Soil moisture simulation using individual versus ensemble soft computing models

Zounemat‐Kermani, Mohammad; Kermani, Soudabeh Golestani; Alizamir, Meysam; Fadaee, Marzieh

doi:10.1007/s13762-022-04202-y

Cited by 4 publications

(3 citation statements)

References 44 publications

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“…In the context of organic water quality indicators, many scholars consider indirect methods employing artificial intelligence and machine learning [5][6][7], such as artificial neural networks, extreme learning machines, random forests, and swarm intelligence algorithms. For example, the measurement of biochemical oxygen demand (BOD5) involves determining the amount of molecular oxygen consumed in 1 liter of water at 20˚C over a 5-day incubation period, making the process time-consuming.…”

Section: Plos Onementioning

confidence: 99%

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Design and implementation of a Li River water quality monitoring and analysis system based on outlier data analysis

Lu,

Zou,

et al. 2024

PLoS ONE

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The detection of water quality indicators such as Temperature, pH, Turbidity, Conductivity, and TDS involves five national standard methods. Chemically based measurement techniques may generate liquid residue, causing secondary pollution. The water quality monitoring and data analysis system can effectively address the issues that conventional methods require multiple pieces of equipment and repeated measurements. This paper analyzes the distribution characteristics of the historical data from five sensors at a specific time, displays them graphically in real time, and provides an early warning of exceeding the standard; It selects four water samples from different sections of the Li River, based on the national standard method, the average measurement errors of Temperature, PH, TDS, Conductivity and Turbidity are 0.98%, 2.23%, 2.92%, 3.05% and 3.98%.;It further uses the quartile method to analyze the outlier data over 100,000 records and five historical periods are selected. Experiment results show the system is relatively stable in measuring Temperature, PH and TDS, and the proportion of outlier is 0.42%, 0.84% and 1.24%. When Turbidity and Conductivity are measured, the proportion is 3.11% and 2.92%. In the experiment of using 7 methods to fill outlier, K nearest neighbor algorithm is better than others. The analysis of data trends, outliers, means, and extreme values assists in making decisions, such as updating and maintaining equipment, addressing extreme water quality situations, and enhancing regional water quality oversight.

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Section: Plos Onementioning

confidence: 99%

“…Similarly, spectral techniques are primarily used for the measurement of chemical oxygen demand (COD) [9], making online monitoring challenging. Analyzing literature [5][6][7][8][9] reveals that, for organic water quality indicators, due to cost and measurement accuracy issues, and online monitoring methods are not widely adopted.…”

Section: Plos Onementioning

confidence: 99%

Design and implementation of a Li River water quality monitoring and analysis system based on outlier data analysis

Lu,

Zou,

et al. 2024

PLoS ONE

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“…Considering this fact and the abovementioned drawbacks in measuring direct values for BOD, application of indirect methods like mathematical (Sibil et al 2014 ) and artificial intelligence machine learning (AI-ML) methodologies would be worthy of consideration. Having mentioned that, AI and ML techniques have proven to be effective and efficient at simulating, optimizing, and predicting hydro-environmental applications (Zounemat-Kermani et al 2022 ). In essence, AI-MLs are developed based on historical datasets, trained by simple to sophisticated optimization algorithms, and make inferences in complex systems.…”

Section: Introductionmentioning

confidence: 99%

Modelling biochemical oxygen demand using improved neuro-fuzzy approach by marine predators algorithm

Adnan,

Dai,

Kisi

et al. 2023

Environ Sci Pollut Res

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Biochemical oxygen demand (BOD) is one of the most important parameters used for water quality assessment. Alternative methods are essential for accurately prediction of this parameter because the traditional method in predicting the BOD is time-consuming and it is inaccurate due to inconstancies in microbial multiplicity. In this study, the applicability of four hybrid neuro-fuzzy (ANFIS) methods, ANFIS with genetic algorithm (GA), ANFIS with particle swarm optimization (PSO), ANFIS with sine cosine algorithm (SCA), and ANFIS with marine predators algorithm (MPA), was investigated in predicting BOD using distinct input combinations such as potential of hydrogen (pH), dissolved oxygen (DO), electrical conductivity (EC), water temperature (WT), suspended solids (SS), chemical oxygen demand (COD), total nitrogen (TN), and total phosphorus (T-P) acquired from two river stations, Gongreung and Gyeongan, South Korea. The applicability of multi-variate adaptive regression spline (MARS) in determination of the best input combination was examined. The ANFIS-MPA was found to be the best model with the lowest root mean square error and mean absolute error and the highest determination coefficient. It improved the root mean square error of ANFIS-PSO, ANFIS-GA, and ANFIS-SCA models by 13.8%, 12.1%, and 6.3% for Gongreung Station and by 33%, 25%, and 6.3% for Gyeongan Station in the test stage, respectively.

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Comparative Assessment of Different Machine Learning Models to Estimate Daily Soil Moisture

Nagashree,

Nema

2023

Lecture Notes in Civil Engineering

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Soil moisture simulation using individual versus ensemble soft computing models

Cited by 4 publications

References 44 publications

Design and implementation of a Li River water quality monitoring and analysis system based on outlier data analysis

Design and implementation of a Li River water quality monitoring and analysis system based on outlier data analysis

Modelling biochemical oxygen demand using improved neuro-fuzzy approach by marine predators algorithm

Comparative Assessment of Different Machine Learning Models to Estimate Daily Soil Moisture

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