Short-term prediction of groundwater level using improved random forest regression with a combination of random features

Wang, Xuanhui; Liu, Tailian; Zheng, Xilai; Peng, Hui; Xin, Jia; Zhang, Bo

doi:10.1007/s13201-018-0742-6

Cited by 63 publications

(27 citation statements)

References 32 publications

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“…In the last few years, other ensemble and conventional ML models are also developed to predict GWL prediction for sustainable water resource management [39], [40]. In [39] the authors presented an ensemble model based on KNN and RF for three months ahead of groundwater table prediction based on seasonal changes.…”

Section: Literature Reviewmentioning

confidence: 99%

“…In [39] the authors presented an ensemble model based on KNN and RF for three months ahead of groundwater table prediction based on seasonal changes. In [40], the authors proposed an enhanced RF prediction model based on the combination of random features to forecast GWL using two features; temperature (Celsius) and precipitation (Millimeters). The authors reported that the R2 score value of the enhanced RF is 0.8223 for long-term forecasting, which is still improvable.…”

Section: Literature Reviewmentioning

confidence: 99%

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Groundwater Level Prediction Model Using Correlation and Difference Mechanisms Based on Boreholes Data for Sustainable Hydraulic Resource Management

et al. 2021

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Drilling data for groundwater extraction incur changes over time due to variations in hydrogeological and weather conditions. At any time, if there is a need to deploy a change in drilling operations, drilling companies keep monitoring the time-series drilling data to make sure it is not introducing any changes or new errors. Therefore, a solution is needed to predict groundwater levels (GWL) and detect a change in boreholes data to improve drilling efficiency. The proposed study presents an ensemble GWL prediction (E-GWLP) model using boosting and bagging models based on stacking techniques to predict GWL for enhancing hydraulic resource management and planning. The proposed research study consists of two modules; descriptive analysis of boreholes data and GWL prediction model using ensemble model based on stacking. First, descriptive analysis techniques, such as correlation analysis and difference mechanisms, are applied to investigate boreholes log data for extracting underlying characteristics, which is critical for enhancing hydraulic resource management. Second, an ensemble prediction model is developed based on multiple hydrological patterns using robust machine learning (ML) techniques to predict GWL for enhancing drilling efficiency and water resource management. The architecture of the proposed ensemble model involves three boosting algorithms as base models (level-0) and a bagging algorithm as a meta-model that combines the base models predictions (level-1). The base models consist of the following boosting algorithms; eXtreme Gradient Boosting (XGBoost), AdaBoost, Gradient Boosting (GB). The meta-model includes Random Forest (RF) as a bagging algorithm referred to as a level-1 model. Furthermore, different evaluation metrics are used, including mean absolute error (MAE), mean square error (MSE), and root mean square error (RMSE), mean absolute percentage error (MAPE), and R2 score. The performance of the proposed E-GWLP model is compared with existing ensemble and baseline models. The experimental results reveal that the proposed model performed accurately in respect of MAE, MSE, and RMSE of 0.340, 0.564, and 0.751, respectively. The MAPE and R2 score of our proposed approach is 12.658 and 0.976, respectively, which signifies the importance of our work. Moreover, experimental results suggest that E-GWLP model is suitable for sustainable water resource management and improves reservoir engineering.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Groundwater Level Prediction Model Using Correlation and Difference Mechanisms Based on Boreholes Data for Sustainable Hydraulic Resource Management

et al. 2021

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“…Ganga Devi [21] applied Random Forests model for predicting the groundwater quality class label and to check the groundwater from the study area is suitable for drinking purpose or not. Wang et al [22] proposed an enhanced random forest model for short term prediction groundwater level of Daguhe River groundwater source field, in Qingdao, China.…”

Section: Literature Surveymentioning

confidence: 99%

Classification of Groundwater by Applying Simplified Fuzzy Adaptive Resonance Theory

Kiran¹,

Chaparala²,

Radhika³

2021

IJDNE

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Groundwater quality assessment is primarily intended to determine whether the water in a particular area can be used for aquatic purpose or not. The assessment comprises analysis of physical, chemical and microbiological characteristics of groundwater samples. The quality of groundwater can be evaluated through few standard conventional methods viz. Water Quality Index, Canadian Council of Ministries Environmental Water Quality Index and Weighted Arithmetic Water Quality Index etc. In addition to the conventional methods, multivariate statistical methods like Principal Component Analysis, Factor Analysis and Cluster Analysis can also be used to assess the groundwater quality. As these methods are descriptive models, they are inadequate to predict the quality of unknown groundwater sample. Hence, an efficient predictive model is desirable to analyze the characteristic parameters of groundwater samples and predict the quality of an unknown sample. The sample may have both crisp and fuzzy values. Conventional supervised learning methods may not be suitable for constructing the required prediction model as they are not suitable for handling fuzzy input data. Therefore, simplified fuzzy adaptive resonance theory model is an appropriate choice for accomplishing the task of building the prediction model. The present work proposes to assess the quality of groundwater by applying the Weighted Arithmetic Water Quality Index method and Simplified Fuzzy Adaptive Resonance Theory model by considering 7 groundwater quality parameters. The accuracy of afore mentioned approach seems to be pleasing when compared to counter parts like Back propagation and Random Forests classifiers.

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“…The results demonstrated that the RF model has superior predictive capabilities with fewer parameters and training time. Decision tree-based models have been successfully applied in groundwater hydrological modeling (Singh et al, 2014;Wang et al, 2018). Marques et al (2005) used ML algorithms to optimize water supply for crops at different growing stages, given the water cost, the market price, cost of irrigation, crop expenses, and expected yield reduction due to under-or overirrigation throuhout the entire growing period.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning vs. Physics-Based Modeling for Real-Time Irrigation Management

et al. 2020

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Real-time monitoring of soil matric potential has now become a common practice for precision irrigation management. Some crops, such as cranberries, are susceptible to both water and anoxic stresses. Excessive variations in soil matric potential in the root zone may reduce plant transpiration, due to either saturated or dry soil conditions, thereby reducing productivity. A timely supply of the right amount of water is, therefore, fundamental for efficient irrigation management. In this paper, we compare the capabilities of a machine learning-based model and a physics-based model to predict soil matric potential in the root zone. The machine learning model is a random forest algorithm, while the physics-based model is a two-dimensional solver of Richards equation (HYDRUS 2D). After training and calibration on a dataset collected in a cranberry field located in Québec (Canada), the performance of the two models is evaluated for 30 different time frames of 72-h soil matric potential forecasts. The results highlight that both models can accurately forecast the soil matric potential in the root zone. The machine learning-based model can achieve better performance when compared to the physics-based model, but forecasting accuracy decreases rapidly toward the end of the 72-h lead time, while the error for the Richards equation-based model does not increase with time and remain small compared to the typical measurement error.

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Short-term prediction of groundwater level using improved random forest regression with a combination of random features

Cited by 63 publications

References 32 publications

Groundwater Level Prediction Model Using Correlation and Difference Mechanisms Based on Boreholes Data for Sustainable Hydraulic Resource Management

Groundwater Level Prediction Model Using Correlation and Difference Mechanisms Based on Boreholes Data for Sustainable Hydraulic Resource Management

Classification of Groundwater by Applying Simplified Fuzzy Adaptive Resonance Theory

Machine Learning vs. Physics-Based Modeling for Real-Time Irrigation Management

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