Evaluation of Machine Learning Techniques for Inflow Prediction in Lake Como, Italy

Abstract Accurate streamflow prediction is a fundamental task for integrated water resources management and flood risk mitigation. The purpose of this study is to forecast the water inflow to lake Como, (Italy) using different machine learning algorithms. The forecast is done for different days ranging from one day to three days. These models are evaluated by three statistical measures including Mean Absolute Error, Root Mean Squared Error, and the Nash-Sutcliffe Efficiency Coefficient. The experimental results show that Neural Network performs better for streamflow estimation with MAE and RMSE followed by Support Vector Regression and Random Forest.

[1]  J. Refsgaard,et al.  Operational Validation and Intercomparison of Different Types of Hydrological Models , 1996 .

[2]  Gökçen Uysal,et al.  Monthly streamflow estimation using wavelet-artificial neural network model: A case study on Çamlıdere dam basin, Turkey , 2017 .

[3]  Hafzullah Aksoy,et al.  Markov Chain-Incorporated Artificial Neural Network Models for Forecasting Monthly Precipitation in Arid Regions , 2014 .

[4]  M. Valipour,et al.  Comparison of the ARMA, ARIMA, and the autoregressive artificial neural network models in forecasting the monthly inflow of Dez dam reservoir , 2013 .

[5]  B. Pradhan,et al.  A comparative study of logistic model tree, random forest, and classification and regression tree models for spatial prediction of landslide susceptibility , 2017 .

[6]  Ivan Serina,et al.  Automatic classification of radiological reports for clinical care , 2018, Artif. Intell. Medicine.

[7]  E. Toth.  Classification of hydro-meteorological conditions and multiple artificial neural networks for streamflow forecasting , 2009 .

[8]  H. Karimi,et al.  Comparison of SRM and WetSpa models efficiency for snowmelt runoff simulation , 2016, Environmental Earth Sciences.

[9]  Leonard J. Tashman,et al.  Out-of-sample tests of forecasting accuracy: an analysis and review , 2000 .

[10]  Edward H. Bair,et al.  Using machine learning for real-time estimates of snow water equivalent in the watersheds of Afghanistan , 2017 .

[11]  J. Nash,et al.  River flow forecasting through conceptual models part I — A discussion of principles☆ , 1970 .

[12]  Kireet Kumar,et al.  Modelling suspended sediment concentration using artificial neural networks for Gangotri glacier , 2016 .

[13]  Arun Kumar Sangaiah,et al.  Analysis and Prediction of Water Quality Using LSTM Deep Neural Networks in IoT Environment , 2019, Sustainability.

[14]  Jan S. Hesthaven,et al.  Estimation of groundwater storage from seismic data using deep learning , 2018, Geophysical Prospecting.

[15]  C. Notarnicola,et al.  Seasonal river discharge forecasting using support vector regression: A case study in the Italian Alps , 2015 .

[16]  José Manuel Benítez,et al.  On the use of cross-validation for time series predictor evaluation , 2012, Inf. Sci..

[17]  M. Saroli,et al.  Machine Learning Models for Spring Discharge Forecasting , 2018, Geofluids.

[18]  Giorgio Guariso,et al.  The Management of Lake Como: A Multiobjective Analysis , 1986 .

[19]  Alex Avilés,et al.  A hybrid neural network-based technique to improve the flow forecasting of physical and data-driven models: Methodology and case studies in Andean watersheds , 2020 .

[20]  Bernhard Schölkopf,et al.  A tutorial on support vector regression , 2004, Stat. Comput..