Chlorophyll-A Prediction of Lakes with Different Water Quality Patterns in China Based on Hybrid Neural Networks

Dynamics of the wetland ecosystem health in urban and rural settings in high altitude ecoregion

The focus of the present study was to assess the dynamics of wetland ecosystem health in both urban and rural settings situated in the high-altitude Kashmir Himalayan ecoregion. The basic aim was to identify the drivers responsible for wetland degradation in order to sustain ecosystem services effectively. To achieve this, we examined water quality, trophic status, fish species diversity and human disturbances by analyzing changes in land use and land cover (LULC) since 1980. For the limnological characterization of the two wetlands, we evaluated a total of 21 physico-chemical parameters at 24 sites. Two-way analysis of variance revealed significant (p < 0.05) spatial and temporal variability in the water quality parameters. The trophic state index values of 67.7 and 76.7 indicated that the rural and urban wetlands were in eutrophic and hypertrophic status, respectively, signifying potential environmental stress. The data on fish fauna indicated a decline in fish species over the past 40 years, particularly the schizothoracine species. Urban wetlands showed a more significant decrease in species (06) compared to rural wetlands (01). LULC mapping and change analysis employing the visual interpretation technique showed significant transformations in the immediate catchment of wetlands. Substantial growth in the built-up (433.2 % and 2620 %) and decrease in aquatic vegetation (-83.4 % and - 97.5 %) in the immediate catchment was recorded in both the urban and rural wetlands respectively from 1980 to 2020. Our findings demonstrated a relationship between LULC classes and water quality parameters, with an increase in built-up and road areas showing a significant positive correlation with the rise in decadal mean values of total phosphorus, orthophosphorus, nitrate nitrogen, ammonical nitrogen, and calcium content. Based on these observations, we concluded that changes in land use and land cover within the immediate catchment areas of the wetlands were the primary drivers responsible for the deterioration of wetland ecosystem health.

Forecasting water quality variable using deep learning and weighted averaging ensemble models

Water quality variables, including chlorophyll-a (Chl-a), play a pivotal role in comprehending and evaluating the condition of aquatic ecosystems. Chl-a, a pigment present in diverse aquatic organisms, notably algae and cyanobacteria, serves as a valuable indicator of water quality. Thus, the objectives of this study encompass: (1) the assessment of the predictive capabilities of four deep learning (DL) models - namely, recurrent neural network (RNN), long short-term memory (LSTM), gated recurrence unit (GRU), and temporal convolutional network (TCN) - in forecasting Chl-a concentrations; (2) the incorporation of these DL models into ensemble models (EMs) employing genetic algorithm (GA) and non-dominated sorting genetic algorithm (NSGA-II) to harness the strengths of each standalone model; and (3) the evaluation of the efficacy of the developed EMs. Utilizing data collected at 15-min intervals from Small Prespa Lake (SPL) in Greece, the models employed hourly Chl-a concentration lag times, extending up to 6 h, as models' inputs to forecast Chla (t+1). The proposed models underwent training on 70% of the dataset and were subsequently validated on the remaining 30%. Among the standalone DL models, the GRU model exhibited superior performance in Chl-a forecasting, surpassing the RNN, LSTM, and TCN models by 8%, 2%, and 2%, respectively. Furthermore, the integration of DL models through single-objective GA and multi-objective NSGA-II optimization algorithms yielded hybrid models adept at effectively forecasting both low and high Chl-a concentrations. The ensemble model based on NSGA-II outperformed standalone DL models as well as the GA-based model across a range of evaluation indices. For instance, considering the R-squared metric, the study's findings demonstrated that the EM-NSGA-II stands out with exceptional effectiveness compared to DL and EM-GA models, showcasing improvements of 14% (RNN), 8% (LSTM), 6% (GRU), 8% (TCN), and 3% (EM-GA) during the testing phase.

Potable and irrigation suitability of different water supply systems in riverine communities at river Cauvery distributaries, India

Current study focused on investigating the pollution loads of open well, bore well and drinking water from riverine community sites. In addition, drinking and irrigation suitability assessment were also performed by using user specific water quality indices (USWQI) and parameters quality index (PQI). Principal component analysis (PCA) was also performed with physio-chemical parameters. Notable variation was found in most of the water quality parameters at major hamlet and some places exceeded the standards prescribed by authorized organizations. The USWQI was 97.53 to 38.15 in open wells, 96.06 to 68.23 in bore wells, and 88.64 to 74.16 in tap water (drinking water). Among the settlement, highest water quality was recorded at Vilangudi, while the lowest quality found in Karaipakkam area. The predominant drinking water samples were estimated as good quality for human health and hygiene whereas none of the sample was found to be excellent. Open and bore well water samples were of good quality and suitable for agriculture purposes except the few samples which were estimated as poor and fair quality.

Coupling hydrogeochemistry and stable isotopes (δ2H, δ18O and δ13C) to identify factors affecting arsenic enrichment of surface water and groundwater in Precambrian sedimentary rocks, eastern salt range, Punjab, Pakistan

The study area is a part of the Salt Range, where water quality is being deteriorated by natural and anthropogenic sources. This research integrates water quality assessment, arsenic enrichment, hydrogeochemical processes, groundwater recharge and carbon sources in aquifer. Total dissolved solid (TDS) contents in springs water, lake water and groundwater are in range of 681-847 mg/L, 2460-5051 mg/L and 513-7491 mg/L, respectively. The higher concentrations of magnesium and calcium in water bodies next to sodium are because of carbonates, sulfates, halite and silicates dissolution. The average concentrations of ions in groundwater are in order of HCO3- > SO42- > Cl- > Na+  > Mg2+ > Ca2+ > K+ > NO3-, virtually analogous to springs water, but different from lake water, categorized as poor quality and unfit for drinking purposes. Based on major ions hydrochemistry, NaCl and mixed Ca-Mg-Cl type hydrochemical facies are associated with concentration of arsenic (4.2-39.5 µg/L) in groundwater. Groundwater samples (70%) having arsenic concentration (11 ≤ As ≤ 39.5 µg/L) exceeded from World Health Organization (WHO) guideline (As ≤ 10 µg/L) in near neutral to slightly alkaline (6.7 ≤ pH ≤ 8.3), positive Eh(6 ≤ Eh ≤ 204 mV), signifying its oxic condition. Eh-pH diagrams for arsenic and iron indicate that 80% of groundwater for arsenic and iron were in compartments of HAsO42- and Fe(OH)3, unveil oxic environment. Arsenic is moderately positive correlated with TDS, sodium, chloride, bicarbonate, nitrate, sulfate and weak negative with δ13CDIC in surface and groundwater, forecasting multiple sources of arsenic to aquifer. Stable isotopes of waters show recharge of groundwater from local rain and lake water. The lower δ13CDIC values of groundwater are modified by influx of CO2 produced during biological oxidation of soil natural organic matter.

Remote sensing to detect harmful algal blooms in inland waterbodies

Harmful algal blooms (HABs) are an issue of concern for water management worldwide. As such, effective monitoring strategies of HAB spatio-temporal variability in waterbodies are needed. Remote sensing has become an increasingly important tool for HAB detection and monitoring in large lakes. However, accurate HAB detection in small-medium waterbodies via satellite data remains a challenge. Current barriers include the waterbody size, the limited freely available high resolution satellite data, and the lack of field calibration data. To test the applicability of remote sensing for detecting HABs in small-medium waterbodies, three satellites (Planetscope, Sentinel-2 and Landsat-8) were used to understand how spatial resolution, the availability of spectral bands, and the waterbody size itself effect HAB detection skill. Different algorithms and a non-parametric method, Self-Organizing Map (SOM), were tested. Curvature Around Red and NIR minus Red had the best HAB detection skill of the 20 existing algorithms that were tested. Landsat 8 and Sentinel 2 were the best satellites for HAB detection in small to medium waterbodies. The most critical attribute for detecting HABs were the available satellite bands, which determine the detection algorithms that can be used. Importantly, algorithm performance was mostly unrelated to waterbody size. However, there remain some barriers in utilizing satellite data for HAB detection, including algae dynamics, macrophyte cover within the waterbody, weather effects, and the correction models for satellite data. Moreover, it is important to consider the match time between satellite overpass and sampling activities for calibration. Given these challenges, integrating regular sampling activities and remote sensing is recommended for monitoring and managing small-medium waterbodies.

Research on Chlorophyll-a Concentration Retrieval Based on BP Neural Network Model—Case Study of Dianshan Lake, China

The Chlorophyll-a (Chl-a) concentration is an important indicator of water environmental conditions; thus, the simultaneous monitoring of large-area water bodies can be realized through the remote sensing-based retrieval of the Chl-a concentrations. The back propagation (BP) neural network learning method has been widely used for the remote sensing retrieval of water quality in first and second-class water bodies. However, many Chl-a concentration measurements must be used as learning samples with this method, which is constrained by the number of samples, due to the limited time and resources available for simultaneous measurements. In this paper, we conduct correlation analysis between the Chl-a concentration data measured at Dianshan Lake in 2020 and 2021 and synchronized Landat-8 data. Through analysis and study of the radiative transfer model and the retrieval method, a BP neural network retrieval model based on multi-phase Chl-a concentration data is proposed, which allows for the realization of remote sensing-based Chl-a monitoring in third-class water bodies. An analysis of spatiotemporal distribution characteristics was performed, and the method was compared with other constructed models. The research results indicate that the retrieval performance of the proposed BP neural network model is better than that of models constructed using multiple regression analysis and curve estimation analysis approaches, with a coefficient of determination of 0.86 and an average relative error of 19.48%. The spatial and temporal Chl-a distribution over Dianshan Lake was uneven, with high concentrations close to human production and low concentrations in the open areas of the lake. During the period from 2020 to 2021, the Chl-a concentration showed a significant upward trend. These research findings provide reference for monitoring the water environment in Dianshan Lake.

Evidence of the Anthropic Impact on a Crustacean Zooplankton Community in Two North Patagonian Lakes

Lately, agriculture, livestock, forestry, and aquaculture activities have been greatly developed in Chilean North Patagonia, negatively impacting the balance of the environmental conditions in lakes and affecting the development and survival of several native species. The aim of this study was to assess the anthropic impact on a zooplankton community in two North Patagonian lakes. We collected samples from four sites belonging to Lake Icalma and Lake Llanquihue, including four replicates per site. Water samples were analyzed for physicochemical characteristics and zooplankton communities. We focused on the presence of Daphnia pulex, a species of zooplanktonic crustacean that performs a key role in capturing energy from primary producers to deliver it to final consumers such as fish. We found that Llanquihue showed higher total phosphorus, nitrogen, copper, iron, manganese, total dissolved solids (TDS), and conductivity (EC) than Icalma. Furthermore, ecological variables were greatly decreased due to total P, total N, manganese, copper, total dissolved solids, and conductivity, which changed the species dominance of the zooplankton community in Llanquihue, indicating some degree of anthropization. This study provides fundamental information on the anthropogenic impact on water quality, as well as on zooplankton diversity, highlighting the importance of monitoring the health of these North Patagonia freshwater ecosystems.

Water Quality Prediction Method Based on Multi-Source Transfer Learning for Water Environmental IoT System

Water environmental Internet of Things (IoT) system, which is composed of multiple monitoring points equipped with various water quality IoT devices, provides the possibility for accurate water quality prediction. In the same water area, water flows and exchanges between multiple monitoring points, resulting in an adjacency effect in the water quality information. However, traditional water quality prediction methods only use the water quality information of one monitoring point, ignoring the information of nearby monitoring points. In this paper, we propose a water quality prediction method based on multi-source transfer learning for a water environmental IoT system, in order to effectively use the water quality information of nearby monitoring points to improve the prediction accuracy. First, a water quality prediction framework based on multi-source transfer learning is constructed. Specifically, the common features in water quality samples of multiple nearby monitoring points and target monitoring points are extracted and then aligned. According to the aligned features of water quality samples, the water quality prediction models based on an echo state network at multiple nearby monitoring points are established with distributed computing, and then the prediction results of distributed water quality prediction models are integrated. Second, the prediction parameters of multi-source transfer learning are optimized. Specifically, the back propagates population deviation based on multiple iterations, reducing the feature alignment bias and the model alignment bias to improve the prediction accuracy. Finally, the proposed method is applied in the actual water quality dataset of Hong Kong. The experimental results demonstrate that the proposed method can make full use of the water quality information of multiple nearby monitoring points to train several water quality prediction models and reduce the prediction bias.

Inversion of Chlorophyll-a Concentration in Donghu Lake Based on Machine Learning Algorithm

Machine learning algorithm, as an important method for numerical modeling, has been widely used for chlorophyll-a concentration inversion modeling. In this work, a variety of models were built by applying five kinds of datasets and adopting back propagation neural network (BPNN), extreme learning machine (ELM), support vector machine (SVM). The results revealed that modeling with multi-factor datasets has the possibility to improve the accuracy of inversion model, and seven band combinations are better than seven single bands when modeling, Besides, SVM is more suitable than BPNN and ELM for chlorophyll-a concentration inversion modeling of Donghu Lake. The SVM model based on seven three-band combination dataset (SVM3) is the best inversion one among all multi-factor models that the mean relative error (MRE), mean absolute error (MAE), root mean square error (RMSE) of the SVM model based on single-factor dataset (SF-SVM) are 30.82%, 9.44 μg/L and 12.66 μg/L, respectively. SF-SVM performs best in single-factor models, MRE, MAE, RMSE of SF-SVM are 28.63%, 13.69 μg/L and 16.49 μg/L, respectively. In addition, the simulation effect of SVM3 is better than that of SF-SVM. On the whole, an effective model for retrieving chlorophyll-a concentration has been built based on machine learning algorithm, and our work provides a reliable basis and promotion for exploring accurate and applicable chlorophyll-a inversion model.

Stochastic modeling of chlorophyll-a for probabilistic assessment and monitoring of algae blooms in the Lower Nakdong River, South Korea

Eutrophication is one of the critical water quality issues in the world nowadays. Various studies have been conducted to explore the contributing factors related to eutrophication symptoms. However, in the field of eutrophication modeling, the stochastic nature associated with the eutrophication process has not been sufficiently explored, especially in a multivariate stochastic modeling framework. In this study, a multivariate hidden Markov model (MHMM) that can consider the spatio-temporal dependence in chlorophyll-a concentration over the Nakdong River of South Korea was proposed. The MHMM can effectively cluster the intra-seasonal and inter-annual variability of chlorophyll-a, thereby enabling us to understand the spatio-temporal evolutions of algal blooms. The relationships between hydro-climatic conditions (e.g., temperature and river flow) and chlorophyll-a concentrations were evident, whereas a relatively weak relationship with water quality parameters was observed. The MHMM enables us to effectively infer the conditional probability of the eutrophication state for the following month. The self-transition likelihood of staying in the current state is substantially higher than the likelihood of moving to other states. Moreover, the proposed modeling approach can effectively offer a probabilistic decision-support framework for constructing an alert classification of the eutrophication. The potential use of the proposed modeling framework was also provided.

A database of chlorophyll and water chemistry in freshwater lakes

Measures of chlorophyll represent the algal biomass in freshwater lakes that is often used by managers as a proxy for water quality and lake productivity. However, chlorophyll concentrations in lakes are dependent on many interacting factors, including nutrient inputs, mixing regime, lake depth, climate, and anthropogenic activities within the watershed. Therefore, integrating a broad scale dataset of lake physical, chemical, and biological characteristics can help elucidate the response of freshwater ecosystems to global change. We synthesized a database of measured chlorophyll a (chla) values, associated water chemistry variables, and lake morphometric characteristics for 11,959 freshwater lakes distributed across 72 countries. Data were collected based on a systematic review examining 3322 published manuscripts that measured lake chla, and we supplemented these data with online repositories such as The Knowledge Network for Biocomplexity, Dryad, and Pangaea. This publicly available database can be used to improve our understanding of how chlorophyll levels respond to global environmental change and provide baseline comparisons for environmental managers responsible for maintaining water quality in lakes.

A Review of the Artificial Neural Network Models for Water Quality Prediction

Water quality prediction plays an important role in environmental monitoring, ecosystem sustainability, and aquaculture. Traditional prediction methods cannot capture the nonlinear and non-stationarity of water quality well. In recent years, the rapid development of artificial neural networks (ANNs) has made them a hotspot in water quality prediction. We have conducted extensive investigation and analysis on ANN-based water quality prediction from three aspects, namely feedforward, recurrent, and hybrid architectures. Based on 151 papers published from 2008 to 2019, 23 types of water quality variables were highlighted. The variables were primarily collected by the sensor, followed by specialist experimental equipment, such as a UV-visible photometer, as there is no mature sensor for measurement at present. Five different output strategies, namely Univariate-Input-Itself-Output, Univariate-Input-Other-Output, Multivariate-Input-Other(multi), Multivariate-Input-Itself-Other-Output, and Multivariate-Input-Itself-Other (multi)-Output, are summarized. From results of the review, it can be concluded that the ANN models are capable of dealing with different modeling problems in rivers, lakes, reservoirs, wastewater treatment plants (WWTPs), groundwater, ponds, and streams. The results of many of the review articles are useful to researchers in prediction and similar fields. Several new architectures presented in the study, such as recurrent and hybrid structures, are able to improve the modeling quality of future development.

Using Machine-Learning Algorithms for Eutrophication Modeling: Case Study of Mar Menor Lagoon (Spain)

The Mar Menor is a hypersaline coastal lagoon with high environmental value and a characteristic example of a highly anthropized hydro-ecosystem located in the southeast of Spain. An unprecedented eutrophication crisis in 2016 and 2019 with abrupt changes in the quality of its waters caused a great social alarm. Understanding and modeling the level of a eutrophication indicator, such as chlorophyll-a (Chl-a), benefits the management of this complex system. In this study, we investigate the potential machine learning (ML) methods to predict the level of Chl-a. Particularly, Multilayer Neural Networks (MLNNs) and Support Vector Regressions (SVRs) are evaluated using as a target dataset information of up to nine different water quality parameters. The most relevant input combinations were extracted using wrapper feature selection methods which simplified the structure of the model, resulting in a more accurate and efficient procedure. Although the performance in the validation phase showed that SVR models obtained better results than MLNNs, experimental results indicated that both ML algorithms provide satisfactory results in the prediction of Chl-a concentration, reaching up to 0.7 R²_CV (cross-validated coefficient of determination) for the best-fit models.

Spectral Feature Selection Optimization for Water Quality Estimation

The spatial heterogeneity and nonlinearity exhibited by bio-optical relationships in turbid inland waters complicate the retrieval of chlorophyll-a (Chl-a) concentration from multispectral satellite images. Most studies achieved satisfactory Chl-a estimation and focused solely on the spectral regions from near-infrared (NIR) to red spectral bands. However, the optical complexity of turbid waters may vary with locations and seasons, which renders the selection of spectral bands challenging. Accordingly, this study proposes an optimization process utilizing available spectral models to achieve optimal Chl-a retrieval. The method begins with the generation of a set of feature candidates, followed by candidate selection and optimization. Each candidate links to a Chl-a estimation model, including two-band, three-band, and normalized different chlorophyll index models. Moreover, a set of selected candidates using available spectral bands implies an optimal composition of estimation models, which results in an optimal Chl-a estimation. Remote sensing images and in situ Chl-a measurements in Lake Kasumigaura, Japan, are analyzed quantitatively and qualitatively to evaluate the proposed method. Results indicate that the model outperforms related Chl-a estimation models. The root-mean-squared errors of the Chl-a concentration obtained by the resulting model (OptiM-3) improve from 11.95 mg·m−3 to 6.37 mg·m−3, and the Pearson’s correlation coefficients between the predicted and in situ Chl-a improve from 0.56 to 0.89.

Water Quality Prediction Method Based on IGRA and LSTM

Water quality prediction has great significance for water environment protection. A water quality prediction method based on the Improved Grey Relational Analysis (IGRA) algorithm and a Long-Short Term Memory (LSTM) neural network is proposed in this paper. Firstly, considering the multivariate correlation of water quality information, IGRA, in terms of similarity and proximity, is proposed to make feature selection for water quality information. Secondly, considering the time sequence of water quality information, the water quality prediction model based on LSTM, whose inputs are the features obtained by IGRA, is established. Finally, the proposed method is applied in two actual water quality datasets: Tai Lake and Victoria Bay. Experimental results demonstrate that the proposed method can take full advantage of the multivariate correlations and time sequence of water quality information to achieve better performance on water quality prediction compared with the single feature or non-sequential prediction methods.

Application of feature selection and regression models for chlorophyll-a prediction in a shallow lake

As a representative index of the algal bloom, the concentration of chlorophyll-a (Chl-a) is a key parameter of concern for environmental managers. The relationships between environmental variables and Chl-a are complex and difficult to establish. Two machine learning methods, including support vector machine for regression (SVR) and random forest (RF), were used in this study to predict Chl-a concentration based on multiple variables. To improve the model accuracy and reduce the input number, two feature selection methods, including minimum redundancy and maximum relevance method (mRMR) and RF, were integrated with regression models. The results showed that the RF model had a higher predictive ability than the SVR model. Furthermore, the less computational time cost and unnecessary prior data transformation also indicated a better applicability of the RF model. The comparison between ensemble models of mRMR-RF and RF-RF showed that the RF-RF yielded a better performance with fewer variables. Seven variables selected from the candidate predictors could interpret most information, and their potential implications to Chl-a were discussed based on the level of importance. Overall, the RF-RF ensemble model can be considered as a useful approach to determine the significant stressors and achieve satisfactory prediction of Chl-a concentration.