Hybrid models for suspended sediment prediction: optimized random forest and multi-layer perceptron through genetic algorithm and stochastic gradient descent methods

Samadianfard, Saeed; Kargar, Katayoun; Shadkani, Sadra; Hashemi, Sajjad; Abbaspour, Akram; Safari, Mir Jafar Sadegh

doi:10.1007/s00521-021-06550-1

Cited by 23 publications

(4 citation statements)

References 49 publications

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“…where (𝑥 𝑖 , 𝑦 𝑖 ) is a randomly selected training sample. Conjugate Gradient: Using an iterative approach to a series of linear conjugate gradient problems, this algorithm determines the minimum of a function [33], [34]:…”

Section: Optimizationsmentioning

confidence: 99%

MLP and RBF Algorithms in Finance: Predicting and Classifying Stock Prices amidst Economic Policy Uncertainty

Ali,

Alakkari,

Abotaleb

et al. 2024

Mesopotamian Journal of Big Data

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In the realm of stock market prediction and classification, the use of machine learning algorithms has gained significant attention. In this study, we explore the application of Multilayer Perceptron (MLP) and Radial Basis Function (RBF) algorithms in predicting and classifying stock prices, specifically amidst economic policy uncertainty. Stock market fluctuations are greatly influenced by economic policies implemented by governments and central banks. These policies can create uncertainty and volatility, which in turn makes accurate predictions and classifications of stock prices more challenging. By leveraging MLP and RBF algorithms, we aim to develop models that can effectively navigate these uncertainties and provide valuable insights to investors and financial analysts. The MLP algorithm, based on artificial neural networks, is able to learn complex patterns and relationships within financial data. The RBF algorithm, on the other hand, utilizes radial basis functions to capture non-linear relationships and identify hidden patterns within the data. By combining these algorithms, we aim to enhance the accuracy of stock price prediction and classification models. The results showed that both MLB and RBF predicted stock prices well for a group of countries using an index reflecting the impact of news on economic policy and expectations, where the MLB algorithm proved its ability to predict chain data. Countries were also classified according to stock price data and uncertainty in economic policy, allowing us to determine the best country to invest in according to the data. The uncertainty surrounding economic policy is what makes stock price forecasting so crucial. Investors must consider the degree of economic policy uncertainty and how it affects asset prices when deciding how to allocate their assets.

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Section: Optimizationsmentioning

confidence: 99%

MLP and RBF Algorithms in Finance: Predicting and Classifying Stock Prices amidst Economic Policy Uncertainty

Ali,

Alakkari,

Abotaleb

et al. 2024

Mesopotamian Journal of Big Data

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“…2) Spark RDD Resilient Distributed Dataset (RDD), a RDD [7][8]. Specifically, it is a distributed, fault-tolerant, scalable and partitioned data structure [9][10]. In addition, Spark provides a full set of supporting arithmetic for RDDs, allowing users to perform very rich logical computations [11][12].…”

Section: ) Spark Ecosystemmentioning

confidence: 99%

An Improved Random Forest Algorithm Based on Spark

2022

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Classification algorithms are an important branch of data mining, and are also of great importance in the era of BD. The random forest algorithm (RFA) is one of the classification algorithms and is widely used in various industries for its good classification performance. However, the performance of RFA is not so good when dealing with high-dimensional data and unbalanced data. The main objective of this paper is to improve the RFA based on Spark. In this paper, we read a large amount of relevant algorithm literature in terms of algorithm research, and gain a comprehensive understanding of what feature selection and unbalanced classification are, as well as what characteristics they have and how these problems should be solved. It then focuses on how some domestic and international scholars have solved these problems. This paper focuses on studying and analysing the strengths and weaknesses of the RFA, and makes relevant improvements to address the two weaknesses of the RFA. In order to solve the problems of the RFA in the field of feature selection and the field of unbalanced classification, the optimization is improved respectively, and the parallelized design of the optimized algorithm is finally implemented on Spark.

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“…The movement of suspended sediments is essential in various fields, like water structure designs, water management, and dam and river engineering 1 , 2 . Modelling the quantity of sediment load (SL) in rivers is vital for designing flow control and water storage facilities, for example, canals and dams 3 , 4 .…”

Section: Introductionmentioning

confidence: 99%

Suspended sediment load prediction using sparrow search algorithm-based support vector machine model

Samantaray,

Sahoo,

Satapathy

et al. 2024

Sci Rep

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Prediction of suspended sediment load (SSL) in streams is significant in hydrological modeling and water resources engineering. Development of a consistent and accurate sediment prediction model is highly necessary due to its difficulty and complexity in practice because sediment transportation is vastly non-linear and is governed by several variables like rainfall, strength of flow, and sediment supply. Artificial intelligence (AI) approaches have become prevalent in water resource engineering to solve multifaceted problems like sediment load modelling. The present work proposes a robust model incorporating support vector machine with a novel sparrow search algorithm (SVM-SSA) to compute SSL in Tilga, Jenapur, Jaraikela and Gomlai stations in Brahmani river basin, Odisha State, India. Five different scenarios are considered for model development. Performance assessment of developed model is analyzed on basis of mean absolute error (MAE), root mean squared error (RMSE), determination coefficient (R2), and Nash–Sutcliffe efficiency (ENS). The outcomes of SVM-SSA model are compared with three hybrid models, namely SVM-BOA (Butterfly optimization algorithm), SVM-GOA (Grasshopper optimization algorithm), SVM-BA (Bat algorithm), and benchmark SVM model. The findings revealed that SVM-SSA model successfully estimates SSL with high accuracy for scenario V with sediment (3-month lag) and discharge (current time-step and 3-month lag) as input than other alternatives with RMSE = 15.5287, MAE = 15.3926, and ENS = 0.96481. The conventional SVM model performed the worst in SSL prediction. Findings of this investigation tend to claim suitability of employed approach to model SSL in rivers precisely and reliably. The prediction model guarantees the precision of the forecasted outcomes while significantly decreasing the computing time expenditure, and the precision satisfies the demands of realistic engineering applications.

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Hybrid models for suspended sediment prediction: optimized random forest and multi-layer perceptron through genetic algorithm and stochastic gradient descent methods

Cited by 23 publications

References 49 publications

MLP and RBF Algorithms in Finance: Predicting and Classifying Stock Prices amidst Economic Policy Uncertainty

MLP and RBF Algorithms in Finance: Predicting and Classifying Stock Prices amidst Economic Policy Uncertainty

An Improved Random Forest Algorithm Based on Spark

Suspended sediment load prediction using sparrow search algorithm-based support vector machine model

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