Basant Yadav

Assessing the suitability of extreme learning machines (ELM) for groundwater level prediction

Abstract Fluctuation of groundwater levels around the world is an important theme in hydrological research. Rising water demand, faulty irrigation practices, mismanagement of soil and uncontrolled exploitation of aquifers are some of the reasons why groundwater levels are fluctuating. In order to effectively manage groundwater resources, it is important to have accurate readings and forecasts of groundwater levels. Due to the uncertain and complex nature of groundwater systems, the development of soft computing techniques (data-driven models) in the field of hydrology has significant potential. This study employs two soft computing techniques, namely, extreme learning machine (ELM) and support vector machine (SVM) to forecast groundwater levels at two observation wells located in Canada. A monthly data set of eight years from 2006 to 2014 consisting of both hydrological and meteorological parameters (rainfall, temperature, evapotranspiration and groundwater level) was used for the comparative study of the models. These variables were used in various combinations for univariate and multivariate analysis of the models. The study demonstrates that the proposed ELM model has better forecasting ability compared to the SVM model for monthly groundwater level forecasting.

Data-based modelling approach for variable density flow and solute transport simulation in a coastal aquifer

ABSTRACT Data-based models, namely artificial neural network (ANN), support vector machine (SVM), genetic programming (GP) and extreme learning machine (ELM), were developed to approximate three-dimensional, density-dependent flow and transport processes in a coastal aquifer. A simulation model, SEAWAT, was used to generate data required for the training and testing of the data-based models. Statistical analysis of the simulation results obtained by the four models show that the data-based models could simulate the complex salt water intrusion process successfully. The selected models were also compared based on their computational ability, and the results show that the ELM is the fastest technique, taking just 0.5 s to simulate the dataset; however, the SVM is the most accurate, with a Nash-Sutcliffe efficiency (NSE) ≥ 0.95 and correlation coefficient R ≥ 0.92 for all the wells. The root mean square error (RMSE) for the SVM is also significantly less, ranging from 12.28 to 77.61 mg/L.

Ensemble Wavelet-Support Vector Machine Approach for Prediction of Suspended Sediment Load Using Hydrometeorological Data

AbstractExplicit prediction of the suspended sediment loads in rivers or streams is very crucial for sustainable water resources and environmental systems. Suspended sediments are a governing facto...

River discharge simulation using variable parameter McCarthy–Muskingum and wavelet-support vector machine methods

In this study, an extended version of variable parameter McCarthy–Muskingum (VPMM) method originally proposed by Perumal and Price (J Hydrol 502:89–102, 2013 ) was compared with the widely used data-based model, namely support vector machine (SVM) and hybrid wavelet-support vector machine (WASVM) to simulate the hourly discharge in Neckar River wherein significant lateral flow contribution by intermediate catchment rainfall prevails during flood wave movement. The discharge data from the year 1999 to 2002 have been used in this study. The extended VPMM method has been used to simulate 9 flood events of the year 2002, and later the results were compared with SVM and WASVM models. The analysis of statistical and graphical results suggests that the extended VPMM method was able to predict the flood wave movement better than the SVM and WASVM models. A model complexity analysis was also conducted which suggests that the two parameter-based extended VPMM method has less complexity than the three parameter-based SVM and WASVM model. Further, the model selection criteria also give the highest values for VPMM in 7 out of 9 flood events. The simulation of flood events suggested that both the approaches were able to capture the underlying physics and reproduced the target value close to the observed hydrograph. However, the VPMM models are slightly more efficient and accurate, than the SVM and WASVM model which are based only on the antecedent discharge data. The study captures the current trend in the flood forecasting studies and showed the importance of both the approaches (physical and data-based modeling). The analysis of the study suggested that these approaches complement each other and can be used in accurate yet less computational intensive flood forecasting.

Simulation-Optimization Approach for the Consideration of Well Clogging during Cost Estimation of In Situ Bioremediation System

AbstractIn situ bioremediation of groundwater has become one of the most widely used technologies for contaminated site treatment because of its relatively low cost, adaptability to site-specific c...