Predicting suspended sediment load in Peninsular Malaysia using support vector machine and deep learning algorithms

Abstract

High loads of suspended sediments in rivers are known to cause detrimental effects to potable water sources, river water quality, irrigation activities, and dam or reservoir operations. For this reason, the study of suspended sediment load (SSL) prediction is important for monitoring and damage mitigation purposes. The present study tests and develops machine learning (ML) models, based on the support vector machine (SVM), artificial neural network (ANN) and long short-term memory (LSTM) algorithms, to predict SSL based on 11 different river data sets comprising of streamflow (SF) and SSL data obtained from the Malaysian Department of Irrigation and Drainage. The main objective of the present study is to propose a single model that is capable of accurately predicting SSLs for any river data set within Peninsular Malaysia. The ANN3 model, based on the ANN algorithm and input scenario 3 (inputs consisting of current-day SF, previous-day SF, and previous-day SSL), is determined as the best model in the present study as it produced the best predictive performance for 5 out of 11 of the tested data sets and obtained the highest average RM with a score of 2.64 when compared to the other tested models, indicating that it has the highest reliability to produce relatively high-accuracy SSL predictions for different data sets. Therefore, the ANN3 model is proposed as a universal model for the prediction of SSL within Peninsular Malaysia.

Intrusion Detection Method Based on Deep Learning

“…The output of the map function is first processed by the MapReduce framework and then sent to the reduced function. This process sorts and groups key/value pairs by key to [10]. Therefore, continuing with examples in the design and construction of a Hadoop-based intrusion detection big data analysis, the reduced function sees the following input:…”

“…(2020102519230212234, [111, 78]) (2020102619230212234, [0, 22,11]) All are timestamped with a series of feature data store ID. All reduce functions must now repeat this list and identify the relevant storage ID [10,11] required for the intrusion detection analysis algorithm:…”

Rainfall time series prediction based on the DWT-SVR- Prophet hybrid model

“…The bene ts of this approach include structural risk minimization and the ability to use small sample sizes. It is an application of SVM in the eld of regression (Essam et al 2022). Its regression process is as follows.…”

Application of machine learning in corrosion inhibition study

“…Machine Learning enables computers to learn without the need for explicit programming. Machine Learning is a broad field that encompasses a wide range of machine learning operations such as clustering, classification, and the development of predictive models [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20]. Machine learning is also being used in corrosion science and engineering [21][22][23][24][25][26][27][28][29][30][31].…”