A novel hybrid XAJ-LSTM model for multi-step-ahead flood forecasting DOI Creative Commons
Zhen Cui, Yanlai Zhou, Shenglian Guo

et al.

Hydrology Research, Journal Year: 2021, Volume and Issue: 52(6), P. 1436 - 1454

Published: June 29, 2021

Abstract The conceptual hydrologic model has been widely used for flood forecasting, while long short-term memory (LSTM) neural network demonstrated a powerful ability to tackle time-series predictions. This study proposed novel hybrid by combining the Xinanjiang (XAJ) and LSTM (XAJ-LSTM) achieve precise multi-step-ahead forecasts. takes forecasts of XAJ as input variables enhance physical mechanism hydrological modeling. Using models benchmark comparison purposes, was applied Lushui reservoir catchment in China. results that three could offer reasonable XAJ-LSTM not only effectively simulate long-term dependence between precipitation datasets, but also create more accurate than models. maintained similar forecast performance after feeding with simulated values during horizons . concludes integrates machine learning can raise accuracy improving interpretability data-driven internals.

Language: Английский

River water quality index prediction and uncertainty analysis: A comparative study of machine learning models DOI

Seyed Babak Haji Seyed Asadollah,

Ahmad Sharafati, Davide Motta

et al.

Journal of environmental chemical engineering, Journal Year: 2020, Volume and Issue: 9(1), P. 104599 - 104599

Published: Oct. 18, 2020

Language: Английский

Citations

276
Application of machine learning in intelligent fish aquaculture: A review DOI
Shili Zhao, Song Zhang, Jincun Liu

et al.

Aquaculture, Journal Year: 2021, Volume and Issue: 540, P. 736724 - 736724

Published: April 3, 2021

Language: Английский

Citations

191
Water quality index modeling using random forest and improved SMO algorithm for support vector machine in Saf-Saf river basin DOI
Bachir Sakaa, Ahmed Elbeltagi, Samir Boudibi

et al.

Environmental Science and Pollution Research, Journal Year: 2022, Volume and Issue: 29(32), P. 48491 - 48508

Published: Feb. 22, 2022

Language: Английский

Citations

87
Prediction modelling framework comparative analysis of dissolved oxygen concentration variations using support vector regression coupled with multiple feature engineering and optimization methods: A case study in China DOI Creative Commons
Xizhi Nong, Laifei Cheng, Lihua Chen

et al.

Ecological Indicators, Journal Year: 2023, Volume and Issue: 146, P. 109845 - 109845

Published: Jan. 2, 2023

Dissolved oxygen (DO) is an essential indicator for assessing water quality and managing aquatic environments, but it still a challenging topic to accurately understand predict the spatiotemporal variation of DO concentrations under complex effects different environmental factors. In this study, practical prediction framework was proposed based on support vector regression (SVR) model coupling multiple intelligence techniques (i.e., four data denoising techniques, three feature selection rules, hyperparameter optimization methods). The holistic tested using matrix (17,532 observation in total) 12 indicators from vital monitoring stations longest inter-basin diversion project world Middle-Route South-to-North Water Diversion Project China), during year 2017 2020 period. results showed that we advocated could successfully concentration variations geographical locations. used "wavelet analysis–LASSO regression–random search–SVR" combination Waihuanhe station has best performance, with Root Mean Square Error (RMSE), (MSE), Absolute (MAE), coefficient determination (R2) values 0.251, 0.063, 0.190, 0.911, respectively. combined methods can significantly promote robustness accuracy provide new universal way investigating understanding drivers variations. For management department, comprehensive also identify reveal key parameters should be concerned monitored factors change. More studies terms potential integrated risk multi-indicators mega projects and/or similar bodies are required future.

Language: Английский

Citations

60
Applications of machine learning to water resources management: A review of present status and future opportunities DOI Creative Commons
Ashraf Ahmed,

Sakina Sayed,

Antoifi Abdoulhalik

et al.

Journal of Cleaner Production, Journal Year: 2024, Volume and Issue: 441, P. 140715 - 140715

Published: Jan. 11, 2024

Water is the most valuable natural resource on earth that plays a critical role in socio-economic development of humans worldwide. used for various purposes, including, but not limited to, drinking, recreation, irrigation, and hydropower production. The expected population growth at global scale, coupled with predicted climate change-induced impacts, warrants need proactive effective management water resources. Over recent decades, machine learning tools have been widely applied to resources management-related fields often shown promising results. Despite publication several review articles applications water-related fields, this paper presents first time comprehensive techniques management, focusing achievements. study examines potential advanced improve decision support systems sectors within realm which includes groundwater streamflow forecasting, distribution systems, quality wastewater treatment, demand consumption, marine energy, drainage flood defence. This provides an overview state-of-the-art approaches industry how they can be ensure supply sustainability, quality, drought mitigation. covers related studies provide snapshot industry. Overall, LSTM networks proven exhibit reliable performance, outperforming ANN models, traditional established physics-based models. Hybrid ML exhibited great forecasting accuracy across all showing superior computational power over ANNs architectures. In addition purely data-driven physical-based hybrid models also developed prediction performance. These efforts further demonstrate Machine powerful practical tool management. It insights, predictions, optimisation capabilities help enhance sustainable use development, healthy ecosystems human existence.

Language: Английский

Citations

56
A transfer Learning-Based LSTM strategy for imputing Large-Scale consecutive missing data and its application in a water quality prediction system DOI
Chen Zeng,

Huan Xu,

Peng Jiang

et al.

Journal of Hydrology, Journal Year: 2021, Volume and Issue: 602, P. 126573 - 126573

Published: July 29, 2021

Language: Английский

Citations

102
Artificial intelligence as an upcoming technology in wastewater treatment: a comprehensive review DOI
Arti Malviya, Dipika Jaspal

Environmental Technology Reviews, Journal Year: 2021, Volume and Issue: 10(1), P. 177 - 187

Published: Jan. 1, 2021

Artificial intelligence (AI) is nowadays an upcoming technology. It a practice of simulating human for varied applications. When compared with the standard practices, AI developing at rapid rate. has proved its worth in several areas such as agriculture, automobile industry, banking and finance, space exploration, artificial creativity, etc. Owing to efficiency, speed, independence from operations, now entering wastewater treatment sector. This technology been used monitoring performance water plants terms efficiency parameters, Biological Oxygen Demand (BOD) Chemical (COD) determination, elimination nitrogen sulphur, prediction turbidity hardness, uptake contaminants, etc., Neural Networks (ANN), Fuzzy Logic Algorithms (FL), Genetic (GA) are basic three models under predominantly Studies reveal that determination coefficient values 0.99 can be attained COD, BOD, heavy metals organics removal using ANN hybrid intelligent systems. review paper describes research all possible utilized which have enhanced pollutant percentage accuracy ranging 84% 90% provided viewpoint on future directions novel research, field due focus pollution remediation, cost effectiveness, energy economy, management.

Language: Английский

Citations

95
Suspended sediment load prediction using long short-term memory neural network DOI Creative Commons
Nouar AlDahoul, Yusuf Essam, Pavitra Kumar

et al.

Scientific Reports, Journal Year: 2021, Volume and Issue: 11(1)

Published: April 9, 2021

Abstract Rivers carry suspended sediments along with their flow. These deposit at different places depending on the discharge and course of river. However, deposition these impacts environmental health, agricultural activities, portable water sources. Deposition reduces flow area, thus affecting movement aquatic lives ultimately leading to change river course. Thus, data variation is crucial information for various authorities. Various authorities require forecasted in operate hydraulic structures properly. Usually, prediction sediment concentration (SSC) challenging due factors, including site-related data, modelling, lack multiple observed factors used prediction, pattern complexity.Therefore, address previous problems, this study proposes a Long Short Term Memory model predict Malaysia's Johor River utilizing only one factor, data. The was collected period 1988–1998. Four models were tested, study, sediments, which are: ElasticNet Linear Regression (L.R.), Multi-Layer Perceptron (MLP) neural network, Extreme Gradient Boosting, Short-Term Memory. Predictions analysed based four scenarios such as daily, weekly, 10-daily, monthly. Performance evaluation stated that outperformed other regression values 92.01%, 96.56%, 96.71%, 99.45% 10-days, monthly scenarios, respectively.

Language: Английский

Citations

74
Application of Machine Learning for eutrophication analysis and algal bloom prediction in an urban river: A 10-year study of the Han River, South Korea DOI

Quang Viet Ly,

Xuan Cuong Nguyen, Ngoc C. Lê

et al.

The Science of The Total Environment, Journal Year: 2021, Volume and Issue: 797, P. 149040 - 149040

Published: July 17, 2021

Language: Английский

Citations

73
A water quality prediction model based on variational mode decomposition and the least squares support vector machine optimized by the sparrow search algorithm (VMD-SSA-LSSVM) of the Yangtze River, China DOI
Chenguang Song,

Leihua Yao,

Chengya Hua

et al.

Environmental Monitoring and Assessment, Journal Year: 2021, Volume and Issue: 193(6)

Published: May 27, 2021

Language: Английский

Citations

69