Convolutional Neural Network- ANN- E (Tanh): A New Deep Learning Model for Predicting Rainfall

Nia, Mahdie Afshari; Panahi, Farhad; Ehteram, Mohammad

doi:10.1007/s11269-023-03454-8

Cited by 11 publications

(3 citation statements)

References 33 publications

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“…Furthermore, the performance of the MLR model depends on the correct selection of inputs [10]. In recent years, researchers have used hybrid models to overcome the limitations of classical machine learning models [14]. By combining multiple models, a hybrid model improves prediction accuracy and overcomes the shortcomings of traditional models [14].…”

Section: References Results Discussionmentioning

confidence: 99%

“…In recent years, researchers have used hybrid models to overcome the limitations of classical machine learning models [14]. By combining multiple models, a hybrid model improves prediction accuracy and overcomes the shortcomings of traditional models [14]. We present an innovative model for predicting GWLs that addresses the limitations of the MLR model.…”

Section: References Results Discussionmentioning

confidence: 99%

“…A linear-LSTM model was used to predict groundwater levels (GWL) and the hybrid models enhanced the linear models' performance. Afshari Nia et al [14] stated that the CNN model played a key role in extracting spatial features from hydrological time series data.…”

Section: References Results Discussionmentioning

confidence: 99%

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A Developed Multiple Linear Regression (MLR) Model for Monthly Groundwater Level Prediction

Ehteram,

Banadkooki

2023

Water

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Groundwater level (GLW) prediction is essential for monitoring water resources. Our study introduces a novel model called convolutional neural network (CNN)–long short-term memory neural network (LSTM)–Multiple linear regression (MLR) for groundwater level prediction. We combine two deep learning models with the MLR model to predict GWL and overcome the limitations of the MLR model. The current paper has several innovations. Our study develops an advanced hybrid model for predicting groundwater levels (GWLs). The study also presents a novel feature selection method for selecting optimal input scenarios. Finally, an advanced method is developed to examine the impact of inputs and model parameters on output uncertainty. The current paper introduces the gannet optimization algorithm (GOA) for choosing the optimal input scenario. A CNN-LSTM-MLR model (CLM), CNN, LSTM, MLR model, CNN-MLR model (CNM), LSTM-MLR model (LSM), and CNN-LSTM model (CNL) were built to predict one-month-ahead GWLs using climate data and lagged GWL data. Output uncertainty was also decomposed into parameter uncertainty (PU) and input uncertainty (IU) using the analysis of variance (ANOVA) method. Based on our findings, the CLM model can successfully predict GWLs, reduce the uncertainty of CNN, LSTM, and MLR models, and extract spatial and temporal features. Based on the study’s findings, the combination of linear models and deep learning models can improve the performance of linear models in predicting outcomes. The GOA method can also contribute to feature selection and input selection. The study findings indicated that the CLM model improved the training Nash–Sutcliffe efficiency coefficient (NSE) of the CNL, LSM, CNM, LSTM, CNN, and MLR models by 6.12%, 9.12%, 12%, 18%, 22%, and 30%, respectively. The width intervals (WIs) of the CLM, CNL, LSM, and CNM models were 0.03, 0.04, 0.07, and, 0.12, respectively, based on IU. The WIs of the CLM, CNL, LSM, and CNM models were 0.05, 0.06, 0.09, and 0.14, respectively, based on PU. Our study proposes the CLM model as a reliable model for predicting GWLs in different basins.

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Section: References Results Discussionmentioning

confidence: 99%

Section: References Results Discussionmentioning

confidence: 99%

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A Developed Multiple Linear Regression (MLR) Model for Monthly Groundwater Level Prediction

Ehteram,

Banadkooki

2023

Water

Self Cite

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Convolutional Neural Network -Support Vector Machine Model-Gaussian Process Regression: A New Machine Model for Predicting Monthly and Daily Rainfall

Ehteram

Ahmed

Khozani

et al. 2023

Water Resour Manage

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Evaluation of Feature Selection Methods in Estimation of Precipitation Based on Deep Learning Artificial Neural Networks

Sattari,

Avram,

Apaydin

et al. 2023

Water Resour Manage

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Precipitation is the most important element of the water cycle and an indispensable element of water resources management. This paper’s aim is to model the monthly precipitation in 8 precipitation observation stations in the province of Hamadan, Iran. The effects and role of different feature weights pre-processing methods (Weight by deviation, Weight by PCA, Weight by correlation and Weight by Support Vector Machine) on artificial intelligence modeling were investigated. Deep learning method based on a multi-layer feed-forward artificial neural network that is trained with Stochastic Gradient Descent using back-propagation (DL-SGD) and Convolutional Neural Networks (CNN) modelling were applied. The precipitation of each station is modeled using the precipitation values of the other stations. The best result, among all scenarios, at the Vasaj station according to the DL-SGD method (CC = 0.9845, NS = 0.9543 and RMSE = 10.4169 mm) and at the Varayineh station according to the CNN method (CC = 0.9679, NS = 0.9362 and RMSE = 16.0988 mm) were estimated.

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Convolutional Neural Network- ANN- E (Tanh): A New Deep Learning Model for Predicting Rainfall

Cited by 11 publications

References 33 publications

A Developed Multiple Linear Regression (MLR) Model for Monthly Groundwater Level Prediction

A Developed Multiple Linear Regression (MLR) Model for Monthly Groundwater Level Prediction

Convolutional Neural Network -Support Vector Machine Model-Gaussian Process Regression: A New Machine Model for Predicting Monthly and Daily Rainfall

Evaluation of Feature Selection Methods in Estimation of Precipitation Based on Deep Learning Artificial Neural Networks

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