Spatio-temporal prediction of water quality parameters of a reservoir using genetic programming and least square support vector machine

2018 ◽  
Vol 8 (3) ◽  
pp. 273
Author(s):  
Mrunalini Shivaji Jadhav ◽  
Arundhati Suresh Warke ◽  
Kanchan Chandrashekhar Khare
Keyword(s):  
Water Quality ◽  
Support Vector Machine ◽  
Genetic Programming ◽  
Quality Parameters ◽  
Least Square ◽  
Water Quality Parameters ◽  
Support Vector ◽  
Temporal Prediction ◽  
Spatio Temporal

A Novel LSSVM Model Integrated with GBO Algorithm to Assessment of Water Quality Parameters

2021 ◽  
Author(s):  
Mojtaba Kadkhodazadeh ◽  
Saeed Farzin
Keyword(s):  
Water Quality ◽  
Time Delays ◽  
Evaluation Criteria ◽  
Quality Parameters ◽  
Least Square ◽  
Water Quality Parameters ◽  
Support Vector ◽  
Sodium Absorption ◽  
Input Combination ◽  
Benchmark Datasets

Abstract In this study, a novel least square support vector machine (LSSVM) model integrated with gradient-based optimizer (GBO) algorithm is introduced for assessment of water quality parameters. For this purpose, three stations including Ahvaz, Armand, and Gotvand in the Karun river basin have been selected to model electrical conductivity (EC), and total dissolved solids (TDS). First, to prove the superiority of the LSSVM-GBO algorithm, the performance is evaluated with three benchmark datasets (Housing, LVST, Servo). Then, the results of the new hybrid algorithm were compared with those of artificial neural network (ANN), adaptive neuro-fuzzy interface system (ANFIS), and LSSVM algorithms. Input combination for assessment of water quality parameters EC and TDS consists of Ca+ 2, Cl-1, Mg+ 2, Na+ 1, SO4, HCO3, sodium absorption ratio (SAR), sum cation (Sum.C), sum anion (Sum.A), PH, and Q. The modelling results based on evaluation criteria showed the significant performance of LSSVM-GBO among all benchmark datasets and algorithms. Other results showed that in Ahvaz station, Sum.C, Sum.A, and Na+1 parameters, and in Gotvand and Armand stations, Sum.C, Sum.A, and Cl-1 parameters have the greatest impact on modelling EC and TDS parameters. In the next step, EC and TDS modelling was performed based on the best input combination and the best algorithm in different time delays. Based on the results, the highest accuracy of modelling EC and TDS parameters in Gotvand station was [0] month time delays.

scholarly journals Estimating Chlorophyll-a and Dissolved Oxygen Based on Landsat 8 Bands Using Support Vector Machine and Recursive Partitioning Tree Regressions

Proceedings ◽  
2019 ◽  
Vol 42 (1) ◽  
pp. 25
Author(s):  
NimishaWagle ◽  
Tri Dev Acharya ◽  
Dong Ha Lee
Keyword(s):  
Water Quality ◽  
Support Vector Machine ◽  
Dissolved Oxygen ◽  
Chlorophyll A ◽  
Recursive Partitioning ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Support Vector ◽  
Landsat 8 ◽  
Large Lakes

In general, water quality mapping is done by interpolation of in situ measurement samples. Often, these parameters change with time. Due to geographic variability and the lack of budget in Nepal, such measurements are done less often. Remote sensors that collect spectral information continually can be very useful in the regular monitoring of water quality parameters. Landsat Operational Land Imager (OLI) bands have been used to estimate water quality parameters. In this work, we model two water quality parameters: chlorophyll-a (Chl-a) and dissolved oxygen (DO) using sequential minimal optimization regression (SMOreg), which implements a support vector machine (SVM) algorithm and recursive partitioning tree (REPTree) regressions. A total of 19 measurements were taken from Phewa Lake, Nepal and various secondary bands were derived from using Landsat 8 Operational Land Imager (OLI) bands. These bands underwent feature selection, and regression models were created based on selected bands and sample data. The results showed satisfactory modelling of water quality parameters using Landsat 8 OLI bands in Phewa Lake. Due to a limited number of data, cross-validation was done with 10 folds. The SVM showed a better result than the REPTree regression. For future studies, the performance can be further evaluated in large lakes with larger sample numbers and other water quality parameters.

scholarly journals Spatio-Temporal Analysis of Water Quality Parameters in Machángara River with Nonuniform Interpolation Methods

Water ◽  
2016 ◽  
Vol 8 (11) ◽  
pp. 507 ◽  
Author(s):  
Iván Vizcaíno ◽  
Enrique Carrera ◽  
Margarita Sanromán-Junquera ◽  
Sergio Muñoz-Romero ◽  
José Luis Rojo-Álvarez ◽  
...  
Keyword(s):  
Water Quality ◽  
Temporal Analysis ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Interpolation Methods ◽  
Spatio Temporal

scholarly journals GIS-Based Modelling of Water Quality in Different Hilsha Spawning Grounds and Sanctuaries of Bangladesh

2018 ◽  
pp. 1-11
Author(s):  
Rumana Yasmin ◽  
Mehady Islam
Keyword(s):  
Water Quality ◽  
Present Status ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Total Alkalinity ◽  
Total Hardness ◽  
Quality Model ◽  
Spawning Grounds ◽  
Temporal Modelling ◽  
Spatio Temporal

The current study was performed to monitor in situ condition and spatio-temporal modelling of the present status of water quality parameters of different spawning grounds and sanctuaries of Hilsha. The study was conducted in nine sites in lower Padma River (Maowa) to lower Meghna River (Bhola, Patuakhali) from 1 August 2015 to 31 January 2016. This study demonstrates surface water temperature, salinity, conductivity and transparency were ranged from 19.00-33.00°C, 0.10-2.90 ppt, 125.60-4720.00 µS/cm and 6.60-74.00 cm respectively. The values of pH, DO, free CO2, total alkalinity, total hardness and free NH3 were varied from 6.00-9.50, 4.50-11.60 mg/L, 3.46-24.00 mg/L, 33.00-172.50 mg/L, 34.20-1291.00 mg/L and 0.20-1.40 mg/L respectively. Moreover, water quality model reveals that the present status of some water quality parameters (free CO2, free NH3, transparency) deviated from optimum condition suitable for the normal physiological process and spawning of Hilsha.

scholarly journals ASSESSMENT OF SEAGRASS PERCENT COVER AND WATER QUALITY USING UAV IMAGES AND FIELD MEASUREMENTS IN BOLINAO, PANGASINAN

2019 ◽  
Vol XLII-4/W19 ◽  
pp. 233-240
Author(s):  
M. K. M. R. Guerrero ◽  
J. A. M. Vivar ◽  
R. V. Ramos ◽  
A. M. Tamondong
Keyword(s):  
Water Quality ◽  
Regression Analysis ◽  
Chlorophyll A ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Support Vector ◽  
Percent Cover ◽  
Chl A ◽  
Relationship Of ◽  
The Relationship

Abstract. The sensitivity to changes in water quality inherent to seagrass communities makes them vital for determining the overall health of the coastal ecosystem. Numerous efforts including community-based coastal resource management, conservation and rehabilitation plans are currently undertaken to protect these marine species. In this study, the relationship of water quality parameters, specifically chlorophyll-a (chl-a) and turbidity, with seagrass percent cover is assessed quantitatively. Support Vector Machine, a pixel-based image classification method, is applied to determine seagrass and non-seagrass areas from the orthomosaic which yielded a 91.0369% accuracy. In-situ measurements of chl-a and turbidity are acquired using an infinity-CLW water quality sensor. Geostatistical techniques are utilized in this study to determine accurate surfaces for chl-a and turbidity. In two hundred interpolation tests for both chl-a and turbidity, Simple Kriging (Gaussian-model type and Smooth- neighborhood type) performs best with Mean Prediction equal to −0.1371 FTU and 0.0061 μg/L, Root Mean Square Standardized error equal to −0.0688 FTU and −0.0048 μg/L, RMS error of 8.7699 FTU and 1.8006 μg/L and Average Standard Error equal to 10.8360 FTU and 1.6726 μg/L. Zones are determined using fishnet tool and Moran’s I to calculate for the seagrass percent cover. Ordinary Least Squares (OLS) is used as a regression analysis to quantify the relationship of seagrass percent cover and water quality parameters. The regression analysis result indicates that turbidity has an inverse relationship while chlorophyll-a has a direct relationship with seagrass percent cover.

scholarly journals Integration of advanced optimization algorithms into least-square support vector machine (LSSVM) for water quality index prediction

2021 ◽  
Author(s):  
See Leng Chia ◽  
Min Yan Chia ◽  
Chai Hoon Koo ◽  
Yuk Feng Huang
Keyword(s):  
Water Quality ◽  
Support Vector Machine ◽  
Water Quality Index ◽  
Quality Index ◽  
Optimization Algorithms ◽  
Least Square ◽  
Support Vector ◽  
Hybrid Particle Swarm Optimization ◽  
Mutation Strategy ◽  
Whale Optimization

Abstract Machine learning models hybridized with optimization algorithms have been applied to many real-life applications, including the prediction of water quality. However, the emergence of newly developed advanced algorithms can provide new scopes and possibilities for further enhancements. In this study, the least-square support vector machine (LSSVM) integrated with advanced optimization algorithms is presented, for the first time, in the prediction of water quality index (WQI) at the Klang River of Malaysia. Thereafter, the LSSVM model using RBF kernel was optimized using the hybrid particle swarm optimization and genetic algorithm (HPSOGA), whale optimization based on self-adapting parameter adjustment and mix mutation strategy (SMWOA) as well as ameliorative moth-flame optimization (AMFO) separately. It was found that the SMWOA-LSSVM model had the better performance for WQI prediction by having the best achievement root means square error (RMSE), mean absolute error (MAE), coefficient of determination (R2) and mean absolute percentage error (MAPE). Comprehensive comparison was done using the global performance indicator (GPI), whereby the SMWOA-LSSVM had the highest average score of 0.31. This could be attributed to the internal architecture of the SMWOA, which was catered to avoid local optima within short optimization period.

scholarly journals Extracting the spatio-temporal linkages between land use/land cover and water quality parameters using spatio-temporal weighted regression

2018 ◽  
Vol 53 (4) ◽  
pp. 205-218
Author(s):  
Farid Karimipour ◽  
Arash Madadi ◽  
Mohammad Hosein Bashough
Keyword(s):  
Water Quality ◽  
Land Use ◽  
Land Cover ◽  
Temporal Variations ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Spatial And Temporal Variations ◽  
Weighted Regression ◽  
Land Use Land Cover ◽  
Spatio Temporal

Abstract Studies in water quality management have indicated significant relationships between land use/land cover (LULC) variables and water quality parameters. Thus, understanding this linkage is essential in protecting and developing water resources. This article extends the conventional geographical weighted regression (GWR) to a temporal version in order to take both spatial and temporal variations of such linkages into account, which has been ignored by many of the previous efforts. The approach has been evaluated for total nitrates and nitrites' concentration as the case study. For this, observations of 45 water quality sampling stations were examined in a time interval of 20 years (1992–2011), and the linkages between LULC variables and NO2 + NO3 concentration were extracted through Pearson correlation coefficient as a global regression model, the conventional geographic weighted regression, and the proposed spatio-temporal weighted regression (STWR). Comparing the results based on two global criteria of goodness-of-fitness (R2) and residual sum of squares (RSS) verifies that the simultaneous consideration of spatial and temporal variations by STWR substantially improves the results.

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