A Novel Approach for Prediction of Monthly Ground Water Level Using a Hybrid Wavelet and Non-Tuned Self-Adaptive Machine Learning Model

Water Resources Management - In recent decades, due to groundwater withdrawal in the Kabodarahang region, Iran, Hamadan, hazardous events such as sinkholes, droughts, water scarcity, etc., have...     

Modelling qualitative and quantitative parameters of groundwater using a new wavelet conjunction heuristic method: wavelet extreme learning machine versus wavelet neural networks

In recent years, as a result of climate change as well as rainfall reduction in arid and semi‐arid regions, modelling qualitative and quantitative parameters belonging to aquifers has become crucially important. In Iran, as aquifers are treated as the most commonly used drinking water resources, modelling their qualitative and quantitative parameters is enormously important. In this paper, for the first time, values of salinity, total dissolved solids (TDS), groundwater level (GWL) and electrical conductivity (EC) of the Arak Plain, located in Markazi Province, Iran, are simulated by means of four modern artificial intelligence models including extreme learning machine (ELM), wavelet extreme learning machine (WELM), online sequential extreme learning machine (OSELM) and wavelet online sequential extreme learning machine (WOSELM) as well as the MODFLOW software for a 15‐year period monthly. To develop the hybrid artificial intelligence models, the wavelet is employed. First, the effective lags in estimating the qualitative and quantitative parameters of the groundwater are identified using the autocorrelation function (ACF) and the partial autocorrelation function (PACF) analysis. After that, four different models are developed by the selected input combinations and also the ACF and the PACF in the form of different lags for each of ELM, WAELM, OSELM and WOSELM methods. Then, the superior models in simulating the groundwater qualitative and qualitative parameters are detected by conducting a sensitivity analysis. To forecast the electrical conductivity (EC) by the best WOSELM model, the values of the Nash–Sutcliffe efficiency coefficient (NSC), Mean Absolute Error (MAE) and the scatter index (SI) are obtained to be 0.991, 18.005 and 4.28E‐03, respectively. In addition, the most effective lags in estimating these parameters are introduced. Subsequently, the results found by the MODFLOW model are compared with those of the artificial intelligence models and it is concluded that the latter are more accurate. For instance, the scatter index and Nash–Sutcliffe efficiency coefficient values calculated by WOSELM for TDS, respectively, are 5.34E‐03 and 0.991. Finally, an uncertainty analysis is conducted to evaluate the performance of different numerical models. For example, MODFLOW has an underestimated performance in simulating the salinity parameter.     

Turbulent Flow in the Vicinity of Side Weirs in Subcritical Conditions

Side weirs are installed on the main channels wall in sewage disposal systems and irrigation networks to divert the flow. In this paper, the flow pattern in rectangular channels along a side weir is predicted using the Volume of Fluid (VOF) scheme and the standard k–ε and RNG k–ε turbulence models. A comparison between the numerical and experimental results shows the high accuracy of the numerical model. For example, the values of root mean square error (RMSE), mean absolute relative error (MARE) and correlation coefficient (R) for the lateral velocity on z = 0.183 m level are calculated 3.782, 0.399 and 0.993, respectively. According to the simulation results, as the flow approaches to the side weir plane the flow field pressure decreases suddenly and at the end of the side weir the pressure increases. Also, by advancing the flow towards the downstream of the side weir the turbulent kinetic energy of the flow within the rectangular channel decreases. For all levels, the turbulent length scale value suddenly increases by reaching the flow to the end of the side weir.     

Extracting Optimal Rule Curve of Dam Reservoir Base on Stochastic Inflow

Water Resources Management - Declining rainfall, development of agricultural and industrial activities, population growth as well as Iran's location in arid and semi-arid regions of the planet...     

A Comparative Study of Artificial Intelligence Models and A Statistical Method for Groundwater Level Prediction

Today, various methods have been developed to extract drinking water resources, which scientists use to simulate the quantitative and qualitative water resources parameters. Due to Iran's geographical and climatic characteristics, this region is located on the drought belt in Asia. In this research, some Artificial Intelligence (AI) and mathematical models have been used for groundwater level prediction. The AI models used for this research are Extreme Learning Machine (ELM), Least Square Support Vector Machine (LSSVM), Adaptive Neuro-Fuzzy Inference System (ANFIS), and Multiple Linear Regression (MLR) model. In this study, simultaneously, these models were used to simulate and estimate groundwater level (GWL). The database used in the simulation is the data related to the Total Dissolved Solids (TDS), Electrical Conductivity (EC), Salinity (S), and Time (t) parameters. The results showed that ELM was more accurate than other methods. In Uncertainty Wilson Score Method (UWSM) analysis, ELM had an Underestimation performance and was determined as the more precise model.