Optimized Design of Neural Networks for a River Water Level Prediction System

Lineros, Miriam López; Luna, Antonio Madueño; Ferreira, Pedro; Ruano, A. E.

doi:10.3390/s21196504

Cited by 10 publications

(8 citation statements)

References 29 publications

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“…MOGA has been used successfully for different applications, such as HVAC control [90], detection of cerebral accidents [60], Ground Penetrating Radar (GPR) target detection [91], or river water level prediction [98].…”

Section: Parameter Estimationmentioning

confidence: 99%

Design of Ensemble Forecasting Models for Home Energy Management Systems

et al. 2021

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The increasing levels of energy consumption worldwide is raising issues with respect to surpassing supply limits, causing severe effects on the environment, and the exhaustion of energy resources. Buildings are one of the most relevant sectors in terms of energy consumption; as such, efficient Home or Building Management Systems are an important topic of research. This study discusses the use of ensemble techniques in order to improve the performance of artificial neural networks models used for energy forecasting in residential houses. The case study is a residential house, located in Portugal, that is equipped with PV generation and battery storage and controlled by a Home Energy Management System (HEMS). It has been shown that the ensemble forecasting results are superior to single selected models, which were already excellent. A simple procedure was proposed for selecting the models to be used in the ensemble, together with a heuristic to determine the number of models.

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Section: Parameter Estimationmentioning

confidence: 99%

Design of Ensemble Forecasting Models for Home Energy Management Systems

et al. 2021

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“…The ANFIS-GA technique exhibited better results as compared to the traditional ANFIS model. Lineros (Lineros et al, 2021) studied the effect of using a multi-objective genetic algorithm (MOGA) framework for the design of an artificial neural network (ANN) technique that is applied for designing 1-step-ahead river water level prediction models. A design process is a semi-automatic approach that can split data into datasets and find a near-optimal model with the proper topology and inputs, performing well on unseen data (data not utilised for model design).…”

Section: The Genetic Algorithm (Ga)mentioning

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

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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.

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“…Multiple studies recommended applying the hybrid models in water level forecasting, for example, Ghorbani et al [22], Wang et al [23], Zhu et al [24], and Dat et al [25]. Moreover, according to Zhang et al [26], who reviewed diferent univariate WL prediction models, the analysis of univariate data-driven models is widely utilised due to their simplicity and low data requirements, for example, Lineros et al [27], Liu et al [28], Mohammadi et al [7], and Ghorbani et al [22].…”

Section: Introductionmentioning

confidence: 99%

Application of Metaheuristic Algorithms and ANN Model for Univariate Water Level Forecasting

Mohammed

Zubaidi

Al‐Ansari

et al. 2023

Advances in Civil Engineering

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With the rapid development of machine learning (ML) models, the artificial neural network (ANN) is being increasingly applied for forecasting hydrological processes. However, researchers have not treated hybrid ML models in much detail. To address these issues, this study herein suggests a novel methodology to forecast the monthly water level (WL) based on multiple lags of the Tigris River in Al-Kut, Iraq, over ten years. The methodology includes preprocessing data methods, and the ANN model optimises with a marine predator algorithm (MPA). In the optimisation procedure, to decrease uncertainty and expand the predicting range, the slime mould algorithm (SMA-ANN), constriction coefficient-based particle swarm optimisation and chaotic gravitational search algorithms (CPSOCGSA-ANN), and particle swarm optimisation (PSO-ANN) are applied to compare and validate the MPA-ANN model performance. Analysis of results revealed that the data pretreatment methods improved the original data quality and selected the ideal predictors’ scenario by singular spectrum analysis and mutual information methods, respectively. For example, the correlation coefficient of the first lag improved from 0.648 to 0.938. Depending on various evaluation metrics, MPA-ANN tends to forecast WL better than SMA-ANN, PSO-ANN, and CPSOCGSA-ANN algorithms with coefficients of determination of 0.94, 0.81, 0.85, and 0.90, respectively. Evidence shows that the proposed methodology yields excellent results, with a scatter index equal to 0.002. The research outcomes represent an additional step towards evolving various hybrid ML techniques, which are valuable to practitioners wishing to forecast WL data and the management of water resources in light of environmental shifts.

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Optimized Design of Neural Networks for a River Water Level Prediction System

Cited by 10 publications

References 29 publications

Design of Ensemble Forecasting Models for Home Energy Management Systems

Design of Ensemble Forecasting Models for Home Energy Management Systems

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

Application of Metaheuristic Algorithms and ANN Model for Univariate Water Level Forecasting

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