CNN-Bi LSTM Neural Network for Simulating Groundwater Level

Ali, Ahmed Shakir Ali; Ebrahimi, Saman; Ashiq, Muhammad Masood; Alasta, M. Sobhi; Azari, Babak

doi:10.52547/crpase.8.1.2748

Cited by 12 publications

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“…Compared to RNN, LSTM produced more successful forecasting results both in the whole time series and in the time series cases only during storm periods. Ali et al (2022) analyzed hourly GWL changes using the CNN-BiLSTM hybrid model. As a result, they obtained a high R 2 = 0.917 in the training phase and a lower R 2 = 0.632 in the testing phase.…”

Section: Discussionmentioning

confidence: 99%

“…Vadiati et al (2022) evaluated ANN, SVM, ANFIS and FL algorithms for monitoring the GWL change in the Tehran-Karaj plain and used monthly precipitation, temperature, evapotranspiration, river discharge and maximum of three-time delays for groundwater data as input. Although the prediction capacity of all models is high, the most successful model is ANFIS Ali et al (2022). established convolutional neural network (CNN) -bidirectional LSTM model as a hybrid neural network model to monitor the hourly change of GWL.…”

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Mühendislik Alanında Akademik Analiz ve Tartışmalar

2023

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Mühendislik bilim, teknoloji ve matematiği birbirine bağlamayı amaçlar ve bir disiplin olarak binlerce yıldır uygulanmaktadır. Mühendisler, hücre zarlarından inşaat ve protezlere, motor ve taşıma verimliliğinin iyileştirilmesine ve yenilenebilir enerji kaynaklarının geliştirilmesine kadar her konuda çalışırlar. Mühendislik, tekerleğin icadına (ve ötesine) kadar uzanıyor olsa da, terimin kendisi, bir "mühendis"in mancınık ve diğer "kuşatma makineleri" gibi askeri motorlar inşa eden kişi anlamına geldiği 14. yüzyıla kadar uzanmaktadır. Mühendislik, askeri uygulamaların ötesinde gelişerek köprüler ve binalar gibi sivil yapılara uygulanmaya başlanmıştır. Bu da onu orijinal askeri mühendislik alanından ayırmak için inşaat mühendisliği teriminin yaratılmasına yol açmıştır. Bu kitapta mühendislik bilimleri incelenmiştir.

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Mühendislik Alanında Akademik Analiz ve Tartışmalar

2023

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“…The algorithm used to display analysis performance uses Convolutional Neural Network (CNN) and Bidirectional Long Short Term Memory (Bi-LSTM), this method is used to determine the influence of opinion data from Twitter on the J&T Express expedition delivery service which has been carried out text preprocessing and data text preprocessing was not carried out with IndoBert parameters which varied in the learning rate [17].…”

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confidence: 99%

Text Classification Using Indobert Fine-Tuning Modeling With Convolutional Neural Network and Bi-LSTM

Zevana,

Riana

2024

J. Tek. Inform. (JUTIF)

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The technological advancements in goods delivery facilities have been increasing year by year in tandem with the growing online trade, which necessitates delivery services to fulfill the transactional process between sellers and buyers. Since 2000, top brand awards have often conducted official survey analyses to provide comparisons of goods or services, one of which includes delivery services. However, the survey rankings based on public opinion are less accurate due to users of delivery services and service companies being unaware of the specific success factors and weaknesses in their services. The aim of this research is to analyze the comparison of text mining using the Indonesian language transformation method, IndoBert. The algorithm utilized to demonstrate analysis performance employs Convolutional Neural Network (CNN) and Bidirectional Long Short-Term Memory (Bi-LSTM). This method is utilized to determine the impact of opinion data from Twitter on the J&T Express expedition delivery service, incorporating both text preprocessing and data without text preprocessing. The IndoBert parameters vary in the learning rate section based on four factors: price, time, returns, and others. The research data consisted of 2525 comments from Twitter users regarding the delivery service spanning from January 1, 2021, to March 31, 2023. The testing showed that Bi-LSTM with text preprocessing performed 2% higher, achieving 79% at a learning rate of 1x10-6, compared to without text preprocessing at the same learning rate, which reached 77%. Additionally, CNN outperformed by 3% with a rate of 83%, compared to 80% without text preprocessing at a learning rate of 1x10-5. The highest accuracy, reaching 83%, was obtained by CNN with parameters set at 1x10-5, and the preprocessing technique was considered superior to Bi-LSTM.

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River Discharge Forecasting in Mahanadi River Basin Based on Deep Learning Techniques

Sharma,

Kumari

2024

The Springer Series in Applied Machine Learning

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CNN-Bi LSTM Neural Network for Simulating Groundwater Level

Cited by 12 publications

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Mühendislik Alanında Akademik Analiz ve Tartışmalar

Mühendislik Alanında Akademik Analiz ve Tartışmalar

Text Classification Using Indobert Fine-Tuning Modeling With Convolutional Neural Network and Bi-LSTM

River Discharge Forecasting in Mahanadi River Basin Based on Deep Learning Techniques

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