Semi-seasonal groundwater forecast using multiple data-driven models in an irrigated cropland

Alessandro Amaranto, Francisco Munoz-Arioloa, Gerald Corzo, Dimitri P. Solomatine, George Meyer

Research output: Contribution to journalArticleScientificpeer-review

11 Citations (Scopus)

Abstract

In agricultural areas where groundwater is the main water supply for irrigation, forecasts of the water table are key to optimal water management. However, water management can be constrained by semiseasonal to seasonal forecast. The objective is to create an ensemble of water table one- to fivemonth lead-time forecasts based on multiple data-driven models (DDMs). We hypothesize that datadriven modeling capabilities (e.g., random forests, support vector machines, artificial neural networks (ANNs), extreme learning machines, and genetic programming) will improve naïve and autoregressive simulations of groundwater tables. An input variable selection method was used to carry the maximum information in the input (precipitation, crop water demand, changes in groundwater table, snowmelt, and evapotranspiration) and output relationship for the DDMs. Five DDMs were implemented to forecast groundwater tables in an unconfined aquifer in the Northern High Plains (Nebraska, USA). Root mean squared error (RMSE) and Nash-Sutcliffe efficiency index were used to evaluate the skill of the model and three hydrologic regimes were determined statistically as high, mid, and low groundwater table levels. Additionally, varying storage regimes were used to construct rising and falling limbs in the tested well. Results show that all models outperform the baseline for all the lead times, ANNs being the best of all.

Original languageEnglish
Pages (from-to)1227-1246
Number of pages20
JournalJournal of Hydroinformatics
Volume20
Issue number6
DOIs
Publication statusPublished - 2018

Keywords

  • Data-driven models
  • Ensemble
  • Groundwater
  • Semi-seasonal forecast

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