A Review of Water Level Fluctuation Models and Modelling Initiatives

Hussaini, A.; Mahmud, Maqsood; Tang, K. K. W.; Abubakar, A. G.

doi:10.1166/jctn.2020.8781

Cited by 3 publications

(3 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Advances in ML [70,71] have boosted the relevance of stochastic modeling for predicting lake WH [81][82][83][84][85]. Past research in modeling wetland inundation dynamics using ML methods is often restricted to using in situ measurements for identifying the presence of water [86,87].…”

Section: Introductionmentioning

confidence: 99%

Inundation–Desiccation State Prediction for Salt Pans in the Western Pannonian Basin Using Remote Sensing, Groundwater, and Meteorological Data

Schauer,

Schlaffer,

Bueechi

et al. 2023

Remote Sensing

View full text Add to dashboard Cite

Salt pans are unique wetland ecosystems. In the Austrian Seewinkel region, salt pans are in an increasingly vulnerable state due to groundwater drainage and heightened climatic pressures. It is crucial to model how seasonal and long-term hydrological and climatological variations affect the salt pan dynamics in Seewinkel, yet a comprehensive understanding of the driving processes is lacking. The goal of this study is to develop random forest machine learning models driven by hydrological and meteorological data that allow us to predict in early spring (March) of each year the inundation state in the subsequent summer and fall. We utilize Earth observation data from Landsat 5 (L5), 8 (L8), and 9 (L9) to derive the time series of the inundation state for 34 salt pans for the period 1984–2022. Furthermore, we demonstrate that the groundwater level observed in March is the strongest predictor of the salt pan inundation state in summer and fall. Utilizing local groundwater data yields a Matthews correlation coefficient of 0.59. Models using globally available meteorological data, either instead of or in addition to groundwater data, provide comparable results. This allows the global transfer of the approach to comparable ecosystems where no in situ data are available.

show abstract

Section: Introductionmentioning

confidence: 99%

Inundation–Desiccation State Prediction for Salt Pans in the Western Pannonian Basin Using Remote Sensing, Groundwater, and Meteorological Data

Schauer,

Schlaffer,

Bueechi

et al. 2023

Remote Sensing

View full text Add to dashboard Cite

show abstract

“…They are particularly vulnerable since they are typically densely populated, with more than 40% of the population living in shoreline areas. The level of risk associated with this can be rather substantial (Hussaini et al, 2020). Additionally, the overuse of groundwater directly affects the sea water level (Koutsoyiannis, 2020).…”

Section: Introductionmentioning

confidence: 99%

“…In addition, model inputs, data split, model performance criteria, and model inter-comparison were all studied. Hussaini (Hussaini et al, 2020) reviewed the global water level fluctuation modelling project to provide evidence for further improvement in modelling. The scientific theories behind the modelling approaches were closely examined to better grasp their primary features and to present a comprehensive picture of the current water level fluctuation modelling effort.…”

Section: Introductionmentioning

confidence: 99%

Application of hybrid machine learning models and data pre-processing to predict water level of watersheds: Recent trends and future perspective

et al. 2022

View full text Add to dashboard Cite

The community's well-being and economic livelihoods are heavily influenced by the water level of watersheds. The changes in water levels directly affect the circulation processes of lakes and rivers that control water mixing and bottom sediment resuspension, further affecting water quality and aquatic ecosystems. Thus, these considerations have made the water level monitoring process essential to save the environment. Machine learning hybrid models are emerging robust tools that are successfully applied for water level monitoring. Various models have been developed, and selecting the optimal model would be a lengthy procedure. A timely, detailed, and instructive overview of the models' concepts and historical uses would be beneficial in preventing researchers from overlooking models' potential selection and saving significant time on the problem. Thus, recent research on water level prediction using hybrid machines is reviewed in this article to present the "state of the art" on the subject and provide some suggestions on research methodologies and models. This comprehensive study classifies hybrid models into four types algorithm parameter optimisation-based hybrid models (OBH), pre-processingbased hybrid models (PBH), the components combination-based hybrid models (CBH), and hybridisation of parameter optimisation-based with preprocessing-based hybrid models (HOPH); furthermore, it explains the pre-processing of data in detail. Finally, the most popular optimisation methods and future perspectives and conclusions have been discussed.

show abstract

A CPSOCGSA-tuned neural processor for forecasting river water salinity: Euphrates river, Iraq

et al. 2022

View full text Add to dashboard Cite

A Review of Water Level Fluctuation Models and Modelling Initiatives

Cited by 3 publications

References 0 publications

Inundation–Desiccation State Prediction for Salt Pans in the Western Pannonian Basin Using Remote Sensing, Groundwater, and Meteorological Data

Inundation–Desiccation State Prediction for Salt Pans in the Western Pannonian Basin Using Remote Sensing, Groundwater, and Meteorological Data

Application of hybrid machine learning models and data pre-processing to predict water level of watersheds: Recent trends and future perspective

A CPSOCGSA-tuned neural processor for forecasting river water salinity: Euphrates river, Iraq

Contact Info

Product

Resources

About