Groundwater level forecasting using soft computing techniques

Natarajan, N.; Ch, Sudheer

doi:10.1007/s00521-019-04234-5

Cited by 65 publications

(14 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…evaporation, relative humidity, and temperature. In most research, precipitation, and groundwater level by a time delay, are used in developing data-driven models(Mohanty et al, 2010;Natarajan & Sudheer, 2019). For example,Chang et al (2015) used ANN model in two different ways in terms of input data to estimate groundwater level variations in response to climate change, the first using average monthly temperature, precipitation and previous groundwater level, and the second using the two climate variables of precipitation and temperature.…”

mentioning

confidence: 99%

Assessment of probable groundwater changes under representative concentration pathway (RCP) scenarios through the wavelet–GEP model

et al. 2021

View full text Add to dashboard Cite

The main objective of this research was to evaluate the possible impact of climate change on groundwater levels in the Tasuj Plain, Iran. To accomplish this, the values of precipitation for a near future period was projected through four General Circulation Models (GCM). Then, the groundwater level variations through the Genetic Expression Programming (GEP) model were evaluated. The projection results indicated that the average annual precipitation with 33.55 mm in the base period would decline to 20.51 mm, 20.11 mm, and 19.14 mm under three Representative Concentration Pathway (RCP) scenarios, namely RCP2.6, RCP4.5, RCP8.5, respectively. The values of the determination coefficient range from 0.92-0.99; the root mean square error between 0.12 and 0.61, and mean absolute error from 0.08 to 0.54 showed that in the forecasting of groundwater level through the GEP model, all models have acceptable results. The evaluation of groundwater level simulation demonstrated that the developed model through the precipitation and previous groundwater level performs better. Prediction of groundwater level for a future period based on climate change scenarios indicated that the average groundwater level in Tasuj Plain at the beginning of the period would experience a gradual reduction and would then increase very slightly to the end of the period. Overall, it was found that the declining precipitation under climate change has no significant impacts on the groundwater level in the Tasuj Plain, and other parameters like anthropogenic activities could be the primary reason for groundwater depletion.

show abstract

mentioning

confidence: 99%

Assessment of probable groundwater changes under representative concentration pathway (RCP) scenarios through the wavelet–GEP model

et al. 2021

View full text Add to dashboard Cite

show abstract

“…Furthermore, the ELM model was able to forecast the monthly GWL more accurately compared to the SVM model. Another study conducted in Vizianagaram, India, by Natarajan and Sudheer (2020) was performed to compare the usage of different standalone, hybrid ML, and DL models in predicting GWLs. The standalone techniques used were ANN, GP, SVM, the hybrid techniques were a combination of SVM and quantum particle swarm optimization (QPSO), and a combination of SVM and radial basis function (RBF), while ELM was used as the DL technique.…”

Section: Literature Reviewmentioning

confidence: 99%

Modeling the fluctuations of groundwater level by employing ensemble deep learning techniques

Afan

Osman

Essam

et al. 2021

Engineering Applications of Computational Fluid Mechanics

View full text Add to dashboard Cite

This study proposes two techniques: Deep Learning (DL) and Ensemble Deep Learning (EDL) to predict groundwater level (GWL) for five wells in Malaysia. Two scenarios were proposed, scenario-1 (S1): GWL from 4 wells was used as inputs to predict the GWL in the fifth well and scenario-2 (S2): time series with lag time up to 20 days for all five wells. The results from S1 prove that the ensemble EDL generally performs superior to the DL in the estimation of GWL of each station using data of remaining four wells except the Paya Indah Wetland in which the DL method provide better estimates compared to EDL. Regarding S2, the EDL also exhibits superior performance in predicting daily GWL in all five stations compared to the DL model. Implementing EDL decreased the RMSE, NAE and RRMSE by 11.6%, 27.3% and 22.3% and increased the R, Spearman rho and Kendall tau by 0.4%, 1.1% and 3.5%, respectively. Moreover, EDL for S2 shows a high level of precision within less time lag, ranging between 2 and 4 compared to DL. Therefore, the EDL model has the potential in managing the sustainability of groundwater in Malaysia.

show abstract

“… 6 One of the advantages of soft computing techniques over numerical methods is the use of nonlinear algorithms for modeling and predicting the complex groundwater level behavior at various sites. 7 , 8 …”

Section: Introductionmentioning

confidence: 99%

Application of Artificial Intelligence Techniques for the Determination of Groundwater Level Using Spatio–Temporal Parameters

2022

View full text Add to dashboard Cite

Increasing the depth of mining leads to the location of the mine pit below the groundwater level. The entry of groundwater into the mining pit increases costs as well as reduces efficiency and the level of work safety. Prediction of the groundwater level is a useful tool for managing groundwater resources in the mining area. In this study, to predict the groundwater level, multilayer perceptron, cascade forward, radial basis function, and generalized regression neural network models were developed. Moreover, four optimization algorithms, including Bayesian regularization, Levenberg–Marquardt, resilient backpropagation, and scaled conjugate gradient, are used to improve the performance and prediction ability of the multilayer perception and cascade forward neural networks. More than 1377 data points including 12 spatial parameters divided into two categories of sediments and bedrock (longitude, latitude, hydraulic conductivity of sediments and bedrock, effective porosity of sediments and bedrock, the electrical resistivity of sediments and bedrock, depth of sediments, surface level, bedrock level, and fault), and besides, 6 temporal parameters are used (day, month, year, drainage, evaporation, and rainfall). Also, to determine the best models and combine them, 165 extra validation data points are used. After identifying the best models from the three candidate models with a lower average absolute relative error (AARE) value, the committee machine intelligence system (CMIS) model has been developed. The proposed CMIS model predicts groundwater level data with high accuracy with an AARE value of less than 0.11%. Sensitivity analysis indicates that the electrical resistivity of sediments had the highest effect on the groundwater level. Outliers’ estimation applying the Leverage approach suggested that only 2% of the data points could be doubtful. Eventually, the results of modeling and estimating groundwater level fluctuations with low error indicate the high accuracy of machine learning methods that can be a good alternative to numerical modeling methods such as MODFLOW.

show abstract

Groundwater level forecasting using soft computing techniques

Cited by 65 publications

References 37 publications

Assessment of probable groundwater changes under representative concentration pathway (RCP) scenarios through the wavelet–GEP model

Assessment of probable groundwater changes under representative concentration pathway (RCP) scenarios through the wavelet–GEP model

Modeling the fluctuations of groundwater level by employing ensemble deep learning techniques

Application of Artificial Intelligence Techniques for the Determination of Groundwater Level Using Spatio–Temporal Parameters

Contact Info

Product

Resources

About