Groundwater level simulation using gene expression programming and M5 model tree combined with wavelet transform

Bahmani, Ramin; Solgi, Abazar; Ouarda, Taha B. M. J.

doi:10.1080/02626667.2020.1749762

Cited by 20 publications

(13 citation statements)

References 45 publications

(24 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Based on previous studies, Genetic models (GP and GEP) in the simulation and prediction of rainfall-runoff, water table depth variations, precipitation, suspended sediment, evaporation, lake water level variations, reservoir operation, water quality, water demand, flood routing, and streamflow have performed better than ANN, ANFIS, and SVM models (Kisi & Shiri, 2011;Shiri & Kişi, 2011;Kisi et al, 2012;Al-Juboori & Guven, 2016). Also, the superiority of GP and GEP models to other data-driven models in the prediction of groundwater level variations has been demonstrated by several researchers (Fallah-Mehdipour et al, 2013;Fallah-Mehdipour et al, 2014;Kasiviswanathan et al, 2016;Bahmani et al, 2020). Some scholars have studied the impact of climate change on water resources (Abbaspour et al, 2009;Tapoglou et al, 2014;Chang et al, 2015;Salem et al, 2018;Jeihouni et al, 2019a;Razack et al, 2019;Idrizovic et al, 2020).…”

Section: Introductionmentioning

confidence: 98%

Assessment of probable groundwater changes under representative concentration pathway (RCP) scenarios through the wavelet–GEP model

et al. 2021

View full text Add to dashboard Cite

The main objective of this research was to evaluate the possible impact of climate change on groundwater levels in the Tasuj Plain, Iran. To accomplish this, the values of precipitation for a near future period was projected through four General Circulation Models (GCM). Then, the groundwater level variations through the Genetic Expression Programming (GEP) model were evaluated. The projection results indicated that the average annual precipitation with 33.55 mm in the base period would decline to 20.51 mm, 20.11 mm, and 19.14 mm under three Representative Concentration Pathway (RCP) scenarios, namely RCP2.6, RCP4.5, RCP8.5, respectively. The values of the determination coefficient range from 0.92-0.99; the root mean square error between 0.12 and 0.61, and mean absolute error from 0.08 to 0.54 showed that in the forecasting of groundwater level through the GEP model, all models have acceptable results. The evaluation of groundwater level simulation demonstrated that the developed model through the precipitation and previous groundwater level performs better. Prediction of groundwater level for a future period based on climate change scenarios indicated that the average groundwater level in Tasuj Plain at the beginning of the period would experience a gradual reduction and would then increase very slightly to the end of the period. Overall, it was found that the declining precipitation under climate change has no significant impacts on the groundwater level in the Tasuj Plain, and other parameters like anthropogenic activities could be the primary reason for groundwater depletion.

show abstract

Section: Introductionmentioning

confidence: 98%

Assessment of probable groundwater changes under representative concentration pathway (RCP) scenarios through the wavelet–GEP model

et al. 2021

View full text Add to dashboard Cite

show abstract

“…At each internal node, this M5 employs a greedy approach to error minimization, with standard deviation reduction (SDR) determined one node at a time and provided by equation (1). A SDR is calculated at each node for splitting, and then cost complexity pruning (CCP), illustrated below (2), is applied at each leaf node to remove areas that may not be beneficial for the final tree, lowering the number of rules [8,9].…”

Section: M5 Model Treementioning

confidence: 99%

“…Historical geographical characteristics such as vapor pressure, wind speed, humidity, temperature, density, and precipitation are utilized to determine future rainfall projections. Rainfall prediction has been done using a variety of soft computing methods [2]. Effective methods based on ANN and hybrid models have been used to predict rainfall in recent years.…”

Section: Introductionmentioning

confidence: 99%

How M5 Model Trees (M5-MT) on Continuous Data Are Used in Rainfall Prediction: An Experimental Evaluation

Fayaz¹,

Zaman²,

Kaul³

et al. 2022

RIA

View full text Add to dashboard Cite

When using machine learning to predict a class with a continuous numeric value, there are several issues. Only a few machine-learning approaches are capable of doing so, but it remains one of the most difficult jobs to do. In this paper, we show how to use the M5 Model Tree, an approach that can handle continuous numeric data. This method is a stepwise procedure that employs linear functions at the leaf nodes of any created decision tree inducer (such as CART). These M5 model trees provide basic practical formulas such as standard deviation (SD), standard deviation reduction (SDR), cost-complexity pruning (CCP), and so on, which may be simply applied to different benchmark data by another user. This study examines the M5 Model Tree algorithm's capabilities for analysing rainfall data in the Kashmir portion of India's Union Territory of Jammu & Kashmir. One of the best suited models was the M5 model tree, which was built using (70–30) percent training and test ratios, respectively, and predicted an RMSE of 2.593, an MAE of 1.68, and a correlation coefficient (R2) of 0.478. Furthermore, M5 model trees produce models with a minimal number of trails, requiring less computing effort and making them more practical to use.

show abstract

“…These Decision tree algorithms were implemented on mahboobi [15] and Azar [16] [22] proposed a wavelet transform for ground water level simulation using GEP and M5 model tree. These models are combined with the wavelet transform producing the two hybrid models Wavelet GEP (WGEP) and WM5.…”

Section: Literature Reviewmentioning

confidence: 99%

An application of logistic model tree (LMT) algorithm to ameliorate Prediction accuracy of meteorological data

Fayaz¹,

Zaman²,

Butt³

2021

IJATEE

View full text Add to dashboard Cite

Weather forecasting is considered as one of the approaches which are used to check the state of the atmosphere in the future at a specified location. A type of weather forecasting called as rainfall prediction has the highest influence in the farming & agricultural sector and other various sectors like natural disaster management etc. Accurate and timely rainfall prediction is one of the important factors in today's environment. Since the rainfall parameters keep on changing around the world in different places have different factors which are responsible for the rainfall. These parameters include the wind, vapour pressure, humidity at different intervals, temperature, season, evaporation etc. and are considered as the important meteorological parameters for predicting the rainfall in future time [1].*Author for correspondence Decision tree algorithms have been a choice of tool in the machine learning algorithm over the past decade. It was followed up by its modified version, including the more successful one i.e., random forest. However, there has been more precision and accuracy in modified versions of the various decision tree Re search Article

show abstract

Groundwater level simulation using gene expression programming and M5 model tree combined with wavelet transform

Cited by 20 publications

References 45 publications

Assessment of probable groundwater changes under representative concentration pathway (RCP) scenarios through the wavelet–GEP model

Assessment of probable groundwater changes under representative concentration pathway (RCP) scenarios through the wavelet–GEP model

How M5 Model Trees (M5-MT) on Continuous Data Are Used in Rainfall Prediction: An Experimental Evaluation

An application of logistic model tree (LMT) algorithm to ameliorate Prediction accuracy of meteorological data

Contact Info

Product

Resources

About