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)

被引：0

作者：

Saroughi, Mohsen ^{[1
]}

Mirzania, Ehsan ^{[2
]}

Achite, Mohammed ^{[3
]}

Katipoglu, Okan Mert ^{[4
]}

Al-Ansari, Nadhir ^{[5
]}

Vishwakarma, Dinesh Kumar ^{[6
]}

Chung, Il-Moon ^{[7
]}

Alreshidi, Maha Awjan ^{[8
]}

Yadav, Krishna Kumar ^{[9
,10
]}

机构：

[1] Univ Tehran, Coll Agr & Nat Resources, Fac Agr Engn & Technol, Dept Irrigat & Reclamat Engn, Karaj, Iran

[2] Univ Tabriz, Dept Water Engn, Tabriz, Iran

[3] Hassiba Benbouali Univ Chlef, Fac Nat & Life Sci, Lab Water & Environm, Chlef 02180, Algeria

[4] Erzincan Binali Yildirim Univ, Dept Civil Engn, Erzincan, Turkiye

[5] Lulea Univ Technol, Dept Civil Environm & Nat Resources Engn, S-97187 Lulea, Sweden

[6] Govind Ballabh Pant Univ Agr & Technol, Dept Irrigat & Drainage Engn, Udham Singh Nagar 263145, Uttarakhand, India

[7] Korea Inst Civil Engn & Bldg Technol, Dept Water Resources & River Res, Goyang Si 10223, South Korea

[8] Univ Hail, Dept Chem, Hail 81441, Saudi Arabia

[9] Madhyanchal Profess Univ, Fac Sci & Technol, Bhopal 462044, India

[10] Al Ayen Univ, Sci Res Ctr, Environm & Atmospher Sci Res Grp, Thi Qar 64001, Nasiriyah, Iraq

来源：

HELIYON | 2024年 / 10卷 / 07期

关键词：

Artificial intelligence; Deep learning; Groundwater level; Hybrid algorithm; Machine learning; OPTIMIZATION ALGORITHMS; WAVELET ANALYSIS; NEURAL-NETWORKS; SVM; RUNOFF; FLUCTUATIONS; TEMPERATURE; SENSITIVITY; SIMULATION; REGRESSION;

D O I：

10.1016/j.heliyon.2024.e29006

中图分类号：

O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];

学科分类号：

07 ; 0710 ; 09 ;

摘要：

The estimation of groundwater levels is crucial and an important step in ensuring sustainable management of water resources. In this paper, selected piezometers of the Hamedan-Bahar plain located in west of Iran. The main objective of this study is to compare effect of various preprocessing methods on input data for different artificial intelligence (AI) models to predict groundwater levels (GWLs). The observed GWL, evaporation, precipitation, and temperature were used as input variables in the AI algorithms. Firstly, 126 method of data pre-processing was done by python programming which are classified into three classes: 1- statistical methods, 2wavelet transform methods and 3- decomposition methods; later, various pre-processed data used by four types of widely used AI models with different kernels, which includes: Support Vector Machine (SVR), Artificial Neural Network (ANN), Long -Short Term memory (LSTM), and Pelican Optimization Algorithm (POA) - Artificial Neural Network (POA-ANN) are classified into three classes: 1- machine learning (SVR and ANN), 2deep learning (LSTM) and 3- hybrid -ML (POAANN) models, to predict groundwater levels (GWLs). Akaike Information Criterion (AIC) were used to evaluate and validate the predictive accuracy of algorithms. According to the results, based on summation (train and test phases) of AIC value of 1778 models, average of AIC values for ML, DL, hybrid -ML classes, was decreased to -25.3%, -29.6% and -57.8%, respectively. Therefore, the results showed that all data pre-processing methods do not lead to improvement of prediction accuracy, and they should be selected very carefully by trial and error. In conclusion, wavelet -ANN model with daubechies 13 and 25 neurons (db13_ANN_25) is the best model to predict GWL that has -204.9 value for AIC which has grown by 5.23% (-194.7) compared to the state without any pre-processing method (ANN_Relu_25).

引用

页数：25