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The traditional artificial neural network (ANN) inversion of electrical resistivity imaging (ERI) based on gradient descent algorithm is known to be inept for its low computation efficiency and does not ensure global convergence. In order to solve above problems, a kernel principal component wavelet neural network (KPCWNN) trained by an improved shuffled frog leaping algorithm (ISFLA) method is proposed in this study. An additional kernel principal component (KPC) layer is applied to reduce the dimensionality of apparent resistivity data and increase the computational efficiency of wavelet neural network (WNN). Meanwhile, a novel ISFLA algorithm is adopted for improving the learning ability and inversion quality of WNN. In the proposed ISFLA, a hybrid LC mutation attractor is used to enhance the exploitation ability and a differential updating rule is used to enhance the exploration ability. Four groups of experiments are considered to demonstrate the feasibility of the proposed inversion method. The inversion results of the synthetic and field examples show that the introduced method is superior to other algorithms in terms of prediction accuracy and computational efficiency, which contribute to better inversion results., (Copyright © 2018 Elsevier Ltd. All rights reserved.)

For better stability, safety and water resource management in a dam, it is important to evaluate the amount of seepage from the dam body. This research is focused on machine learning approach to predict the amount of seepage from Pakistan’s Earth and rock fill Tarbela Dam during 2003 to 2015. The data of temperature, rainfall, water inflow, sediment inflow, reservoir level collected during 2003 to 2015 served as input while the seepage from dam during this period was the output. Artificial Neural Network (ANN), Random Forest (RF), Support Vector Machine (SVM), and CatBoost (CB), have been used to model the input-output relationship. The algorithms used to predict the dam seepage reported a high R2 scores between actual and predicted values of average seepage, suggesting their reliability in predicting the seepage in the Tarbela Dam. Moreover, the CatBoost algorithm outperformed, by achieving an R2 score of 0.978 in training, 0.805 in validation, and 0.773 in testing phase. Similarly, RMSE was 0.025 in training, 0.076 in validation, and 0.111 in testing phase. Furthermore, to understand the sensitivity of each parameter on the output (average seepage), Shapley Additive Explanations (SHAP), a model explanation algorithm, was used to understand the affect of each parameter on the output. A comparison of SHAP used for all the machine learning models is also presented. According to SHAP summary plots, reservoir level was reported as the most significant parameter, affecting the average seepage in Tarbela Dam. Moreover, a direct relationship was observed between reservoir level and average seepage. It was concluded that the machine learning models are reliable in predicting and understanding the dam seepage in the Tarbela Dam. These Machine Learning models address the limitations of humans in data collecting and analysis which is highly prone to errors, hence arriving at misleading information that can lead to dam failure.