A Neural Network-Based Hydrological Model for Very High-Resolution Forecasting Using Weather Radar Data

Abstract

Many hydro-meteorological disasters in small and steep watersheds develop quickly and significantly impact human lives and infrastructures. High-resolution rainfall data and machine learning methods have been used as modeling frameworks to predict those events, such as flash floods. However, a critical question remains: How long must the rainfall input data be for an empirical-based hydrological forecast? The present article employed an artificial neural network (ANN)hydrological model to address this issue to predict river levels and investigate its dependency on antecedent rainfall conditions. The tests were performed using observed water level data and high-resolution weather radar rainfall estimation over a small watershed in the mountainous region of Rio de Janeiro, Brazil. As a result, the forecast water level time series only archived a successful performance (i.e., Nash–Sutcliffe model efficiency coefficient (NSE) > 0.6) when data inputs considered at least 2 h of accumulated rainfall, suggesting a strong physical association to the watershed time of concentration. Under extended periods of accumulated rainfall (>12 h), the framework reached considerably higher performance levels (i.e., NSE > 0.85), which may be related to the ability of the ANN to capture the subsurface response as well as past soil moisture states in the watershed. Additionally, we investigated the model’s robustness, considering different seeds for random number generating, and spacial applicability, looking at maps of weights.

Enhancing Subsurface Soil Moisture Forecasting: A Long Short-Term Memory Network Model Using Weather Data

“…They compared these approaches with LSTM models for short-term soil moisture predictions, using rainfall and soil moisture as primary inputs [28]. A et al [29] and Santos et al [30] also developed machine learning models for subsurface soil moisture forecasts, and they used rainfall and historical soil moisture data as inputs. While these studies primarily utilized rainfall as a weather variable, they collectively underscore the potential of developing robust subsurface soil moisture models leveraging a wider array of weather-related variables, such as temperature, humidity, and more.…”

Evaluating the Utility of Selected Machine Learning Models for Predicting Stormwater Levels in Small Streams

“…(eXtreme Gradient Boosting) method, etc.) [42][43][44] and methods for sensitivity analyses of machine learning models (e.g., SHAP (SHapley Additive exPlanations) analysis, PDP (Partial Dependence Plot) analysis, etc.) [45][46][47].…”