Combining autoregressive integrated moving average with Long Short-Term Memory neural network and optimisation algorithms for predicting ground water level
Brain; Forecasting; Genetic algorithms; Groundwater resources; Light modulators; Long short-term memory; Soil conservation; Time series; Water conservation; Water levels; Water management; Auto regressive integrated moving average models; Auto-regressive integrated moving average model model; Ground...
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my.uniten.dspace-268792023-05-29T17:37:29Z Combining autoregressive integrated moving average with Long Short-Term Memory neural network and optimisation algorithms for predicting ground water level Sheikh Khozani Z. Barzegari Banadkooki F. Ehteram M. Najah Ahmed A. El-Shafie A. 57185668800 57491527900 57113510800 57214837520 16068189400 Brain; Forecasting; Genetic algorithms; Groundwater resources; Light modulators; Long short-term memory; Soil conservation; Time series; Water conservation; Water levels; Water management; Auto regressive integrated moving average models; Auto-regressive integrated moving average model model; Ground water level; Long short-term memory model; Memory modeling; Non linear; Optimisations; Optimization algorithms; Salp swarms; Times series; Groundwater The groundwater resources are the essential sources for irrigation and agriculture management. Forecasting groundwater levels (GWL) for the current and future periods is an essential topic of watershed management. The prediction of GWL helps prevent overexploitation. The Auto-Regressive Integrated Moving Average model (ARIMA) is a widely known linear statistical model. One of the drawbacks of the ARIMA models is that they may not capture all existing patterns, such as non-linear parts of time series. This article introduces a new hybrid model, namely the ARIMA-Long Short-Term Memory (LSTM) neural network, to capture the linear and non-linear components of a GWL time series in the Yazd-Ardekn Plain in Iran. This study applied the ARIMA-LSTM in forecasting three-, six-, and nine-month-ahead GWL. To determine the hyperparameters of the LSTM algorithm, the Salp Swarm Algorithm (SSA), sine cosine optimisation algorithm (SCOA), particle swarm optimisation algorithm (PSOA), and genetic algorithm (GA) were coupled with the LSTM model. Two different scenarios were devised to introduce new input combinations. In the first scenario, the residual values of the ARIMA model and the lagged GWL data were inserted into hybrid and standalone LSTM models for forecasting the GWL. In the second scenario, the summation of the outputs of the ARIMA and LSTM models gave the final outputs. In terms of the content of three-month-ahead GWL predictions for the second scenario, the ARIMA-LSTM-SSA produced better results than the ARIMA-LSTM-SCOA, ARIMA-LSTM-PSOA, ARIMA-LSTM-GA, ATIMA-LSTM, LSTM, and ARIMA algorithms, which had lower mean absolute error values (MAE) of 5%, 9.4%, 15%, 38%, 42%, and 47%, respectively. However, the general results indicated that an increased forecasting horizon reduced the accuracy of the models. The new hybrid ARIMA-LSTM- SSA model was highly capable of forecasting other hydrological variables for capturing non-linear and linear elements of the time series. � 2022 Elsevier Ltd Final 2023-05-29T09:37:29Z 2023-05-29T09:37:29Z 2022 Article 10.1016/j.jclepro.2022.131224 2-s2.0-85126532631 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85126532631&doi=10.1016%2fj.jclepro.2022.131224&partnerID=40&md5=21b4c5b7280cb03b9d4f2f05392b1649 https://irepository.uniten.edu.my/handle/123456789/26879 348 131224 Elsevier Ltd Scopus |
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Brain; Forecasting; Genetic algorithms; Groundwater resources; Light modulators; Long short-term memory; Soil conservation; Time series; Water conservation; Water levels; Water management; Auto regressive integrated moving average models; Auto-regressive integrated moving average model model; Ground water level; Long short-term memory model; Memory modeling; Non linear; Optimisations; Optimization algorithms; Salp swarms; Times series; Groundwater |
| author2 |
57185668800 |
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57185668800 Sheikh Khozani Z. Barzegari Banadkooki F. Ehteram M. Najah Ahmed A. El-Shafie A. |
| format |
Article |
| author |
Sheikh Khozani Z. Barzegari Banadkooki F. Ehteram M. Najah Ahmed A. El-Shafie A. |
| spellingShingle |
Sheikh Khozani Z. Barzegari Banadkooki F. Ehteram M. Najah Ahmed A. El-Shafie A. Combining autoregressive integrated moving average with Long Short-Term Memory neural network and optimisation algorithms for predicting ground water level |
| author_sort |
Sheikh Khozani Z. |
| title |
Combining autoregressive integrated moving average with Long Short-Term Memory neural network and optimisation algorithms for predicting ground water level |
| title_short |
Combining autoregressive integrated moving average with Long Short-Term Memory neural network and optimisation algorithms for predicting ground water level |
| title_full |
Combining autoregressive integrated moving average with Long Short-Term Memory neural network and optimisation algorithms for predicting ground water level |
| title_fullStr |
Combining autoregressive integrated moving average with Long Short-Term Memory neural network and optimisation algorithms for predicting ground water level |
| title_full_unstemmed |
Combining autoregressive integrated moving average with Long Short-Term Memory neural network and optimisation algorithms for predicting ground water level |
| title_sort |
combining autoregressive integrated moving average with long short-term memory neural network and optimisation algorithms for predicting ground water level |
| publisher |
Elsevier Ltd |
| publishDate |
2023 |
| _version_ |
1806428175366881280 |
| score |
13.212979 |