Prediction of groundwater table and drought analysis; a new hybridization strategy based on bi-directional long short-term model and the Harris hawk optimization algorithm

Farzin, Saeed; Anaraki, Mahdi Valikhan; Naeimi, Mohammad; Zandifar, Samira

doi:10.2166/wcc.2022.066

Cited by 28 publications

(9 citation statements)

References 47 publications

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“…Morshed-Bozorgdel et al (2022) find that a two-level ensemble of ML algorithms captures the high variation in wind speed. Farzin et al (2022) use a bi-directional long short-term model (BLSTM) and the Harris hawk optimization (HHO) algorithm to optimize the model's hyperparameters to forecast the underground water table in Iran. They find that their BLSTM-HHO method is superior to benchmark methods regarding assessment criteria such as mean absolute error (MAE), root mean square error (RMSE), and forecast variance.…”

Section: Literature Review Of Forecasting Modelsmentioning

confidence: 99%

Forecasting carbon dioxide emissions: application of a novel two-stage procedure based on machine learning models

Wang

Jin

2023

Journal of Water and Climate Change

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Accurate forecast of carbon dioxide (CO2) emissions plays a significant role in China's carbon peaking and carbon neutrality policies. A novel two-stage forecast procedure based on support vector regression (SVR), random forest (RF), ridge regression (Ridge), and artificial neural network (ANN) is proposed and evaluated by comparing it with the single-stage forecast procedure. Nine independent variables’ data (study period: 1985–2020) are used to forecast the CO2 emissions in China. Our results reveal that, when the time gap, h increases from 1 to 8, the average root mean squared error (RMSE) and mean absolute error (MAE) of SVR–SVR, SVR–RF, SVR–Ridge, and SVR–ANN are almost uniformly lower than errors arising from their single-stage version, respectively. Among these two-stage models, SVR–ANN exhibits the lowest forecast errors, whereas SVR–RF admits the highest. The mean percentage decrease in forecast errors of SVR–SVR vs. SVR, SVR–RF vs. RF, SVR–Ridge vs. Ridge, and SVR–ANN vs. ANN are 36.06, 5.98, 43.05, and 14.81 for RMSE, and 36.06, 6.91, 43.27, and 15.35 for MAE. Our two-stage procedure is also suitable to forecast other variables, such as fossil fuel and renewable energy consumption.

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Section: Literature Review Of Forecasting Modelsmentioning

confidence: 99%

Forecasting carbon dioxide emissions: application of a novel two-stage procedure based on machine learning models

Wang

Jin

2023

Journal of Water and Climate Change

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“…The Bi-LSTM is a type of neural network where the performance varies depending on the number of nodes in the hidden layer. To select the appropriate number of hidden nodes, we performed forecasts by increasing the number of nodes in the hidden layer from 16 (2 4 ) to 128 (2 7 ) in a doubling fashion [42,43]. The optimal number of hidden nodes was determined as the one that resulted in the lowest root mean square error (RMSE).…”

Section: Bidirectional Long Short-term Memorymentioning

confidence: 99%

PM2.5 Concentration Forecasting Using Weighted Bi-LSTM and Random Forest Feature Importance-Based Feature Selection

Kim

Jung

et al. 2023

Atmosphere

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Particulate matter (PM) in the air can cause various health problems and diseases in humans. In particular, the smaller size of PM2.5 enable them to penetrate deep into the lungs, causing severe health impacts. Exposure to PM2.5 can result in respiratory, cardiovascular, and allergic diseases, and prolonged exposure has also been linked to an increased risk of cancer, including lung cancer. Therefore, forecasting the PM2.5 concentration in the surrounding is crucial for preventing these adverse health effects. This paper proposes a method for forecasting the PM2.5 concentration after 1 h using bidirectional long short-term memory (Bi-LSTM). The proposed method involves selecting input variables based on the feature importance calculated by random forest, classifying the data to assign weight variables to reduce bias, and forecasting the PM2.5 concentration using Bi-LSTM. To compare the performance of the proposed method, two case studies were conducted. First, a comparison of forecasting performance according to preprocessing. Second, forecasting performance between deep learning (long short-term memory, gated recurrent unit, and Bi-LSTM) and conventional machine learning models (multi-layer perceptron, support vector machine, decision tree, and random forest). In case study 1, The proposed method shows that the performance indices (RMSE: 3.98%p, MAE: 5.87%p, RRMSE: 3.96%p, and R2:0.72%p) are improved because weights are given according to the input variables before the forecasting is performed. In case study 2, we show that Bi-LSTM, which considers both directions (forward and backward), can effectively forecast when compared to conventional models (RMSE: 2.70, MAE: 0.84, RRMSE: 1.97, R2: 0.16). Therefore, it is shown that the proposed method can effectively forecast PM2.5 even if the data in the high-concentration section is insufficient.

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“…Over the past 20 years, the use of AI approaches has expanded across various fields. In the context of simulating and predicting water quality, various machine learning algorithms, including adaptive boosting (Adaboost) [24], gradient boosting (GBM) [25], extreme gradient boosting (XGBoost) [26], decision tree (DT) [27], extra trees (ExT) [28], radial basis function (RBF) [29], artificial neural network (ANN) [29,30], random forest (RF) [31], deep feed-forward neural network (DFNN) [23], and convolutional neural network (CNN) [22] have been examined for their efficacy. However, researchers still face the challenge of determining the most suitable techniques for a given problem.…”

Section: Introductionmentioning

confidence: 99%

Coupling Machine and Deep Learning with Explainable Artificial Intelligence for Improving Prediction of Groundwater Quality and Decision-Making in Arid Region, Saudi Arabia

Alshehri

Rahman

2023

Water

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Recently, machine learning (ML) and deep learning (DL) models based on artificial intelligence (AI) have emerged as fast and reliable tools for predicting water quality index (WQI) in various regions worldwide. In this study, we propose a novel stacking framework based on DL models for WQI prediction, employing a convolutional neural network (CNN) model. Additionally, we introduce explainable AI (XAI) through XGBoost-based SHAP (SHapley Additive exPlanations) values to gain valuable insights that can enhance decision-making strategies in water management. Our findings demonstrate that the stacking model achieves the highest accuracy in WQI prediction (R2: 0.99, MAPE: 15.99%), outperforming the CNN model (R2: 0.90, MAPE: 58.97%). Although the CNN model shows a relatively high R2 value, other statistical measures indicate that it is actually the worst-performing model among the five tested. This discrepancy may be attributed to the limited training data available for the CNN model. Furthermore, the application of explainable AI (XAI) techniques, specifically XGBoost-based SHAP values, allows us to gain deep insights into the models and extract valuable information for water management purposes. The SHAP values and interaction plot reveal that elevated levels of total dissolved solids (TDS), zinc, and electrical conductivity (EC) are the primary drivers of poor water quality. These parameters exhibit a nonlinear relationship with the water quality index, implying that even minor increases in their concentrations can significantly impact water quality. Overall, this study presents a comprehensive and integrated approach to water management, emphasizing the need for collaborative efforts among all stakeholders to mitigate pollution levels and uphold water quality. By leveraging AI and XAI, our proposed framework not only provides a powerful tool for accurate WQI prediction but also offers deep insights into the models, enabling informed decision-making in water management strategies.

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Prediction of groundwater table and drought analysis; a new hybridization strategy based on bi-directional long short-term model and the Harris hawk optimization algorithm

Cited by 28 publications

References 47 publications

Forecasting carbon dioxide emissions: application of a novel two-stage procedure based on machine learning models

Forecasting carbon dioxide emissions: application of a novel two-stage procedure based on machine learning models

PM2.5 Concentration Forecasting Using Weighted Bi-LSTM and Random Forest Feature Importance-Based Feature Selection

Coupling Machine and Deep Learning with Explainable Artificial Intelligence for Improving Prediction of Groundwater Quality and Decision-Making in Arid Region, Saudi Arabia

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