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Monthly streamflow forecasting is critical for improving water resource management. In this study, several base-classifier data-mining algorithms – conjunctive rule (CR), isotonic regression (ISOR), sequential minimal optimization regression (SMOR) – as well as several hybrid data-mining techniques – disjoint aggregating or dagging (DA)-CR, DA-ISOR, and DA-SMOR – that combine dagging with these algorithms were developed and applied to forecasting streamflow 3 and 12 months into the future (i.e., Qt+3 and Qt+12) based on meteorological data from a nearby station. Thirty years of data (from 1988 to 2018) that included precipitation, minimum relative humidity, maximum relative humidity, evaporation, hours of sunshine, maximum temperature, minimum temperature, wind speed, and streamflow were collected at the Kermanshah synoptic station were input to develop and evaluate several models. Varying combinations of the input data were tested to find the optimal set to employ. The models were validated and compared using several quantitative statistical indices. Though all satisfactorily predicted monthly streamflow, the hybrid models (DA-CR, DA-ISOR, and DA-SMOR) outperformed the base-classifier models (CR, ISOR, and SMOR), proving that dagging improved data-mining models significantly. Of the hybrid models, D-SMOR was the best. The models developed in this study are cost-effective tools for quick and accurate monthly streamflow forecasting. HIGHLIGHTS Streamflow is forecasting based on meteorological data from a nearby station.; Forecasting streamflow is for 3 and 12 months ahead (i.e., Qt+3 and Qt+12).; Conjunctive rule (CR), isotonic regression (ISOR), sequential minimal optimization regression (SMOR) – disjoint aggregating or dagging (DA)-CR, DA-ISOR, and DA-SMOR models are developed.; The DA-SMOR model has the highest prediction power.; Minimum and maximum temperature are the two most effective variables in Q forecasting.;

Abstract This paper presents some new analytical solutions to an accurate prediction of the behavior of the groundwater flow in aquifers in response to changes in surface water. The new analytical solutions are obtained using integral transforms. An anisotropic rectangular confined aquifer bounded with four time-varying streams is undertaken. The effects of anisotropy on groundwater head and flow rate near time-varying streams are investigated. Depending on the change rates of the streams level, an anisotropic aquifer may render either lower or higher hydraulic head than an isotropic aquifer. In addition, an anisotropic aquifer has provided less water exchange at the interfaces than an isotropic one. The sensitivity of the hydraulic head to change rate of the streams level in both isotropic and anisotropic aquifers is evaluated. It is shown that the aquifer response is more sensitive to change rate of the streams parallel to y-direction and less sensitive to change rate of the streams parallel to x-direction in an anisotropic aquifer and vice versa in an isotropic aquifer. The results of the present new analytical solutions are compared with numerical model of MODFLOW. The results obtained from the presented solutions showed good agreement with the results of MODFLOW. The results show that the presented new analytical solutions are accurate, robust and efficient. Therefore, the results indicate that the presented new analytical solutions are very effective in the simulation of the groundwater flow in river–aquifer systems. Furthermore, one of the advantages of the new analytical solutions is to investigate the sensitivity analysis of aquifer parameters, which has been carried out in this paper. Also, some other new analytical solutions for steady-state conditions and sudden fall in streams level are provided as well. Feasibility of the proposed new analytical solutions is presented via calculating and simulating the hydraulics of groundwater flow in river–aquifer systems by means of integral transforms.

ET0 plays an essential role in water resources management. In this study, nine temperature-based, ten radiation-based and six mass transfer-based methods at three-time scales (monthly, seasonal, and annual) were evaluated versus the FAO56-PM model, and the best and worst methods were selected from each group for the cold moderate and semi-humid climate of Iran. At annual and seasonal time scales (spring, summer and autumn seasons) Hargreaves-M4 and Trajkovic methods (temperature-based methods), Priestley-Taylor and IRMAK1 equations (radiation-based methods) in winter season and for monthly time scale: Meyer and Rohwer, Hargreaves-M4, Trajkovic, IRMAK1, and Priestley-Taylor methods were shown to have the best performance in comparison to FAO56 PM. These equations are the best ET0 estimators in cold moderate, and semi-humid climate. In this study, two equations with the best performance, namely Hargreaves-M4 and Trajkovic were optimized as linear and power functions. The results showed that the power equation had better performance than the linear equation. Finally, two equations were presented based on the linear multivariate regression to simplify ET0 estimation. The results revealed that those kinds of equations, which used Tmax, Tmin as input parameters have high accuracy in comparison with Hargreaves-M4 and Trajkovic equations.