Forecasting Groundwater Fluctuation from GRACE Data Using GRNN

Kumar, Dilip; Bhattacharjya, Rajib Kumar

doi:10.1007/978-981-15-4032-5_28

Cited by 1 publication

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“…GR has a high learning speed and is very useful for function approximation problems. For small sample data, the prediction effect is excellent, and also unstable data can be processed. , GR does not have RBF accuracy but has a major advantage in classification and fit, especially when data accuracy is inappropriate. A GR model consists of four layers (input, pattern, summation, and output layer) as illustrated in Figure …”

Section: Methodsmentioning

confidence: 99%

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

2022

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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.

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Section: Methodsmentioning

confidence: 99%