Spatiotemporal assessment of water quality monitoring network in a tropical river

Camara, Moriken; Jamil, Nor Rohaizah; Abdullah, Ahmad Fikri; Hashim, Rohasliney

doi:10.1007/s10661-019-7906-1

Cited by 9 publications

(4 citation statements)

References 20 publications

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“…All of these overall high nutrient levels could be attributed to the decomposition of organic matter and in particular litter in streams [38] [39].…”

Section: Environmental Variablesmentioning

confidence: 99%

Spatiotemporal Variation of Benthic Macroinvertebrates in Some Tropical Forest Streams of the Nyong Catchment (Cameroon)

Dzavi¹,

Menbohan²,

Mboye³

et al. 2022

OJAppS

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With the aim of assessing the benthic macroinvertebrates' diversity, a study was carried out in some tropical forest streams of the Nyong River catchment in Cameroon from February 2019 to February 2020. A total of 167 samples were carried out from 13 stations during 13 months. Some environmental variables were measured. These parameters varied more or less from station to station but significantly at the temporal level. In this study, 13,690 benthic macroinvertebrates belonging to 4 phyla, 7 classes, 16 orders and 93 families were collected. The benthic macroinvertebrates were more abundant and more diversified in the stations whose waters are well oxygenated and present a moderate current compared to the stations with a very weak current. The diversity varied significantly from 1.33 ± 0.14 bits/ind to 2.00 ± 0.35 bits/ind and the high values were found in stations with multiple substrates and well-oxygenated waters. Temporally, the diversity varied significantly from 1.10 ± 0.16 bits/ind in NM sampling station in February 2020 to 1.87 ± 0.1 bits/ind in the OB sampling station in September. In addition, the settlement was more abundant during the short dry season, more precisely during the month of August (1471 individuals) but richer during the long dry season during the month of February (54 families). The distribution of the abundances of the benthic fauna in the different stations made it possible to identify five typological groups using the rarefaction curves, the ascending hierarchical classification and the principal component analyses. Each of these five groups is characterized by a specific taxonomic richness, composition and abundance.

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“…All of these overall high nutrient levels could be attributed to the decomposition of organic matter and in particular litter in streams [38] [39].…”

Section: Environmental Variablesmentioning

confidence: 99%

Spatiotemporal Variation of Benthic Macroinvertebrates in Some Tropical Forest Streams of the Nyong Catchment (Cameroon)

Dzavi¹,

Menbohan²,

Mboye³

et al. 2022

OJAppS

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“…The highest mean values of NO2 --N, Fe, and coliform were found in Cluster 4. COD, TOC, and SO4 2were found high at all clusters which could be the representative of the overall effects of aquacultural activities [28].…”

Section: ) Spatial and Temporal Variation Of Water Qualitymentioning

confidence: 67%

Application of GIS and Multi-Criteria Statistical Techniques in Assessing Water Quality in the Coastal Province of Vietnamese Mekong Delta

Giao

Nhien

2021

App. Envi. Res.

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The study was conducted to evaluate the quality and spatial distribution of surface water quality in Soc Trang, a coastal province using Geographic information system (GIS) and multivariate statistical analysis. Water monitoring data was collected from 19 sampling locations with 19 parameters were analyzed from February 2019 to August 2020. The results indicated that water quality was contaminated with organic matters, nutrients, coliforms and salinity. Water quality index (WQI=22–73) indicated that water quality was from poor to medium level. Cluster analysis (CA) classified 19 monitoring sites into 7 groups and 19 months into 3 seasons including rainy season, rainy season-early dry season, dry season-early rainy season. CA results showed that the location and frequency of water quality monitoring could be significantly reduced, saving up to 75% the monitoring costs. The maps of the polluted parameters (TSS, DO, BOD, COD, TOC, NH4+-N, NO2--N, Coliform, Fe, Cl-) illustrated that the areas located in the interior fields and near the sea had poorer water quality compared to the areas adjacent to the Hau River. The combination of multivariable statistics and GIS was very useful for spatial and temporal analysis of water quality monitoring data.

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“…Nevertheless, frequent and accurate testing and sampling of existing water bodies are time-consuming and exorbitant, resulting in the tedious study and limited calibration and validation of WQ evaluation 9 , 10 . Developing appropriate data-intelligence models for WQ monitoring is part and parcel of better surface water management 11 , 12 .…”

Section: Introductionmentioning

confidence: 99%

An improved adaptive neuro fuzzy inference system model using conjoined metaheuristic algorithms for electrical conductivity prediction

Ahmadianfar¹,

Shirvani-Hosseini

et al. 2022

Sci Rep

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Precise prediction of water quality parameters plays a significant role in making an early alert of water pollution and making better decisions for the management of water resources. As one of the influential indicative parameters, electrical conductivity (EC) has a crucial role in calculating the proportion of mineralization. In this study, the integration of an adaptive hybrid of differential evolution and particle swarm optimization (A-DEPSO) with adaptive neuro fuzzy inference system (ANFIS) model is adopted for EC prediction. The A-DEPSO method uses unique mutation and crossover processes to correspondingly boost global and local search mechanisms. It also uses a refreshing operator to prevent the solution from being caught inside the local optimal solutions. This study uses A-DEPSO optimizer for ANFIS training phase to eliminate defects and predict accurately the EC water quality parameter every month at the Maroon River in the southwest of Iran. Accordingly, the recorded dataset originated from the Tange-Takab station from 1980 to 2016 was operated to develop the ANFIS-A-DEPSO model. Besides, the wavelet analysis was jointed to the proposed algorithm in which the original time series of EC was disintegrated into the sub-time series through two mother wavelets to boost the prediction certainty. In the following, the comparison between statistical metrics of the standalone ANFIS, least-square support vector machine (LSSVM), multivariate adaptive regression spline (MARS), generalized regression neural network (GRNN), wavelet-LSSVM (WLSSVM), wavelet-MARS (W-MARS), wavelet-ANFIS (W-ANFIS) and wavelet-GRNN (W-GRNN) models was implemented. As a result, it was apparent that not only was the W-ANFIS-A-DEPSO model able to rise remarkably the EC prediction certainty, but W-ANFIS-A-DEPSO (R = 0.988, RMSE = 53.841, and PI = 0.485) also had the edge over other models with Dmey mother in terms of EC prediction. Moreover, the W-ANFIS-A-DEPSO can improve the RMSE compared to the standalone ANFIS-DEPSO model, accounting for 80%. Hence, this model can create a closer approximation of EC value through W-ANFIS-A-DEPSO model, which is likely to act as a promising procedure to simulate the prediction of EC data.

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Spatiotemporal assessment of water quality monitoring network in a tropical river

Cited by 9 publications

References 20 publications

Spatiotemporal Variation of Benthic Macroinvertebrates in Some Tropical Forest Streams of the Nyong Catchment (Cameroon)

Spatiotemporal Variation of Benthic Macroinvertebrates in Some Tropical Forest Streams of the Nyong Catchment (Cameroon)

Application of GIS and Multi-Criteria Statistical Techniques in Assessing Water Quality in the Coastal Province of Vietnamese Mekong Delta

An improved adaptive neuro fuzzy inference system model using conjoined metaheuristic algorithms for electrical conductivity prediction

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