Development and Assessment of Water-Level Prediction Models for Small Reservoirs Using a Deep Learning Algorithm

Kusudo, Tsumugu; Yamamoto, Atsushi; Kimura, Masaomi; Matsuno, Yutaka

doi:10.3390/w14010055

Cited by 7 publications

(3 citation statements)

References 39 publications

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“…Wetland water levels can be predicted in several ways, including physically based and data-driven approaches [58]. Physically based approaches can increase the level of complexity, are time-consuming to develop and require a high level of knowledge in the relevant field [16].…”

Section: Available Machine-learning Techniques To Predict Wetland Wat...mentioning

confidence: 99%

Wetland Water-Level Prediction in the Context of Machine-Learning Techniques: Where Do We Stand?

et al. 2023

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Wetlands are simply areas that are fully or partially saturated with water. Not much attention has been given to wetlands in the past, due to the unawareness of their value to the general public. However, wetlands have numerous hydrological, ecological, and social values. They play an important role in interactions among soil, water, plants, and animals. The rich biodiversity in the vicinity of wetlands makes them invaluable. Therefore, the conservation of wetlands is highly important in today’s world. Many anthropogenic activities damage wetlands. Climate change has adversely impacted wetlands and their biodiversity. The shrinking of wetland areas and reducing wetland water levels can therefore be frequently seen. However, the opposite can be seen during stormy seasons. Since wetlands have permissible water levels, the prediction of wetland water levels is important. Flooding and many other severe environmental damage can happen when these water levels are exceeded. Therefore, the prediction of wetland water level is an important task to identify potential environmental damage. However, the monitoring of water levels in wetlands all over the world has been limited due to many difficulties. A Scopus-based search and a bibliometric analysis showcased the limited research work that has been carried out in the prediction of wetland water level using machine-learning techniques. Therefore, there is a clear need to assess what is available in the literature and then present it in a comprehensive review. Therefore, this review paper focuses on the state of the art of water-level prediction techniques of wetlands using machine-learning techniques. Nonlinear climatic parameters such as precipitation, evaporation, and inflows are some of the main factors deciding water levels; therefore, identifying the relationships between these parameters is complex. Therefore, machine-learning techniques are widely used to present nonlinear relationships and to predict water levels. The state-of-the-art literature summarizes that artificial neural networks (ANNs) are some of the most effective tools in wetland water-level prediction. This review can be effectively used in any future research work on wetland water-level prediction.

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Section: Available Machine-learning Techniques To Predict Wetland Wat...mentioning

confidence: 99%

Wetland Water-Level Prediction in the Context of Machine-Learning Techniques: Where Do We Stand?

et al. 2023

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“…Water level predictions can be conducted using both physical and data-driven approaches [27]. Physical-based approaches increase the levels of complexity while requiring significant time to conduct and to develop [26].…”

Section: Artificial Neural Network (Ann) To Predict Wetland Water Levelsmentioning

confidence: 99%

“…Therefore, wetlands A prediction model was developed using meteorological data and the previous year's water levels. According to the literature, the majority of the related studies have utilized the time series data of meteorological data as the input data to the model [26,27,46]. Meteorological data were collected from the Department of Meteorology, Sri Lanka, while the water levels were collected from the Land Development Corporation, Sri Lanka.…”

Section: Case Studymentioning

confidence: 99%

Wetland Water Level Prediction Using Artificial Neural Networks—A Case Study in the Colombo Flood Detention Area, Sri Lanka

Jayathilake

Sarukkalige

Hoshino

et al. 2022

Climate

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Historically, wetlands have not been given much attention in terms of their value due to the general public being unaware. Nevertheless, wetlands are still threatened by many anthropogenic activities, in addition to ongoing climate change. With these recent developments, water level prediction of wetlands has become an important task in order to identify potential environmental damage and for the sustainable management of wetlands. Therefore, this study identified a reliable neural network model by which to predict wetland water levels over the Colombo flood detention area, Sri Lanka. This is the first study conducted using machine learning techniques in wetland water level predictions in Sri Lanka. The model was developed with independent meteorological variables, including rainfall, evaporation, temperature, relative humidity, and wind speed. The water levels measurements of previous years were used as dependent variables, and the analysis was based on a seasonal timescale. Two neural network training algorithms, the Levenberg Marquardt algorithm (LM) and the Scaled Conjugate algorithm (SG), were used to model the nonlinear relationship, while the Mean Squared Error (MSE) and Coefficient of Correlation (CC) were used as the performance indices by which to understand the robustness of the model. In addition, uncertainty analysis was carried out using d-factor simulations. The performance indicators showed that the LM algorithm produced better results by which to model the wetland water level ahead of the SC algorithm, with a mean squared error of 0.0002 and a coefficient of correlation of 0.99. In addition, the computational efficiencies were excellent in the LM algorithm compared to the SC algorithm in terms of the prediction of water levels. LM showcased 3–5 epochs, whereas SC showcased 34–50 epochs of computational efficiencies for all four seasonal predictions. However, the d-factor showcased that the results were not within the cluster of uncertainty. Therefore, the overall results suggest that the Artificial Neural Network can be successfully used to predict the wetland water levels, which is immensely important in the management and conservation of the wetlands.

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A Water Level Estimation System Based on Image Recognition of Water Level Gauge

Asada,

Yukawa,

Wakabayashi

et al. 2024

Lecture Notes on Data Engineering and Communications Technologies

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Development and Assessment of Water-Level Prediction Models for Small Reservoirs Using a Deep Learning Algorithm

Cited by 7 publications

References 39 publications

Wetland Water-Level Prediction in the Context of Machine-Learning Techniques: Where Do We Stand?

Wetland Water-Level Prediction in the Context of Machine-Learning Techniques: Where Do We Stand?

Wetland Water Level Prediction Using Artificial Neural Networks—A Case Study in the Colombo Flood Detention Area, Sri Lanka

A Water Level Estimation System Based on Image Recognition of Water Level Gauge

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