Saroughi Mohsen, Mirzania Ehsan, Achite Mohammed, Katipoğlu Okan Mert, Al-Ansari Nadhir, Vishwakarma Dinesh Kumar, Chung Il-Moon, Alreshidi Maha Awjan, Yadav Krishna Kumar
Department of Irrigation and Reclamation Engineering, Faculty of Agricultural Engineering and Technology, College of Agriculture and Natural Resources, University of Tehran, Karaj, Iran.
Department of Water Engineering, University of Tabriz, Tabriz, Iran.
Heliyon. 2024 Apr 2;10(7):e29006. doi: 10.1016/j.heliyon.2024.e29006. eCollection 2024 Apr 15.
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 pre-processing 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, 2- wavelet 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), 2- deep learning (LSTM) and 3- hybrid-ML (POA-ANN) 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).
地下水位的估算至关重要，是确保水资源可持续管理的重要一步。本文选取了位于伊朗西部的哈马丹 - 巴哈尔平原的测压管。本研究的主要目的是比较各种预处理方法对不同人工智能（AI）模型输入数据的影响，以预测地下水位（GWLs）。观测到的地下水位、蒸发量、降水量和温度被用作AI算法的输入变量。首先，通过Python编程完成了126种数据预处理方法，这些方法分为三类：1 - 统计方法，2 - 小波变换方法和3 - 分解方法；之后，将四种具有不同内核的广泛使用的AI模型所使用的各种预处理数据，包括：支持向量机（SVR）、人工神经网络（ANN）、长短期记忆（LSTM）和鹈鹕优化算法（POA） - 人工神经网络（POA - ANN）分为三类：1 - 机器学习（SVR和ANN），2 - 深度学习（LSTM）和3 - 混合机器学习（POA - ANN）模型，以预测地下水位（GWLs）。使用赤池信息准则（AIC）来评估和验证算法的预测准确性。根据结果，基于1778个模型的AIC值总和（训练和测试阶段），ML、DL、混合ML类的AIC值平均值分别下降到 - 25.3%、 - 29.6%和 - 57.8%。因此，结果表明并非所有数据预处理方法都能提高预测准确性，应通过反复试验非常谨慎地选择。总之，具有Daubechies 13和25个神经元的小波 - ANN模型（db13_ANN_25）是预测GWL的最佳模型，其AIC值为 - 204.9，与没有任何预处理方法的状态（ANN_Relu_25）相比增长了5.23%（ - 194.7）。
