1
|
Katipoğlu OM, Mohammadi B, Keblouti M. Bee-inspired insights: Unleashing the potential of artificial bee colony optimized hybrid neural networks for enhanced groundwater level time series prediction. ENVIRONMENTAL MONITORING AND ASSESSMENT 2024; 196:724. [PMID: 38990407 DOI: 10.1007/s10661-024-12838-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/07/2024] [Accepted: 06/15/2024] [Indexed: 07/12/2024]
Abstract
Analysis of the change in groundwater used as a drinking and irrigation water source is of critical importance in terms of monitoring aquifers, planning water resources, energy production, combating climate change, and agricultural production. Therefore, it is necessary to model groundwater level (GWL) fluctuations to monitor and predict groundwater storage. Artificial intelligence-based models in water resource management have become prevalent due to their proven success in hydrological studies. This study proposed a hybrid model that combines the artificial neural network (ANN) and the artificial bee colony optimization (ABC) algorithm, along with the ensemble empirical mode decomposition (EEMD) and the local mean decomposition (LMD) techniques, to model groundwater levels in Erzurum province, Türkiye. GWL estimation results were evaluated with mean square error (MSE), coefficient of determination (R2), and residual sum of squares (RSS) and visually with violin, scatter, and time series plot. The study results indicated that the EEMD-ABC-ANN hybrid model was superior to other models in estimating GWL, with R2 values ranging from 0.91 to 0.99 and MSE values ranging from 0.004 to 0.07. It has also been revealed that promising GWL predictions can be made with previous GWL data.
Collapse
Affiliation(s)
- Okan Mert Katipoğlu
- Department of Civil Engineering, Faculty of Engineering and Architecture, Erzincan Binali Yıldırım University, Erzincan, Turkey.
| | - Babak Mohammadi
- Department of Physical Geography and Ecosystem Science, Lund University, Sölvegatan 12, 223 62, Lund, Sweden
| | - Mehdi Keblouti
- Department of Civil Engineering and Hydraulic, Institute of Sciences and Technology, Abdelhafid Boussouf University Center, RP 26, 43000, Mila, Algeria
| |
Collapse
|
2
|
Saroughi M, Mirzania E, Achite M, Katipoğlu OM, Al-Ansari N, Vishwakarma DK, Chung IM, Alreshidi MA, Yadav KK. 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). Heliyon 2024; 10:e29006. [PMID: 38601575 PMCID: PMC11004570 DOI: 10.1016/j.heliyon.2024.e29006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2023] [Revised: 03/25/2024] [Accepted: 03/28/2024] [Indexed: 04/12/2024] Open
Abstract
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).
Collapse
Affiliation(s)
- Mohsen Saroughi
- Department of Irrigation and Reclamation Engineering, Faculty of Agricultural Engineering and Technology, College of Agriculture and Natural Resources, University of Tehran, Karaj, Iran
| | - Ehsan Mirzania
- Department of Water Engineering, University of Tabriz, Tabriz, Iran
| | - Mohammed Achite
- Faculty of Nature and Life Sciences, Laboratory of Water and Environment, Hassiba Benbouali University of Chlef, Chlef, 02180, Algeria
| | - Okan Mert Katipoğlu
- Department of Civil Engineering, Erzincan Binali Yıldırım University, Erzincan, Turkey
| | - Nadhir Al-Ansari
- Department of Civil, Environmental, and Natural Resources Engineering, Lulea University of Technology, 97187, Lulea, Sweden
| | - Dinesh Kumar Vishwakarma
- Department of Irrigation and Drainage Engineering, Govind Ballabh Pant University of Agriculture and Technology, Pantnagar, Udham Singh Nagar, Uttarakhand, 263145, India
| | - Il-Moon Chung
- Department of Water Resources and River Research, Korea Institute of Civil Engineering and Building Technology, Goyang-si, 10223, Republic of Korea
| | | | - Krishna Kumar Yadav
- Faculty of Science and Technology, Madhyanchal Professional University, Ratibad, Bhopal, 462044, India
- Environmental and Atmospheric Sciences Research Group, Scientific Research Center, Al-Ayen University, Thi-Qar, Nasiriyah, 64001, Iraq
| |
Collapse
|
3
|
Cha GW, Choi SH, Hong WH, Park CW. Development of Machine Learning Model for Prediction of Demolition Waste Generation Rate of Buildings in Redevelopment Areas. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 20:107. [PMID: 36612429 PMCID: PMC9819715 DOI: 10.3390/ijerph20010107] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/24/2022] [Revised: 12/14/2022] [Accepted: 12/17/2022] [Indexed: 06/17/2023]
Abstract
Owing to a rapid increase in waste, waste management has become essential, for which waste generation (WG) information has been effectively utilized. Various studies have recently focused on the development of reliable predictive models by applying artificial intelligence to the construction and prediction of WG information. In this study, research was conducted on the development of machine learning (ML) models for predicting the demolition waste generation rate (DWGR) of buildings in redevelopment areas in South Korea. Various ML algorithms (i.e., artificial neural network (ANN), K-nearest neighbors (KNN), linear regression (LR), random forest (RF), and support vector machine (SVM)) were applied to the development of an optimal predictive model, and the main hyper parameters (HPs) for each algorithm were optimized. The results suggest that ANN-ReLu (coefficient of determination (R2) 0.900, the ratio of percent deviation (RPD) 3.16), SVM-polynomial (R2 0.889, RPD 3.00), and ANN-logistic (R2 0.883, RPD 2.92) are the best ML models for predicting the DWGR. They showed average errors of 7.3%, 7.4%, and 7.5%, respectively, compared to the average observed values, confirming the accurate predictive performance, and in the uncertainty analysis, the d-factor of the models appeared less than 1, showing that the presented models are reliable. Through a comparison with ML algorithms and HPs applied in previous related studies, the results herein also showed that the selection of various ML algorithms and HPs is important in developing optimal ML models for WG management.
Collapse
Affiliation(s)
- Gi-Wook Cha
- School of Science and Technology Acceleration Engineering, Kyungpook National University, Daegu 41566, Republic of Korea
| | - Se-Hyu Choi
- School of Architectural, Civil, Environmental and Energy Engineering, Kyungpook National University, Daegu 41566, Republic of Korea
| | - Won-Hwa Hong
- School of Architectural, Civil, Environmental and Energy Engineering, Kyungpook National University, Daegu 41566, Republic of Korea
| | - Choon-Wook Park
- Industry Academic Cooperation Foundation, Kyungpook National University, Daegu 41566, Republic of Korea
| |
Collapse
|
4
|
Sheng D, Meng X, Wen X, Wu J, Yu H, Wu M. Contamination characteristics, source identification, and source-specific health risks of heavy metal(loid)s in groundwater of an arid oasis region in Northwest China. THE SCIENCE OF THE TOTAL ENVIRONMENT 2022; 841:156733. [PMID: 35716754 DOI: 10.1016/j.scitotenv.2022.156733] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/19/2022] [Revised: 05/28/2022] [Accepted: 06/12/2022] [Indexed: 05/09/2023]
Abstract
Heavy metal(loid)s accumulation in groundwater has posed serious ecological and health concerns worldwide. Source-specific risk apportionment is crucial to prevent and control potential heavy metal(loid)s pollution in groundwater. However, there is very limited comprehensive information on the health risk apportionment for groundwater heavy metal(loid)s in arid regions. Thus, the Zhangye Basin, a typical arid oasis region in Northwest China, was selected to investigate the contamination characteristics, possible pollution sources, and source-specific health risks of groundwater heavy metal(loid)s. The heavy metal pollution index (HPI), the Nemerow index (NI), and the contamination degree (CD) were adopted to assess the pollution level of heavy metal(loid)s; then source-specific health risk was apportioned integrating the absolute principal component scores-multiple linear regression (APCS-MLR) with health risk assessment. Noticeable accumulation of Mn, Fe, and As was observed in this region with especially Fe/As in 12.68%/2.11% of the samples revealing significant enrichment. Approximately 3.5% of the groundwater samples caused moderate or higher pollution level based on the HPI. The APCS-MLR model was more physically applicable for the current research than the positive matrix factorization (PMF) model. Industrial-agricultural activity factor (12.56%) was the major source of non-cancer (infants: 59.15%, children: 64.87%, teens: 64.06%, adults: 64.02%) and cancer risks (infants: 77.36%, children: 77.35%, teens: 77.40%, adults: 77.41%). Industrial-agricultural activities should be given priority to control health risks of heavy metal(loid)s in groundwater. These findings provide fundamental and significant information for mitigating health risks caused by heavy metal(loid)s in groundwater of typical arid oasis regions by controlling priority sources.
Collapse
Affiliation(s)
- Danrui Sheng
- Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou, Gansu 730000, People's Republic of China; University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
| | - Xianhong Meng
- Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou, Gansu 730000, People's Republic of China
| | - Xiaohu Wen
- Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou, Gansu 730000, People's Republic of China.
| | - Jun Wu
- Yantai Research Institute, Harbin Engineering University, Yantai, Shandong 264006, People's Republic of China.
| | - Haijiao Yu
- School of Resources and Environment, Linyi University, Linyi, Shandong 276005, People's Republic of China
| | - Min Wu
- College of Geomatics, Xi'an University of Science and Technology, Xi'an, Shaanxi 710054, People's Republic of China
| |
Collapse
|