Climate change impact assessment on groundwater level changes: A study of hybrid model techniques
Abstract One of the most important sources of water supply is groundwater. However, the groundwater level (GWL) is significantly impacted by the global climate change. Therefore, under these more severe climate change conditions, the accurate and simple forecast of farmland GWL is a crucial componen...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2023-06-01
|
| Series: | IET Signal Processing |
| Subjects: | |
| Online Access: | https://doi.org/10.1049/sil2.12227 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832547361741602816 |
|---|---|
| author | Stephen Afrifa Tao Zhang Xin Zhao Peter Appiahene Mensah Samuel Yaw |
| author_facet | Stephen Afrifa Tao Zhang Xin Zhao Peter Appiahene Mensah Samuel Yaw |
| author_sort | Stephen Afrifa |
| collection | DOAJ |
| description | Abstract One of the most important sources of water supply is groundwater. However, the groundwater level (GWL) is significantly impacted by the global climate change. Therefore, under these more severe climate change conditions, the accurate and simple forecast of farmland GWL is a crucial component of agricultural water management. A hybrid model (HM) of Bayesian random forest (BRF), Bayesian support vector machine (BSVM), and Bayesian artificial neural network (BANN) is built in this study. The HM is made up of a Bayesian model averaging (BMA) and three machine learning models: random forest (RF), support vector machine (SVM), and artificial neural network. These three HMs are employed to help automate logical inference and decision‐making in business intelligence for groundwater management. For this purpose, data on 8 separate climatic factors that impact GWL changes in the study area were acquired. Nine distinct farming communities' GWL change data were utilised as the dependent variables for each model fit (community data). The effectiveness of the HM techniques was assessed using the evaluation metrics of mean absolute error (MAE), coefficient of determination (R2), mean absolute percent error (MAPE), and root mean square error (RMSE). The model fit in Suhum had the greatest performance with the highest accuracy (R2 varied from 0.9051 to 0.9679) and the lowest error scores (RMSE ranged from 0.0653 to 0.0727, and MAE ranged from 0.0121 to 0.0541), according to the models' evaluation results. The BRF delivered the greatest results when compared to the two independent HMs, the BSVM and BANN. Future GWL and climatic variable data may be trained using the trained HM techniques to determine the effects of climate change. Farmers, businesses, and civil society organisations might benefit from continuous monitoring of GWL data and education on climate change to help control and prevent excessive deteriorations of global climate change on GWL. |
| format | Article |
| id | doaj-art-667f7a0cf4884493a99e477338e738d6 |
| institution | Kabale University |
| issn | 1751-9675 1751-9683 |
| language | English |
| publishDate | 2023-06-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Signal Processing |
| spelling | doaj-art-667f7a0cf4884493a99e477338e738d62025-02-03T06:45:06ZengWileyIET Signal Processing1751-96751751-96832023-06-01176n/an/a10.1049/sil2.12227Climate change impact assessment on groundwater level changes: A study of hybrid model techniquesStephen Afrifa0Tao Zhang1Xin Zhao2Peter Appiahene3Mensah Samuel Yaw4School of Electrical and Information Engineering Tianjin University Tianjin ChinaSchool of Electrical and Information Engineering Tianjin University Tianjin ChinaSchool of Electrical and Information Engineering Tianjin University Tianjin ChinaDepartment of Computer Science and Informatics University of Energy and Natural Resources Sunyani GhanaSchool of Electrical and Information Engineering Tianjin University Tianjin ChinaAbstract One of the most important sources of water supply is groundwater. However, the groundwater level (GWL) is significantly impacted by the global climate change. Therefore, under these more severe climate change conditions, the accurate and simple forecast of farmland GWL is a crucial component of agricultural water management. A hybrid model (HM) of Bayesian random forest (BRF), Bayesian support vector machine (BSVM), and Bayesian artificial neural network (BANN) is built in this study. The HM is made up of a Bayesian model averaging (BMA) and three machine learning models: random forest (RF), support vector machine (SVM), and artificial neural network. These three HMs are employed to help automate logical inference and decision‐making in business intelligence for groundwater management. For this purpose, data on 8 separate climatic factors that impact GWL changes in the study area were acquired. Nine distinct farming communities' GWL change data were utilised as the dependent variables for each model fit (community data). The effectiveness of the HM techniques was assessed using the evaluation metrics of mean absolute error (MAE), coefficient of determination (R2), mean absolute percent error (MAPE), and root mean square error (RMSE). The model fit in Suhum had the greatest performance with the highest accuracy (R2 varied from 0.9051 to 0.9679) and the lowest error scores (RMSE ranged from 0.0653 to 0.0727, and MAE ranged from 0.0121 to 0.0541), according to the models' evaluation results. The BRF delivered the greatest results when compared to the two independent HMs, the BSVM and BANN. Future GWL and climatic variable data may be trained using the trained HM techniques to determine the effects of climate change. Farmers, businesses, and civil society organisations might benefit from continuous monitoring of GWL data and education on climate change to help control and prevent excessive deteriorations of global climate change on GWL.https://doi.org/10.1049/sil2.12227artificial neural networkfrequency estimationGaussian distributionmathematical analysismaximum likelihood estimationneural nets |
| spellingShingle | Stephen Afrifa Tao Zhang Xin Zhao Peter Appiahene Mensah Samuel Yaw Climate change impact assessment on groundwater level changes: A study of hybrid model techniques IET Signal Processing artificial neural network frequency estimation Gaussian distribution mathematical analysis maximum likelihood estimation neural nets |
| title | Climate change impact assessment on groundwater level changes: A study of hybrid model techniques |
| title_full | Climate change impact assessment on groundwater level changes: A study of hybrid model techniques |
| title_fullStr | Climate change impact assessment on groundwater level changes: A study of hybrid model techniques |
| title_full_unstemmed | Climate change impact assessment on groundwater level changes: A study of hybrid model techniques |
| title_short | Climate change impact assessment on groundwater level changes: A study of hybrid model techniques |
| title_sort | climate change impact assessment on groundwater level changes a study of hybrid model techniques |
| topic | artificial neural network frequency estimation Gaussian distribution mathematical analysis maximum likelihood estimation neural nets |
| url | https://doi.org/10.1049/sil2.12227 |
| work_keys_str_mv | AT stephenafrifa climatechangeimpactassessmentongroundwaterlevelchangesastudyofhybridmodeltechniques AT taozhang climatechangeimpactassessmentongroundwaterlevelchangesastudyofhybridmodeltechniques AT xinzhao climatechangeimpactassessmentongroundwaterlevelchangesastudyofhybridmodeltechniques AT peterappiahene climatechangeimpactassessmentongroundwaterlevelchangesastudyofhybridmodeltechniques AT mensahsamuelyaw climatechangeimpactassessmentongroundwaterlevelchangesastudyofhybridmodeltechniques |