Groundwater Depth Prediction Using Data-Driven Models with the Assistance of Gamma Test

Tian, Jiyang; Li, Chuanzhe; Liu, Jia; Yu, Fuliang; Cheng, Shuanghu; Zhao, Na; Jaafar, Wan Zurina Wan

doi:10.3390/su8111076

Cited by 25 publications

(13 citation statements)

References 24 publications

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“…An ANN is a computer system that simulates the learning process of human neurons. Among the various training methods, the Back Propagation Network (BPN) method is the most widely used because of its effectiveness and short learning time [28]. The BPN method was used to develop a prediction model with the basic network structure shown in Figure 3 [29].…”

Section: Methodsmentioning

confidence: 99%

A Duration Prediction Using a Material-Based Progress Management Methodology for Construction Operation Plans

Han

2017

Sustainability

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Precise and accurate prediction models for duration and cost enable contractors to improve their decision making for effective resource management in terms of sustainability in construction. Previous studies have been limited to cost-based estimations, but this study focuses on a material-based progress management method. Cost-based estimations typically used in construction, such as the earned value method, rely on comparing the planned budget with the actual cost. However, accurately planning budgets requires analysis of many factors, such as the financial status of the sectors involved. Furthermore, there is a higher possibility of changes in the budget than in the total amount of material used during construction, which is deduced from the quantity take-off from drawings and specifications. Accordingly, this study proposes a material-based progress management methodology, which was developed using different predictive analysis models (regression, neural network, and auto-regressive moving average) as well as datasets on material and labor, which can be extracted from daily work reports from contractors. A case study on actual datasets was conducted, and the results show that the proposed methodology can be efficiently used for progress management in construction.

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Section: Methodsmentioning

confidence: 99%

A Duration Prediction Using a Material-Based Progress Management Methodology for Construction Operation Plans

Han

2017

Sustainability

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“…The key to the accuracy of SVM regression model is the penalty factor C and the width of kernel function σ [36]. Therefore, this paper chooses the improved CS algorithm to optimize C and σ in order to improve the generalization ability of SVM.…”

Section: Svmmentioning

confidence: 99%

Cost Forecasting of Substation Projects Based on Cuckoo Search Algorithm and Support Vector Machines

Niu

Zhao

et al. 2018

Sustainability

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Accurate prediction of substation project cost is helpful to improve the investment management and sustainability. It is also directly related to the economy of substation project. Ensemble Empirical Mode Decomposition (EEMD) can decompose variables with non-stationary sequence signals into significant regularity and periodicity, which is helpful in improving the accuracy of prediction model. Adding the Gauss perturbation to the traditional Cuckoo Search (CS) algorithm can improve the searching vigor and precision of CS algorithm. Thus, the parameters and kernel functions of Support Vector Machines (SVM) model are optimized. By comparing the prediction results with other models, this model has higher prediction accuracy.

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“…Data driven models are developed with existing data and information on the relationship between input and output parameters. ese models are location specific, with the output values being applicable only to the location where it is developed [10]. Statistical, fuzzy, regression, and ANN are mathematical approaches typically used in these data-driven models.…”

Section: Introductionmentioning

confidence: 99%

Lion Algorithm-Optimized Long Short-Term Memory Network for Groundwater Level Forecasting in Udupi District, India

Supreetha

Shenoy

Nayak

2020

Applied Computational Intelligence and Soft Computing

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Groundwater is a precious natural resource. Groundwater level (GWL) forecasting is crucial in the field of water resource management. Measurement of GWL from observation-wells is the principle source of information about the aquifer and is critical to its evaluation. Most part of the Udupi district of Karnataka State in India consists of geological formations: lateritic terrain and gneissic complex. Due to the topographical ruggedness and inconsistency in rainfall, the GWL in Udupi region is declining continually and most of the open wells are drying-up during the summer. Hence, the current research aimed at developing a groundwater level forecasting model by using hybrid long short-term memory-lion algorithm (LSTM-LA). e historical GWL and rainfall data from an observation well from Udupi district, located in Karnataka state, India, were used to develop the model. e prediction accuracy of the hybrid LSTM-LA model was better than that of the feedforward neural network (FFNN) and the isolated LSTM models. e hybrid LSTM-LA-based forecasting model is promising for a larger dataset.

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Groundwater Depth Prediction Using Data-Driven Models with the Assistance of Gamma Test

Cited by 25 publications

References 24 publications

A Duration Prediction Using a Material-Based Progress Management Methodology for Construction Operation Plans

A Duration Prediction Using a Material-Based Progress Management Methodology for Construction Operation Plans

Cost Forecasting of Substation Projects Based on Cuckoo Search Algorithm and Support Vector Machines

Lion Algorithm-Optimized Long Short-Term Memory Network for Groundwater Level Forecasting in Udupi District, India

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