Article 2022 · 0 atıf

COMPARISON OF DATA SPLITTING METHODS FOR NEURAL NETWORKS USED IN HYDROLOGICAL FORECASTING

Alioui, Youssef, Acar, Resat, Senocak, Serkan

Özet

In past decades, artificial neural networks (ANN) have been applied frequently in the prediction of water resources parameters. While developing ANN models, the available data were split into training, validation, and testing subsets. Most researchers did not consider the impact of the splitting method on the performance of the models and split the data using ad hoc methods. To investigate the impact of the data splitting method used on the performance of ANN models for inflow prediction, four data splitting methods were investigated to develop an ANN that predicts the inflow for multiple reservoirs. These methods were compared regarding the bias and variance of the performance of the models. The results indicate that the performance of the model is indeed influenced by the choice of the data division method. It was found that self-organizing maps (SOM) and k-means based stratified sampling are well suited for data division in the ANN development for water inflow prediction.

Anahtar Kelimeler

Hydrology water resources river engineering dams neural networks k-means LAND-USE VALIDATION MODELS PREDICTION VARIABLES RUNOFF

WoS Kategorileri

Environmental Sciences

Sürdürülebilir Kalkınma Amaçları

Temiz Su ve Sıhhi Koşullar Sudaki Yaşam İklim Eylemi

Künye

Dergi: FRESENIUS ENVIRONMENTAL BULLETIN
Yıl: 2022
Cilt: 31
Sayı: 1
Sayfa: 34-42
ISSN: 1018-4619
eISSN: 1610-2304
WoS UID: WOS:000736347700004