monitoring

Improving irrigation planning and early prediction for agricultural drought in Wisconsin
A new ensemble modeling framework based on artificial neural network nonlinear autoregressive networks was developed to predict spatio-temporal fluctuations of groundwater in the densely irrigated area of Wisconsin central sands (WCS).
Ammara Talib, Ankur R Desai (2020)
Dec 15, 2020
Improving irrigation planning and early prediction for agricultural drought in Wisconsin
Field-scale mapping and forecasting of water budgets in intensively irrigated agricultural regions through an advanced ensemble modeling framework
An algorithm is developed that can accurately predict and forecast farm-scale regional daily out to 3 days. Daily ET forecast (3 days) model based on random forest (RF) has R2 and RMSE of 0.72 mm and 0.76mm respectively while recurrent neural network (RNN) ensemble forecast model was able to forecast 3 days ET with R2 and RMSE of 0.71 mm and 0.78 mm respectively.
Ammara Talib, Ankur R Desai (2019)
Dec 10, 2019
Field-scale mapping and forecasting of water budgets in intensively irrigated agricultural regions through an advanced ensemble modeling framework
Efficacy of Machine Learning Algorithms for Identifying Hotspots of Groundwater Depletion in Intensively Irrigated Agricultural Regions
A new ensemble modeling framework based on artificial neural network nonlinear autoregressive networks was developed to predict spatio-temporal fluctuations of groundwater in the densely irrigated area of Wisconsin central sands (WCS).
Ammara Talib, Ankur R Desai (2018)
Dec 11, 2018
Efficacy of Machine Learning Algorithms for Identifying Hotspots of Groundwater Depletion in Intensively Irrigated Agricultural Regions