Multiscale groundwater level forecasting: Coupling new machine learning approaches with wavelet transforms

Rahman, A. T. M. Sakiur; Hosono, Takahiro; Quilty, John; Das, Jayanta; Basak, Amiya

doi:10.1016/j.advwatres.2020.103595

Cited by 143 publications

(59 citation statements)

References 103 publications

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“…As mentioned in introduction, the XGB model has been applied to various fields, but hardly to groundwater research. Recent studies used the XGB model to predict groundwater level [57,58], analyze groundwater salinity [59], and assess groundwater quality [60]. In these studies, the XGB model had more accurate results compared to ANN, SVM, and multiple linear regression methods.…”

Section: Discussionmentioning

confidence: 99%

The Predictive Capability of a Novel Ensemble Tree-Based Algorithm for Assessing Groundwater Potential

Park

2021

Sustainability

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Understanding the potential groundwater resource distribution is critical for sustainable groundwater development, conservation, and management strategies. This study analyzes and maps the groundwater potential in Busan Metropolitan City, South Korea, using random forest (RF), gradient boosting machine (GBM), and extreme gradient boosting (XGB) methods. Fourteen groundwater conditioning factors were evaluated for their contribution to groundwater potential assessment using an elastic net. Curvature, the stream power index, the distance from drainage, lineament density, and fault density were excluded from the subsequent analysis, while nine other factors were used to create groundwater potential maps (GMPs) using the RF, GBM, and XGB models. The accuracy of the resultant GPMs was tested using receiver operating characteristic curves and the seed cell area index, and the results were compared. The analysis showed that the three models used in this study satisfactorily predicted the spatial distribution of groundwater in the study area. In particular, the XGB model showed the highest prediction accuracy (0.818), followed by the GBM (0.802) and the RF models (0.794). The XGB model, which is the most recently developed technique, was found to best contribute to improving the accuracy of the GPMs. These results contribute to the establishment of a sustainable management plan for groundwater resources in the study area.

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Section: Discussionmentioning

confidence: 99%

The Predictive Capability of a Novel Ensemble Tree-Based Algorithm for Assessing Groundwater Potential

Park

2021

Sustainability

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“…The contours of the real part of the Morlet wavelet coefficients can reflect the energy intensity information at different phases and time scales. The variation process of the wavelet coefficients with time represents the evolution law of alternating high and low values of the time series at this scale (Rahman et al 2020). The positive and negative changes in contour of the real part of the coefficient represent the evolution process and abrupt characteristics of the given data in the near future: the positive value corresponds to the rise period of the sequence, the negative value corresponds to the reduction phase, and the zero value corresponds to the transition period.…”

Section: Wavelet Analysismentioning

confidence: 99%

Characteristics in the Spatiotemporal Variation and Periodic Evolution of Groundwater in the Xining Area of China, Eastern Qinghai–Tibet Plateau

Li¹,

Zhan²,

Lu³

et al. 2021

Preprint

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Groundwater, an important source of water, has profound effects on global human survival and production. The spatiotemporal characteristics of groundwater and the periodic variation law of different time scales must be analyzed to grasp the dynamic situation of groundwater and provide scientific guidance for the rational utilization and management of groundwater resources. In this study, the temporal and spatial variability and periodicity of groundwater level were studied in the Xining region of the eastern Qinghai–Tibet Plateau China using traditional statistical method, geographic information system and Morlet wavelet analysis. Results show that the groundwater dynamics in the study area are mainly controlled by three factors, namely, the amount of river water infiltration, discharge from the groundwater, and artificial exploitation. The aforementioned factors can be divided into three dynamic types according to their combination relationship: hydrological, hydrological exploitation, and runoff-discharge types. The groundwater depth showed a trend of first increasing, then stabilizing, and finally decreasing from 1980 to 2020. The analysis in spatial variability demonstrated that the groundwater depth in different periods has a great spatial difference, with a moderate spatial variation intensity. Moreover, the spatial correlation of groundwater in the abundant season is lower than that in the dry season, which is mainly caused by the strengthening of artificial exploitation. The groundwater depth in the Xining region presents a pattern of deep in the south and east and shallow in the north and west by Kriging interpolation of spherical model in geographic information system. Meanwhile, the inter-annual groundwater has continued to decline since the chronic overexploitation between the 1960s and 2000s, with a maximum cumulative depth of 15 m. Then, the amount extraction had been further reduced, and rainfall had significantly increased in recent years. Accordingly, the cone of depression has undergone an evolutionary process from an expansion period to a stable period to a shrinking period. Furthermore, the annual groundwater level of most monitoring wells in study area has the same multi-year scale time variation characteristics with an evident regular periodic variation on the 9-14a and 17-25a time scales by using Morlet wavelet transform analysis. The temporal sequence of groundwater from 1980 to 2020 has the first and second main periods of 12a and 21a. In accordance with the two time-scales, the groundwater level will continue to rise in the short term in the future, which provides a scientific theoretical basis for the long-term sustainable development of groundwater resources and government decision-making.

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“…However, the highly nonlinear, non-stationary and relatively complex hydrological system, the need of lots of hydrogeological data as well as the proper initial and boundary conditions bring plenty of difficulties in the characterization of the real hydrological system, and thus threaten the accuracy and the popularization of these models [14,15]. With the rapid growth of the data-based methods (mainly machine learning models), conventional algorithms such as the artificial neural network (ANN), the support vector machine (SVM), the extreme learning machine (ELM), the adaptive neuro-fuzzy inference system (ANFIS) and genetic programming (GP) have become viable techniques for groundwater forecasting owing to the greater simplicity in design and flexibility [16,17]. A comprehensive and explicit review of the machine learning application in GWL modeling can be found in Rajaee et al [18].…”

Section: Introductionmentioning

confidence: 99%

Deep Learning-Based Predictive Framework for Groundwater Level Forecast in Arid Irrigated Areas

Liu

Yang

et al. 2021

Water

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An accurate groundwater level (GWL) forecast at multi timescales is vital for agricultural management and water resource scheduling in arid irrigated areas such as the Hexi Corridor, China. However, the forecast of GWL in these areas remains a challenging task owing to the deficient hydrogeological data and the highly nonlinear, non-stationary and complex groundwater system. The development of reliable groundwater level simulation models is necessary and profound. In this study, a novel ensemble deep learning GWL predictive framework integrating data pro-processing, feature selection, deep learning and uncertainty analysis was constructed. Under this framework, a hybrid model equipped with currently the most effective algorithms, including the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) for data decomposition, the genetic algorithm (GA) for feature selection, the deep belief network (DBN) model, and the quantile regression (QR) for uncertainty evaluation, denoted as CEEMDAN-GA-DBN, was proposed for the 1-, 2-, and 3-month ahead GWL forecast at three GWL observation wells in the Jiuquan basin, northwest China. The capability of the CEEMDAN-GA-DBN model was compared with the hybrid CEEMDAN-DBN and the standalone DBN model in terms of the performance metrics including R, MAE, RMSE, NSE, RSR, AIC and the Legates and McCabe’s Index as well as the uncertainty criterion including MPI and PICP. The results demonstrated the higher degree of accuracy and better performance of the objective CEEMDAN-GA-DBN model than the CEEMDAN-DBN and DBN models at all lead times and all the wells. Overall, the CEEMDAN-GA-DBN reduced the RMSE of the CEEMDAN-DBN and DBN models in the testing period by about 9.16 and 17.63%, while it improved their NSE by about 6.38 and 15.32%, respectively. The uncertainty analysis results also affirmed the slightly better reliability of the CEEMDAN-GA-DBN method than the CEEMDAN-DBN and DBN models at the 1-, 2- and 3-month forecast horizons. The derived results proved the ability of the proposed ensemble deep learning model in multi time steps ahead of GWL forecasting, and thus, can be used as an effective tool for GWL forecasting in arid irrigated areas.

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Multiscale groundwater level forecasting: Coupling new machine learning approaches with wavelet transforms

Cited by 143 publications

References 103 publications

The Predictive Capability of a Novel Ensemble Tree-Based Algorithm for Assessing Groundwater Potential

The Predictive Capability of a Novel Ensemble Tree-Based Algorithm for Assessing Groundwater Potential

Characteristics in the Spatiotemporal Variation and Periodic Evolution of Groundwater in the Xining Area of China, Eastern Qinghai–Tibet Plateau

Deep Learning-Based Predictive Framework for Groundwater Level Forecast in Arid Irrigated Areas

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