The data-driven approach as an operational real-time flood forecasting model

Abstract

Accurate water level forecasts are essential for flood warning. This study adopts a data-driven approach based on the adaptive

network–based fuzzy inference system (ANFIS) to forecast the daily water levels of the Lower Mekong River at Pakse, Lao

People’s Democratic Republic. ANFIS is a hybrid system combining fuzzy inference system and artificial neural networks. Five

ANFIS models were developed to provide water level forecasts from 1 to 5 days ahead, respectively. The results show that

although ANFIS forecasts of water levels up to three lead days satisfied the benchmark, four- and five-lead-day forecasts were

only slightly better in performance compared with the currently adopted operational model. This limitation is imposed by the

auto- and cross-correlations of the water level time series. Output updating procedures based on the autoregressive (AR) and

recursive AR (RAR) models were used to enhance ANFIS model outputs. The RAR model performed better than the AR model.

In addition, a partial recursive procedure that reduced the number of recursive steps when applying the AR or the RAR model for

multi-step-ahead error prediction was superior to the fully recursive procedure. The RAR-based partial recursive updating

procedure significantly improved three-, four- and five-lead-day forecasts. Our study further shows that for long lead times,

ANFIS model errors are dominated by lag time errors. Although the ANFIS model with the RAR-based partial recursive

updating procedure provided the best results, this method was able to reduce the lag time errors significantly for the falling limbs

only. Improvements for the rising limbs were modest.