Employing deep learning in crisis management and decision making through prediction using time series data in Mosul Dam Northern Iraq DOI Creative Commons

Khalid MK Khafaji,

Bassem Ben Hamed

PeerJ Computer Science, Journal Year: 2024, Volume and Issue: 10, P. e2416 - e2416

Published: Oct. 31, 2024

Specifically, Iraq is threatened in its second-largest northern city, Mosul, by the collapse of Mosul Dam due to problems at root dam, causing a wave floods that will cause massive loss life, resources, and public property. The objective this study effectively monitor level dam water predicting held In anticipation achieving flood stage breaking supporting behavior through formation 14-day time series data predict seven days later. Used six deep learning models (deep neural network (DNN), convolutional (CNN), long short-term memory (CNN-LSTM), CNN-LSTM-Skip CNN-LSTM Skip Attention) were trained dam; these levels being under surveillance prepared case danger, alert people potential threats depending on dam’s level. These created from actual sets it’s fundamental historical reading for 13 years (1993–2006) stored was adopted coordination with Iraqi Ministry Water Resources Centre Research Dams University. methodology applied shows model’s performance efficiency prediction results’ low error rate. It also compares those practical results determine adopt performance-efficient model lower comparison proved accuracy results, superior model, it has highest ability perform high MAE = 0.087153 steps 0 s 196 ms/step 0.00067. current demonstrated Dam, which suffers foundation may future. Therefore, must be monitored accurately. aims test effectiveness proposed after evaluating their applying process within scenario obtain predictive values 14 days. showed hybrid correctly accurately obtaining integrated framework required scenario. concluded possible enhance identify risk an early stage, allows proactive crisis management sound decision-making, thus mitigating adverse effects crises safety infrastructure reducing human losses areas along Tigris River.

Language: Английский

Enhancing road traffic flow prediction with improved deep learning using wavelet transforms DOI Creative Commons
Fouzi Harrou, Abdelhafid Zeroual, Farid Kadri

et al.

Results in Engineering, Journal Year: 2024, Volume and Issue: 23, P. 102342 - 102342

Published: June 7, 2024

Precise traffic flow prediction is a central component of advancing intelligent transportation systems, providing essential insights for optimizing management, reducing travel times, and alleviating congestion. This study introduces an efficient deep learning approach that synergistically integrates the benefits wavelet-based denoising Recurrent Neural Networks (RNNs). integrated methodology introduced to effectively capture inherent nonlinearity temporal dependencies in time series data. Specifically, Long Short-Term Memory (LSTM) Gated Unit (GRU) are address challenges associated with accurately forecasting flow. To enhance quality, data preprocessed using exponential smoothing filtering as filters, eliminating outliers. The effectiveness proposed techniques evaluated measurements collected from diverse highway locations across California, including Old Bayshore highway, situated south Interstate 880 (I880), Ashby Ave positioned west 80 (I80) San Francisco Bay Area. results obtained through integrating both architectures, LSTM GRU, within wavelet transform-based filter demonstrate enhancement performance. Symlet Haar wavelets achieved high performance average R2 0.982 0.9811, respectively.

Language: Английский

Citations

24
Hybridization of Stochastic Hydrological Models and Machine Learning Methods for Improving Rainfall-Runoff Modelling DOI Creative Commons

Sianou Ezéckiel Houénafa,

Olatunji Johnson,

Erick Kiplangat Ronoh

et al.

Results in Engineering, Journal Year: 2025, Volume and Issue: unknown, P. 104079 - 104079

Published: Jan. 1, 2025

Language: Английский

Citations

3
Enhancing wind power prediction with self-attentive variational autoencoders: A comparative study DOI Creative Commons
Fouzi Harrou, Abdelkader Dairi, Abdelhakim Dorbane

et al.

Results in Engineering, Journal Year: 2024, Volume and Issue: 23, P. 102504 - 102504

Published: July 14, 2024

Accurate wind power prediction is critical for efficient grid management and the integration of renewable energy sources into grid. This study presents an effective deep-learning approach that improves short-term forecasting accuracy. The method incorporates a Variational Autoencoder (VAE) with self-attention mechanism applied in both encoder decoder. empowers model to leverage VAE's strengths time-series modeling nonlinear approximation while focusing on most relevant features within data. effectiveness this evaluated through comprehensive comparison eight established deep learning methods, including Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM) networks, Bidirectional LSTMs (BiLSTMs), Convolutional (ConvLSTMs), Gated Units (GRUs), Stacked Autoencoders (SAEs), Restricted Boltzmann Machines (RBMs), vanilla VAEs. Real-world data from five turbines France Turkey used evaluation. Five statistical metrics are employed quantitatively assess performance each method. results indicate SA-VAE consistently outperformed other models, achieving highest average R2 value 0.992, demonstrating its superior predictive capability compared existing techniques.

Language: Английский

Citations

8
Improving Short-term Daily Streamflow Forecasting Using an Autoencoder Based CNN-LSTM Model DOI
Umar Muhammad Mustapha Kumshe,

Z.M. Abdulhamid,

Baba Ahmad Mala

et al.

Water Resources Management, Journal Year: 2024, Volume and Issue: 38(15), P. 5973 - 5989

Published: Aug. 13, 2024

Language: Английский

Citations

6
Advancing Reservoir Water Level Predictions: Evaluating Conventional, Ensemble and Integrated Swarm Machine Learning Approaches DOI Creative Commons

Issam Rehamnia,

Amin Mahdavi‐Meymand

Water Resources Management, Journal Year: 2024, Volume and Issue: unknown

Published: Sept. 25, 2024

Language: Английский

Citations

4
A Unified Approach to Smart Coconut Farming with IoT and Deep Learning for Recommendation of Pesticides and Fertilizers DOI

B. Ch. S. N. L. S. Sai Baba,

Siddhani Hasavilasini, S. Gowri Shankar K. Ravi Teja

et al.

Published: Jan. 1, 2025

Language: Английский

Citations

0
AquaMap: Empowering Communities to Report and Map Water-Related Issues in Real-Time with Deep Learning DOI
Harshitha Lakshmi Durga Nalla, Anusha Bhuchupalli, Tejasree Addala

et al.

Published: Jan. 1, 2025

Language: Английский

Citations

0
Predicting Bearing Capacity Factors of Multiple Shallow Foundations Using Finite Element Limit Analysis and Machine Learning Approaches DOI

Kittiphan Yoonirundorn,

Teerapong Senjuntichai,

Angsumalin Senjuntichai

et al.

Transportation Infrastructure Geotechnology, Journal Year: 2025, Volume and Issue: 12(3)

Published: Feb. 26, 2025

Language: Английский

Citations

0
Dynamics of Water Stress in Bangalore, India: Exploring the Confluence of Geopolitical, Climatic, and Anthropogenic Factors DOI
Prasanta Moharaj, Suvashisa Rana, Dibakar Sahoo

et al.

Journal of Asian and African Studies, Journal Year: 2025, Volume and Issue: unknown

Published: April 28, 2025

This study attempts to examine the water crisis dynamics in Bangalore, elucidating complex interplay between geopolitical tensions, climate change impacts, and anthropogenic factors that intensify regional scarcity. Through a rigorous analysis of secondary data, this research highlights critical role disputes crisis. It also emphasizes exacerbating effects variability, evidenced by erratic monsoon patterns inadequate groundwater replenishment. Furthermore, explores how such as rapid urbanization unsustainable extraction compound The urgency situation prompts proposal strategic adaptation mitigation measures.

Language: Английский

Citations

0
Analyzing the Dynamics Between Crude Oil Spot Prices and Futures Prices by Maturity Terms: Deep Learning Approaches to Futures-Based Forecasting DOI Creative Commons
JeongHoe Lee,

Bingjiang Xia

Results in Engineering, Journal Year: 2024, Volume and Issue: 24, P. 103086 - 103086

Published: Oct. 9, 2024

Language: Английский

Citations

2