Article
Computer Science, Artificial Intelligence
Ranjit Kumar Paul, Sandip Garai
Summary: Accurate forecasting in Indian agriculture is crucial, with machine learning techniques like artificial neural network and wavelet transformation being effective in handling nonlinear datasets to improve model accuracy.
Article
Green & Sustainable Science & Technology
Tarate Suryakant Bajirao, Pravendra Kumar, Manish Kumar, Ahmed Elbeltagi, Alban Kuriqi
Summary: This study analyzed the validity of simple and wavelet-coupled Artificial Intelligence models for daily Suspended Sediment estimation in the Koyna River basin of India. The results showed that data pre-processing using wavelet transform significantly improves the model's predictive efficiency and reliability, with the Coiflet wavelet-coupled ANFIS model performing the best. Sensitivity analysis revealed the importance of the previous one-day SSC as the most crucial input variable for daily SSC estimation in the Koyna River basin.
Article
Water Resources
Pavan Kumar Yeditha, Tarun Pant, Maheswaran Rathinasamy, Ankit Agarwal
Summary: This study investigates the spatiotemporal characterization of streamflow in six unregulated catchments in India, revealing significant oscillations at different time scales and relationships between streamflow and precipitation as well as global climate indices. The analysis showed in-phase relationships between streamflow and IOD and NAO, while lag correlations were observed with Nino 3.4 and PDO indices at various time scales.
JOURNAL OF WATER AND CLIMATE CHANGE
(2022)
Article
Environmental Sciences
Pawan S. Wable, Madan Kumar Jha, Sirisha Adamala, Mukesh Kumar Tiwari, Sabinaya Biswal
Summary: This study focuses on drought forecasting using Artificial Neural Network (ANN)-based models. Four models were developed and compared, and the hybrid wavelet and bootstrap models performed better than the conventional and single bootstrap models.
ENVIRONMENTAL MONITORING AND ASSESSMENT
(2023)
Article
Ecology
Celso Augusto Guimaraes Santos, Gleycielle Rodrigues do Nascimento, Camilo Allyson Simoes de Farias, Richarde Marques da Silva, Manoranjan Mishra
Summary: This study presents a methodology that improves streamflow forecasting by using wavelet neural networks to relate streamflows with rainfall data. The methodology performs well in long-term forecasts, especially in the Mahanadi River basin, and can be applied in other catchments.
ECOLOGICAL INFORMATICS
(2023)
Article
Computer Science, Interdisciplinary Applications
Zain Syed, Prince Mahmood, Sajjad Haider, Shakil Ahmad, Khan Zaib Jadoon, Rashid Farooq, Sibtain Syed, Khalil Ahmad
Summary: This study combines the Wavelet Transform and Artificial Neural Network to forecast streamflow of the Gilgit River at different time intervals. Short-term forecasts performed best while intermediate forecasts showed decreasing performance, but long-term forecasts improved. The models underestimated high flows and slightly overestimated low-to-intermediate flow conditions.
JOURNAL OF HYDROINFORMATICS
(2023)
Article
Computer Science, Artificial Intelligence
Aasim, S. N. Singh, Abheejeet Mohapatra
Summary: This paper proposes a hybrid model that combines the Wavelet Transform (WT) and Support Vector Machine (SVM) features for electrical load forecasting. The model improves overall forecasting ability by maximizing error contribution in sub-series forecasting.
APPLIED SOFT COMPUTING
(2021)
Article
Engineering, Civil
Isa Ebtehaj, Hossein Bonakdari
Summary: A novel hybrid model for multi-steps-ahead flood forecasting is proposed, which integrates the discrete wavelet transform and improved outlier-robust extreme learning machine models. The model overcomes the limitations of existing methods and achieves high accuracy in peak flow forecasting.
JOURNAL OF HYDROLOGY
(2022)
Article
Thermodynamics
Ruan Luzia, Lihki Rubio, Carlos E. Velasquez
Summary: Several studies have focused on improving forecasting techniques for capturing multiple patterns in time series. The advancement in computing hardware has made it possible to solve complex equations using large amounts of data, such as neural networks. However, time series methods like ARIMA can also provide good approximations with low computational resources. To enhance ARIMA approximations, they can be combined with techniques like Wavelet Transform or Fourier Transform. This study evaluates the suitability of using artificial neural networks, ARIMA combined with Wavelet Transform, or Fourier Transform to make predictions for different time horizons and frequencies. The results indicate that artificial neural networks perform better for short-term horizons, ARIMA with Fourier Transform provides the best approximation for monthly time series and any time horizon, and ARIMA with Wavelet Transform offers the best approximation for medium-term and long-term periods at any time frequency.
Article
Engineering, Multidisciplinary
Junaid Farooq, Mohammad Abid Bazaz
Summary: This paper introduces a deep learning-based online incremental learning technique using Artificial Neural Network (ANN) to develop an adaptive and non-intrusive analytical model of Covid-19 pandemic, which can analyze the temporal dynamics of disease spread in real-time. The model has been validated with historical data and provides a 30-day forecast of disease spread in the five worst affected states in India.
ALEXANDRIA ENGINEERING JOURNAL
(2021)
Article
Engineering, Environmental
Tzu-Chia Chen
Summary: Modern artificial intelligence techniques can effectively simulate water quality parameters, and the accuracy of machine learning models can be improved by using wavelet theory. The study conducted in Gao-ping River, Taiwan found that hybrid models with wavelet transform significantly increased the accuracy of ANN and ANFIS models.
WATER SCIENCE AND TECHNOLOGY
(2023)
Article
Environmental Sciences
Liang Chen, Xingrong Lu, Daping Deng, Mehdi Kouhdarag, Yimin Mao
Summary: This study focuses on the dynamic transient analysis of arched beam bridges over rivers. A novel metric, the wavelet packet rate index (WPERI), is introduced for detecting cracks in curved bridge segments over rivers. The WPERI proves reliable in accounting for the river environment's impact on structural integrity, and its sensitivity and precision in crack detection are highlighted. The study underscores the potential of wavelet packet decomposition and finite element analysis as tools for crack detection in riverine bridges.
Article
Computer Science, Artificial Intelligence
Sarah Almaghrabi, Mashud Rana, Margaret Hamilton, Mohammad Saiedur Rahaman
Summary: Accurate and reliable prediction of photovoltaic power output is crucial for grid stability and power dispatching. Current wavelet transform methods have limitations in terms of time complexity. This study proposes a new approach that improves the efficiency of wavelet transform by using a simplified model.
Article
Engineering, Environmental
Muhammad Hammad, Muhammad Shoaib, Hamza Salahudin, Muhammad Azhar Inam Baig, Mudasser Muneer Khan, Muhammad Kaleem Ullah
Summary: Accurate forecasting of rainfall is achieved by utilizing the novel WMTLNN model which effectively captures historical observations. The WMTLNN model outperforms other models, with over 80% forecasting accuracy and NSE values ranging from 0.85 to 0.95. Wavelet transformation of time series data enhances efficiency and accuracy of rainfall forecast.
STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT
(2021)
Article
Construction & Building Technology
Yongkang Zeng, Jingjing Chen, Ning Jin, Xiaoping Jin, Yang Du
Summary: Air quality measurement and forecasting is a popular research topic in the field of sustainable intelligent environmental design, urban area development, and pollution control, especially for developing countries in Asia like China. This study proposes a novel forecasting model that integrates the extended stationary wavelet transform and nested long short-term memory neural network for PM2.5 air quality forecasting. The results show that the proposed method outperforms state-of-the-art forecasting methods in terms of different error metrics.
BUILDING AND ENVIRONMENT
(2022)