Flood Stage Forecasting Using Machine-Learning Methods: A Case Study on the Parma River (Italy)

Abstract

Real-time river flood forecasting models can be useful for issuing flood alerts and reducing or preventing inundations. To this end, machine-learning (ML) methods are becoming increasingly popular thanks to their low computational requirements and to their reliance on observed data only. This work aimed to evaluate the ML models’ capability of predicting flood stages at a critical gauge station, using mainly upstream stage observations, though downstream levels should also be included to consider backwater, if present. The case study selected for this analysis was the lower stretch of the Parma River (Italy), and the forecast horizon was extended up to 9 h. The performances of three ML algorithms, namely Support Vector Regression (SVR), MultiLayer Perceptron (MLP), and Long Short-term Memory (LSTM), were compared herein in terms of accuracy and computational time. Up to 6 h ahead, all models provided sufficiently accurate predictions for practical purposes (e.g., Root Mean Square Error < 15 cm, and Nash-Sutcliffe Efficiency coefficient > 0.99), while peak levels were poorly predicted for longer lead times. Moreover, the results suggest that the LSTM model, despite requiring the longest training time, is the most robust and accurate in predicting peak values, and it should be preferred for setting up an operational forecasting system.

Spatial prediction of flood-susceptible zones in the Ourika watershed of Morocco using machine learning algorithms

“…Overall, the 16 models were consistent in predicting 53.79% and 0.11% of the watershed as belonging to the very low and very high flood risk classes, respectively, while inconsistencies were observed for the remaining 46.10%. A total of 11 flood-conditioning factors, including curvature, elevation, distance to river, drainage density, slope, flow accumulation, precipitation, TWI, NDVI, SPI and WI, were selected as numerical variables, while five factors, including aspect, flow direction, geology, substrate resistance to erosion, and land use, were identified as categorical variables in mapping flood susceptibility based on the literature [30,[36][37][38][39][40][41][42][43][44][45].…”

“…These include artificial neural network (ANN), which is one of the most widely used ML algorithms for flood risk prediction [21][22][23][24][25][26][27]. Other ML algorithms have been used to predict flood risk such as support vector machine [28][29][30], random forest (RF) [17,31,32], logistic regression [7], adaptive neuro-fuzzy inference system [33] and Long Short-term Memory [34,35].…”

Prediction of land surface temperature of major coastal cities of India using bidirectional LSTM neural networks

“…The weights are calculated by minimizing the mean square error between the output and the actual values. ASCE Task Committee on Application of Artificial Neural Networks in Hydrology (2000) provides a detailed survey of the numerous effective applications of ANNs to hydrological problems, e.g., estimating temperature (Cifuentes et al 2020), and precipitation (Lee et al 2018), modeling stream flows (Uysal et al 2016), forecasting river stages (Dazzi et al 2021), rainfall-runoff modeling (Riad et al 2004), water quality modeling (Rehana & Dhanya 2018;Zhu et al 2018), groundwater modeling (Ebrahimi & Rajaee 2017), and many other applications. More details of the ANN model and the training algorithms can be found in El- Baroudy et al (2010).…”

GeoAI in the US Geological Survey for topographic mapping

“…The application of AI to hydrology has largely focused on atmospheric modeling and image segmentation (Ball, Anderson, & Chan, 2017). However, there is a growing body of research using AI for modeling hydrologic variables (Nourani, Baghanam, Adamowski, & Kisi, 2014) such as flow (Alquraish, Abuhasel, Alqahtani, & Khadr, 2021), runoff (Han, Choi, Jung, & Kim, 2021;Kratzert et al, 2019), sediment, and flooding (Dazzi, Vacondio, & Mignosa, 2021;Han et al, 2021;Jiang, Xie, & Sainju, 2019;Schmidt, Heße, Attinger, & Kumar, 2020). Surface water modeling is informed by atmospheric modeling, yet hydrologic feature extraction often employs traditional image segmentation methods using reflectance and elevation data.…”