Application of deep learning approaches to predict monthly stream flows.

Accurate and reliable flow estimations are of great importance for hydroelectric power generation, flood and drought risk management, and the effective use of water resources. This research carries out a comprehensive study on the application of gated recurrent unit (GRU) neural network, recurrent neural network (RNN), and long short-term memory (LSTM) to predict with river flows at three different streamflow observation stations in Erzincan, Bayburt, and Gümüshane. Monthly streamflow time series covering the years 1978 to 2015 were used to set up artificial intelligence models. During the modeling phase, 70% of the data was divided into training (October 1978-April 2004), 15% validation (May 2004-September 2009), and 15% test set (October 2010-September 2015). Model performances were made according to the correlation coefficient, root mean square error, the ratio of RMSE to the standard deviation, Nash-Sutcliffe efficiency coefficient, index of agreement, and volumetric efficiency values. The calculation results show that GRU leads efficient estimation results for estimating streamflow and can also be used in allied water resources.

GIS-based groundwater potential mapping using boosted regression tree, classification and regression tree, and random forest machine learning models in Iran.

Groundwater is considered one of the most valuable fresh water resources. The main objective of this study was to produce groundwater spring potential maps in the Koohrang Watershed, Chaharmahal-e-Bakhtiari Province, Iran, using three machine learning models: boosted regression tree (BRT), classification and regression tree (CART), and random forest (RF). Thirteen hydrological-geological-physiographical (HGP) factors that influence locations of springs were considered in this research. These factors include slope degree, slope aspect, altitude, topographic wetness index (TWI), slope length (LS), plan curvature, profile curvature, distance to rivers, distance to faults, lithology, land use, drainage density, and fault density. Subsequently, groundwater spring potential was modeled and mapped using CART, RF, and BRT algorithms. The predicted results from the three models were validated using the receiver operating characteristics curve (ROC). From 864 springs identified, 605 (≈70 %) locations were used for the spring potential mapping, while the remaining 259 (≈30 %) springs were used for the model validation. The area under the curve (AUC) for the BRT model was calculated as 0.8103 and for CART and RF the AUC were 0.7870 and 0.7119, respectively. Therefore, it was concluded that the BRT model produced the best prediction results while predicting locations of springs followed by CART and RF models, respectively. Geospatially integrated BRT, CART, and RF methods proved to be useful in generating the spring potential map (SPM) with reasonable accuracy.