizes statistically significant lagged , improved by antecedent and real-time rainfall data to forecast the next daily value. The performance of the proposed ConvLSTM model is validated against 9 different rainfall datasets in flood prone regions in Fiji which faces flood-driven devastations almost annually. The results illustrate the superiority of ConvLSTM-based flood model over the benchmark methods, all of which were tested at the 1-day, 3-day, 7-day, and the 14-day forecast horizon. For instance, the Root Mean Squared Error (RMSE) for the study sites were 0.101, 0.150, 0.211 and 0.279 for the four forecasted periods, respectively, using ConvLSTM model. For the next best model, the RMSE values were 0.105, 0.154, 0.213 and 0.282 in that same order for the four forecast horizons. In terms of the difference in model performance for individual stations, the Legate-McCabe Efficiency Index (LME) were 0.939, 0.898, 0.832 and 0.726 for the four forecast horizons, respectively. The results demonstrated practical utility of ConvLSTM in accurately forecasting and its potential use in disaster management and risk mitigation in the current phase of extreme weather events.

Efficacy of ANFIS-GOA technique in flood prediction: a case study of Mahanadi river basin in India

“…The above results indicated that performance of ANFIS-GOA in terms of RMSE and WI value is prominent compared to hybrid ANN with KNN (Kan et al 2020); ANFIS-GA, ANFIS-PSO, ANFIS-ACO (Azad et al 2018); hybrid deep learning ConvLSTM (Moishin et al 2021); FSF-ARIMA (Banihabib et al 2020) and ANFIS (Rezaeianzadeh et al 2014).…”

Cloud Affected Solar UV Prediction With Three-Phase Wavelet Hybrid Convolutional Long Short-Term Memory Network Multi-Step Forecast System

“…Having the convolutional operation embedded inside the long short-term memory (LSTM) cell, it robustly extracts statistically significant antecedent lagged inputs from the predictive variables whilst the LSTM learns from the sequentially incorporated features for low latency predictions [18], [19]. Recently, convLSTM was applied for flood index forecasts [20] and precipitation forecasts [21], and these studies illustrated the superiority of convLSTM over the benchmarked counterparts. Being an intelligent and versatile predictive model, convLSTM is highly suitable for modeling cloud-affected UVI.…”

Rad-cGAN v1.0: Radar-based precipitation nowcasting model with conditional Generative Adversarial Networks for multiple domains

“…The ConvLSTM model was shown to outperform the traditional optical flow-based precipitation nowcasting model. Recent studies have shown that the ConvLSTM model can be successfully applied to predict future radar-based precipitation (Kim et al, 2017;Moishin et al, 2021).…”

“…They showed that ConvLSTM can capture the spatiotemporal correlation between input rainfall image frames, which are recorded every 6 min across Hong Kong. Several studies have shown that the ConvLSTM architecture can be successfully applied to the precipitation nowcasting model (Kim et al, 2017;Moishin et al, 2021;Sønderby et al, 2020;Jeong et al, 2021). Although the convolution neural network (CNN) does not have a structure to conserve temporal information, Agrawal et al (2019) showed that a fully connected CNN called U-Net can make better predictions than traditional NWP models.…”