How can regime characteristics of catchments help in training of local and regional LSTM-based runoff models? - Archive ouverte HAL Access content directly
Preprints, Working Papers, ... Year : 2021

How can regime characteristics of catchments help in training of local and regional LSTM-based runoff models?

Abstract

In the field of Deep Learning, the long short-term memory (LSTM) networks lie in the category of recurrent neural network (RNN) architectures. The distinctive capability of the LSTM is learning non linear long term dependency structures. This makes the LSTM a good candidate for prediction tasks in non linear time dependent systems such as prediction of runoff in a catchment. In this study, we use a large sample of 740 gauged catchments with very diverse hydro-geo-climatic conditions across France. We present a regime classification based on three hydro-climatic indices to identify and classify catchments with similar hydrological behaviors. We do this because we aim to investigate how regime derived information can be used in training LSTM-based runoff models. The LSTM-based models that we investigate include local models trained on individual catchments as well as regional models trained on a group of catchments. In local training, for each regime, we identify the optimal lookback, i.e. the length of the sequence of past forcing data that the LSTM needs to work through. We then use this length in training regional models that differ in two aspects: 1) hydrological homogeneity of the catchments used in their training, 2) configuration of the static attributes used in their inputs. We examine how each of these aspects contributes to learning of the LSTM in regional training. At every step of this study, we benchmark performances of the LSTM against a conceptual model (GR4J) on both train and unseen data. We show that the optimal lookback is regime dependent and homogeneity of the train catchments in regional training has a more significant contribution to learning of the LSTM than the number of the train catchments.
Fichier principal
Vignette du fichier
2021_Hashemi_hydrology and earth system sciences_EGU.pdf (1.52 Mo) Télécharger le fichier
Origin : Files produced by the author(s)
Licence : CC BY - Attribution

Dates and versions

hal-03482700 , version 1 (26-01-2023)

Licence

Attribution - CC BY 4.0

Identifiers

Cite

Reyhaneh Hashemi, Pierre Brigode, Pierre-André Garambois, Pierre Javelle. How can regime characteristics of catchments help in training of local and regional LSTM-based runoff models?. 2023. ⟨hal-03482700⟩
70 View
3 Download

Altmetric

Share

Gmail Facebook Twitter LinkedIn More