A Novel Hybrid Algorithms for Groundwater Level Prediction

Forecasting of stage-discharge in a non-perennial river using machine learning with gamma test

“…Taylor delivered a single demonstration demonstrating how to show several assessment metrics in real-time simultaneously. Correlation coefficients and standard deviation values between expected and observed values might be shown in this diagram to aid in the detection of changes between the two values [ 37 , 115 , 116 ].…”

“…Also, these models are flexible enough to predict hydrological problems with high efficiency [ [31] , [32] , [33] ]. Machine learning and artificial intelligence models have become very popular in recent decade [ [34] , [35] , [36] , [37] , [38] ]. Forecasting of the stream discharge various models such as multiple-linear regression (MLR) [ 2 , [39] , [40] , [41] , [42] ], rating curve [ [43] , [44] , [45] , [46] , [47] ], wavelet-based MLR (WMLR) [ 48 , 49 ], support vector machine (SVM) [ 39 , 44 , [50] , [51] , [52] , [53] ], artificial neural network (ANN) [ 45 , [53] , [54] , [55] , [56] , [57] ], wavelet-based artificial neural network (WANN) [ 2 , 39 , 58 ], adaptive neuro-fuzzy inference system (ANFIS) [ [59] , [60] , [61] ], wavelet-based support vector machine (WSVM) [ 39 , 62 ], wavelet–bootstrap–ANN (WBANN) [ 48 , 63 ], M5-model trees [ 46 , 64 ], random forest (RF) [ 65 ], ARIMA [ 65 , 66 ], gene expression programming (GEP) [ 32 , 67 , 68 ], genetic algorithm (GA) [ 3 , 33 , 69 ], genetic programming (GP) [ 32 ], Bagged M5P [ 65 ], integrating long-short-term memory (LSTM) [ 69 , 70 ], wavelet–bootstrap–multiple linear regression (WBMLR) [ 48 ], Fuzzy logic and f...…”

Eco-hydrological modeling of soil wetting pattern dimensions under drip irrigation systems

“…The performance indicators used were: the mean absolute error (MAE), the root mean square error (RMSE), the Nash- Sutcliffe model efficiency (EF), and R2. The following relationships were used to calculate the various statistical indicators as listed below [ 80 , 81 ]:…”

Evaluate effect of 126 pre-processing methods on various artificial intelligence models accuracy versus normal mode to predict groundwater level (case study: Hamedan-Bahar Plain, Iran)

“…These models involve specific knowledge of the physical characteristics of the study area, complex boundary layers, more assumptions and large dataset which makes it more expensive, labour consuming, tedious etc [ 1 , [10] , [11] , [12] , [13] ]. Nowadays, various machine learning techniques has been proved the capability to overcome the traditional techniques limitations and shown to be precise estimation of different parameters or events with multi time scale in the complex hydrology modelling studies [ [14] , [15] , [16] , [17] , [18] , [19] , [20] , [21] , [22] , [23] , [24] ].…”