A data-mining framework for exploring the multi-relation between fish species and water quality through self-organizing map

Enhancing eco-sensing in aquatic environments: Fish jumping behavior automatic recognition using YOLOv5

Contemporary research on ichthyological behavior predominantly investigates underwater environments. However, the intricate nature of aquatic ecosystems often hampers subaqueous observations of fish behavior due to interference. Transitioning the observational perspective from subaqueous to supra-aquatic enables a more direct assessment of fish physiology and habitat conditions. In this study, we utilized the YOLOv5 convolutional neural network target detection model to develop a fish jumping behavior (FJB) recognition model. A dataset comprising 877 images of fish jumping, captured via a camera in a reservoir, was assembled for model training and validation. After training and validating the model, its recognition accuracy was further tested in real aquatic environments. The results show that YOLOv5 outperforms YOLOv7, YOLOv8, and YOLOv9 in detecting splashes. Post 50 training epochs, YOLOv5 achieved over 97 % precision and recall in the validation set, with an F1 score exceeding 0.9. Furthermore, an enhanced YOLOv5-SN model was devised by integrating specific rules related to ripple size variation and duration, attributable to fish jumping. This modification significantly mitigates noise interference in the detection process. The model's robustness against weather variations ensures reliable detection of fish jumping behavior under diverse meteorological conditions, including rain, cloudiness, and sunshine. Different meteorological elements exert varying effects on fish jumping behavior. The research results can lay the foundation for intelligent perception in aquatic ecology assessment and aquaculture.

A new strategy of using periphyton to simultaneously promote remediation of PAHs-contaminated soil and production of safer crops

Contaminated farmland leads to serious problems for human health through biomagnification in the soil-crop-human chain. In this paper, we have established a new soil remediation strategy using periphyton for the production of safer rice. Four representative polycyclic aromatic hydrocarbons (PAHs), including phenanthrene (Phe), pyrene (Pyr), benzo[b]fluoranthene (BbF), and benzo[a]pyrene (BaP), were chosen to generate artificially contaminated soil. Pot experiments demonstrated that in comparison with rice cultivation in polluted soil with ΣPAHs (50 mg kg-1) but without periphyton, adding periphyton decreased ΣPAHs contents in both rice roots and shoots by 98.98% and 99.76%, respectively, and soil ΣPAHs removal reached 94.19%. Subsequently, risk assessment of ΣPAHs based on toxic equivalent concentration (TEQ), pollution load index (PLI), hazard index (HI), toxic unit for PAHs mixture (TUm), and incremental lifetime cancer risk (ILCR) indicated that periphyton lowered the ecological and carcinogenicity risks of PAHs. Besides, the role of periphyton in enhancing the rice productivity was revealed. The results indicated that periphyton alleviated the oxidative stress of PAHs on rice by reducing malondialdehyde (MDA) content and increasing total antioxidant capacity (T-AOC). Periphyton reduced the toxic stress of PAHs on the soil by promoting soil carbon cycling and metabolic activities as well. Periphyton also improved the soil's physicochemical properties, such as the percentage of soil aggregate, the contents of humic substances (HSs) and nutrients, which increased rice biomass. These findings confirmed that periphyton could improve rice productivity by enhancing soil quality and health. This study provides a new eco-friendly strategy for soil remediation and simultaneously enables the production of safe crops on contaminated land.

Analysis of self-organizing maps and explainable artificial intelligence to identify hydrochemical factors that drive drinking water quality in Haor region

Water contamination undermines human survival and economic growth. Water resource protection and management require knowledge of water hydrochemistry and drinking water quality characteristics, mechanisms, and factors. Self-organizing maps (SOM) have been developed using quantization and topographic error approaches to cluster hydrochemistry datasets. The Piper diagram, saturation index (SI), and cation exchange method were used to determine the driving mechanism of hydrochemistry in both surface and groundwater, while the Gibbs diagram was used for surface water. In addition, redundancy analysis (RDA) and a generalized linear model (GLM) were used to determine the key drinking water quality parameters in the study area. Additionally, the study aimed to utilize Explainable Artificial Intelligence (XAI) techniques to gain insights into the relative importance and impact of different parameters on the entropy water quality index (EWQI). The SOM results showed that thirty neurons generated the hydrochemical properties of water and were organized into four clusters. The Piper diagram showed that the primary hydrochemical facies were HCO3--Ca2+ (cluster 4), Cl---Na+ (all clusters), and mixed (clusters 1 and 4). Results from SI and cation exchange show that demineralization and ion exchange are the driving mechanisms of water hydrochemistry. About 45 % of the studied samples are classified as "medium quality"," that could be suitable as drinking water with further refinement. Cl- may pose increased non-carcinogenic risk to adults, with children at double risk. Cluster 4 water is low-risk, supporting EWQI findings. The RDA and GLM observations agree in that Ca2+, Mg2+, Na+, Cl- and HCO3- all have a positive and significant effect on EWQI, with the exception of K+. TDS, EC, Na+, and Ca2+ have been identified as influencing factors based on bagging-based XAI analysis at global and local levels. The analysis also addressed the importance of SO4, HCO3, Cl, Mg2+, K+, and pH at specific locations.

A flexible framework for regionalization of base flow for river habit maintenance and its thresholds

Information on base flow for river habit maintenance (BF_RH) and its thresholds is necessary for water resource utilization and protection. BF_RH and its thresholds have significant spatial differences; however, it is still unclear how to identify and assess these characteristics. In this study, a technical framework was proposed to clarify the specific procedures and methods for regionalization of BF_RH and its thresholds in large-scale areas. The framework includes four parts: construction of controlling factor system, sub-region division, identification of dominant factors, and determination of the thresholds in sub-regions. The framework was then applied to China to analyzed the regionalized characteristics of BF_RH and its thresholds from a national perspective. The results illustrate the following: (1) the country is divided into nine sub-regions, and the controlling factors and their action paths to BF_RH vary greatly. The elements of climate, vegetation, soil, topography and morphology are satisfactory in explaining the variance of BF_RH and its thresholds, as R² of the partial least squares structural equation modeling is between 0.503 and 0.848. (2) The value of BF_RH/MAF (i.e. the proportion of BF_RH to mean annual natural flow) differs greatly among sub-regions. The mean value is the largest in the Northwest Region, reaching 20 %, while it is only 1.7 % in the Northeast Cold Region. (3) The dynamic and static thresholds are obtained by using the precipitation and other indices as the explanatory variables in the sub-regions. In general, the more abundant the water resources, the higher may be the threshold. Moreover, attention should be paid to the positive and negative effects of vegetation restoration on this threshold. The case study proves that the framework can guide the determination of BF_RH, especially for ungagged rivers. Importantly, the framework is flexible and highly adaptable in different regions.

Understanding the patterns and processes underlying water quality and pollution risk in West-Africa River using self-organizing maps and multivariate analyses

Rivers are dynamic systems in complex interactions with their surrounding environments. Reliable and fast interpretation of water quality is therefore needed for sustainable river management. Unfortunately, water quality and environmental status interactions have not yet been documented sufficiently in West-Africa. This study explored the spatial-latitudinal and seasonal features of water quality along the Sô River Basin (SRB, West Africa) using self-organizing map (SOM) and principal component analysis. Twenty-two water quality variables were measured in the surface layer at 12 different sampling sites during a twenty-four-month period from July 2016 to June 2018. The results revealed three water quality groups, following an upstream-downstream pollution gradient: (1) upstream and middle reach sites with high dissolved oxygen and Secchi disk depth values, which are more suitable for the aquatic biota; (2) downstream sites with high concentrations of ammonium, biochemical oxygen demand, and heavy metals especially in flood period, reflecting both high organic and heavy metal pollution; and (3) brackish downstream sites characterized by less heavy metal and organic pollutions. No significant variation was observed between seasons. However, the SRB relatively suffered from higher risks of heavy metal contamination and organic pollution in wet seasons. Although hydroclimatic processes affect the water quality, anthropogenic inputs of point and non-point sources were identified and discussed as a more prominent factor contributing to variation in the water quality condition. These results offer insights into the water quality dynamics in river-estuary system as well as potential pollution sources, crucial for defining sanitation, and management measures.

Insight into structural composition of dissolved organic matter in saline-alkali soil by fluorescence spectroscopy coupled with self-organizing map and structural equation modeling

Soil salinization has been occurring all over the world, which severely affected crop production and threatened the life of mankind. It is necessary to take serious steps to improve soil fertility for the sustainability and productive capacity of agriculture. Soil samples of different depths were collected from native vegetation communities (Comm. Phragmites communis (CPC) and Comm. Populus alba (CPA)) and irrigated crops (corn fields (CFD) and seed melon fields (SMF)) in Hetao irrigation area of China. Three dimensional excitation-emission matrix (EEM) fluorescence technology combined with self-organizing map were used to analyze the dissolved organic matter (DOM) composition and structural characteristics in saline-alkali soils and its spatial distribution under different vegetation covers. Critical factors were recognized by classification and regression tree (CART) for distinguishing soil samples, and latent factors were revealed with structural equation modeling (SEM) for improving the humification degree of DOM from saline soils in Hetao irrigation area. Five components were obtained in the DOM substances, i.e., tyrosine-like (C1), tryptophan-like (C2), UV fulvic-like (C3), visible fulvic-like (C4) and humic-like (C5). The protein-like peaks were all obvious, and the fulvic-like peaks (600-735 a.u.) were conspicuous in the CPC soil than in others, except CFD1 and SMF1. C1 was the critical factor to distinguish native vegetation from irrigated crops, and C1 and C2 were the critical factors to distinguish CFD from SMF. Contrary to the HA/FA (0.20) and A/C (0.25), the path coefficient (-0.15) of sources with T/H was negative, indicating that the incremental contents of fluorenscense substances were in the sequences of protein-like > visible fulvic-like > UV fulvic-like > humic-like, affecting by the allochthonous. C1 (1.00) and C4 (1.00) were the primary components for improving the humification degree of DOM, which were principally originated from plant debris. EEM combined with self-organizing map, CART and SEM is an efficient way to distinguish different salinized soils and reveal the latent factors for improving the soil fertility.

Revealing Population Connectivity of the Estuarine Tapertail Anchovy Coilia nasus in the Changjiang River Estuary and Its Adjacent Waters Using Otolith Microchemistry

The estuarine tapertail anchovy, Coilia nasus, is a migratory fish with high economic value in China. We collected fish from the Changjiang River (the Yangtze River) estuary, the Qiantang River estuary, and the southern Yellow Sea, and studied their relationships using otolith elemental and stable isotopic microchemistry signatures to assess the population connectivity of C. nasus. Results show that, in addition to Ca, other elements were present in the otolith core. The δ18O, Na/Ca, Fe/Ca, and Cu/Ca values of the Qiantang population were significantly higher than those of the others, whereas its δ13C and Ba/Ca values were found to be significantly lower. Otolith multi-element composition and stable isotope ratios differed significantly between the Qiantang and Changjiang estuary groups (p < 0.05); however, no difference was observed between the latter and the Yellow Sea group. Cluster analysis, linear discriminant analysis, and a self-organizing map strongly suggest possible connectivity between the fish populations of the Changjiang estuary and Yellow Sea, while the population of the Qiantang River estuary appears to be independent. Notably, results suggest a much closer connectivity between the fish populations of the Changjiang River and the Yellow Sea.

Potential for Artificial Intelligence (AI) and Machine Learning (ML) Applications in Biodiversity Conservation, Managing Forests, and Related Services in India

The recent advancement in data science coupled with the revolution in digital and satellite technology has improved the potential for artificial intelligence (AI) applications in the forestry and wildlife sectors. India shares 7% of global forest cover and is the 8th most biodiverse region in the world. However, rapid expansion of developmental projects, agriculture, and urban areas threaten the country’s rich biodiversity. Therefore, the adoption of new technologies like AI in Indian forests and biodiversity sectors can help in effective monitoring, management, and conservation of biodiversity and forest resources. We conducted a systematic search of literature related to the application of artificial intelligence (AI) and machine learning algorithms (ML) in the forestry sector and biodiversity conservation across globe and in India (using ISI Web of Science and Google Scholar). Additionally, we also collected data on AI-based startups and non-profits in forest and wildlife sectors to understand the growth and adoption of AI technology in biodiversity conservation, forest management, and related services. Here, we first provide a global overview of AI research and application in forestry and biodiversity conservation. Next, we discuss adoption challenges of AI technologies in the Indian forestry and biodiversity sectors. Overall, we find that adoption of AI technology in Indian forestry and biodiversity sectors has been slow compared to developed, and to other developing countries. However, improving access to big data related to forest and biodiversity, cloud computing, and digital and satellite technology can help improve adoption of AI technology in India. We hope that this synthesis will motivate forest officials, scientists, and conservationists in India to explore AI technology for biodiversity conservation and forest management.

The Construction and Effect Analysis of Nursing Safety Quality Management Based on Data Mining

Data mining belongs to knowledge discovery, which is the process of revealing hidden, unknown, and valuable information from a large amount of fuzzy application data. The potential information revealed by data mining can help decision-makers adjust market strategies and reduce market risks. The information mined can be the discovery of a particular study and little known, which must be based on the principle of truth. Nursing safety means that during nursing work, the nursing staff must strictly follow the nursing system and operating procedures, accurately execute doctor's orders, implement nursing plans, and ensure that patients get physical and mental safety during treatment and recovery. This paper aims to explore the construction of nursing safety quality management system and its effect analysis based on data mining. It is hoped that improvements in hospital nursing processes will provide better nursing services for patients using data mining techniques. This paper uses the FP algorithm to mine the data set and generates frequent itemsets, proposes and implements the association rule mining algorithm, and obtains the association rules with practical reference value. This article analyzes the current status and existing problems of nursing management, and puts forward some problems existing in the current nursing management staff's own quality, nursing quality system standards, and nursing management system. The experimental results in this article show that there are 42 cases of poor nursing due to lack of basic medical knowledge, accounting for 52%; there are 12 cases of poor nursing due to their own diseases, accounting for 15%; there were 7 cases of poor nursing due to lack of communication, accounting for 9%; there were 15 cases of poor nursing caused by unreasonable use of restraint devices, accounting for 19%. From these data, it can be seen that patients need to have basic medical knowledge and act in strict accordance with doctors' orders. Family members also need to accompany the patients more and cooperate with all parties in order to maximize the effectiveness of care.

Integration and Development of Enterprise Internal Audit and Big Data Based on Data Mining Technology

Auditing based on big data is the trend in the future audit development. First, the technical environment provides a technical support platform for continuous auditing. Through the development of information technology to promote the merger between financial services, the company's business operations have been digitized, and the original paper audit is also facing changes. This article aims to study the integration and development of enterprise internal audit and big data based on data mining technology. To this end, this article proposes a big data audit system, improves and optimizes the clustering algorithm (key algorithm) of data mining, and designs experiments and analysis to explore its related effects and improved performance, so that it can be more suitable for the research topic. The experimental results of this article show that the improved big data audit system improves the resource perfection of internal audit by 17.4%. The improved algorithm's accuracy rate has increased by 31.4%, and the best clustering ability has also been improved by 20.7%, which can be well applied to the company's internal audit.

Variable-weighted FDA combined with t-SNE and multiple extreme learning machines for visual industrial process monitoring

The visualization of an operating state of industrial processes allows operators to identify and diagnose faults intuitively and quickly. The identification and diagnosis of faults are important for ensuring industrial production safety. A method that combines variable-weighted Fisher discriminant analysis (VWFDA), t-distributed stochastic neighbor embedding (t-SNE), and multiple extreme learning machines (ELMs) is proposed for visual process monitoring. First, the VWFDA weighs variables on the basis of their contribution to the fault, thereby amplifying the fault information. The VWFDA is used to extract feature vectors from industrial data, and normal state and various fault states can be separated from each other in the space formed by these feature vectors. Second, t-SNE is used to visualize these feature vectors. Third, given that t-SNE lacks a transformation matrix during dimension reduction, one ELM is used for each class data of t-SNE to obtain the mapping relation from its input data to its mapping points. Finally, the VWFDA and multiple trained ELMs are combined for online process monitoring. The performance of the proposed approach is compared with that of FDA-t-SNE and other methods on the basis of the Tennessee Eastman process, thereby confirming that the proposed approach is advantageous for visual industrial process monitoring.

Multivariate Analysis Applied to Aquifer Hydrogeochemical Evaluation: A Case Study in the Coastal Significant Subterranean Water Body between “Cecina River and San Vincenzo”, Tuscany (Italy)

The hydrogeochemical characteristics of the significant subterranean water body between “Cecina River and San Vincenzo” (Italy) was evaluated using multivariate statistical analysis methods, like principal component analysis and self-organizing maps (SOMs), with the objective to study the spatiotemporal relationships of the aquifer. The dataset used consisted of the chemical composition of groundwater samples collected between 2010 and 2018 at 16 wells distributed across the whole aquifer. For these wells, all major ions were determined. A self-organizing map of 4 × 8 was constructed to evaluate spatiotemporal changes in the water body. After SOM clustering, we obtained three clusters that successfully grouped all data with similar chemical characteristics. These clusters can be viewed to reflect the presence of three water types: (i) Cluster 1: low salinity/mixed waters; (ii) Cluster 2: high salinity waters; and (iii) Cluster 3: low salinity/fresh waters. Results showed that the major ions had the greater influence over the groundwater chemistry, and the difference in their concentrations allowed the definition of three clusters among the obtained SOM. Temporal changes in cluster assignment were only observed in two wells, located in areas more susceptible to changes in the water table levels, and therefore, hydrodynamic conditions. The result of the SOM clustering was also displayed using the classical hydrochemical approach of the Piper plot. It was observed that these changes were not as easily identified when the raw data were used. The spatial display of the clustering results, allowed the evaluation in a hydrogeological context in a quick and cost-effective way. Thus, our approach can be used to quickly analyze large datasets, suggest recharge areas, and recognize spatiotemporal patterns.

Comparison of multimodal findings on epileptogenic side in temporal lobe epilepsy using self-organizing maps

OBJECTIVE

To develop a decision-making tool to evaluate and compare the performance of neuroimaging markers with clinical findings and the significance of attributes for presurgical lateralization of mesial temporal lobe epilepsy (mTLE).

METHODS

Thirty-five unilateral mTLE patients who qualified as candidates for surgical resection were studied. Seizure semiology, ictal EEG, ictal epileptogenic zone, interictal-irritative zone, and MRI findings were used as clinical markers. Hippocampal T1 volumetry and FLAIR intensity, DTI estimated; mean diffusivity (MD) in the hippocampus and fractional anisotropy (FA) in posteroinferior cingulum and crus of fornix, and the output of logistic regression method on volumetrics of the hippocampus, amygdala, and thalamus were adopted as neuroimaging markers. The self-organizing map (SOM) method was applied to markers to provide predictive methods for mTLE lateralization.

RESULTS

The SOM clustered all clinical attributes correctly with 100% accuracy and sensitivity for both the left and right mTLE. Among the clinical markers, seizure semiology and interictal-irrelative zone are the most sensitive attribute for the left-mTLE group lateralization. The accuracy achieved by applying the SOM method to the neuroimaging attributes was 94%, while the sensitivity was achieved 90% for left and 100% for right mTLE. SOM evidence indicated that the hippocampal volume is the most sensitive attribute for the prediction of the laterality in left-mTLE groups.

CONCLUSION

The proposed SOM method showed that neuroimaging markers may not replace with clinical findings. Nevertheless, multimodal neuroimaging can play an effective role in preoperative lateralization to reduce the costs and risks of surgical resection.

Using machine learning to understand the implications of meteorological conditions for fish kills

Fish kills, often caused by low levels of dissolved oxygen (DO), involve with complex interactions and dynamics in the environment. In many places the precise cause of massive fish kills remains uncertain due to a lack of continuous water quality monitoring. In this study, we tested if meteorological conditions could act as a proxy for low levels of DO by relating readily available meteorological data to fish kills of grey mullet (Mugil cephalus) using a machine learning technique, the self-organizing map (SOM). Driven by different meteorological patterns, fish kills were classified into summer and non-summer types by the SOM. Summer fish kills were associated with extended periods of lower air pressure and higher temperature, and concentrated storm events 2–3 days before the fish kills. In contrast, non-summer fish kills followed a combination of relatively low air pressure, continuous lower wind speed, and successive storm events 5 days before the fish kills. Our findings suggest that abnormal meteorological conditions can serve as warning signals for managers to avoid fish kills by taking preventative actions. While not replacing water monitoring programs, meteorological data can support fishery management to safeguard the health of the riverine ecosystems.

Chemometrics for environmental monitoring: a review

Environmental monitoring is necessary to ensure the overall health and conservation of an ecosystem. However, ecosystems (e.g. air, water, soil), are complex, involving numerous processes (both native and external), inputs, contaminants, and living organisms. As such, monitoring an environmental system is not a trivial task. The data obtained from natural systems is often multifaceted and convoluted, as a multitude of inputs can be intertwined within the matrix of the information obtained as part of a study. This means that trends and important results can be easily overlooked by conventional and single dimensional data analysis protocols. Recently, chemometric methods have emerged as a powerful method for maximizing the details contained within a chemical data set. Specifically, chemometrics refers to the use of mathematical and statistical analysis methods to evaluate chemical data, beyond univariant analysis. This type of analysis can provide a quantitative description of environmental measurements, while also having the capacity to reveal previously overlooked trends in data sets. Applying chemometrics to environmental data allows us to identify and describe the inter-relationship of environmental drivers, sources of contamination, and their potential impact upon the environment. This review aims to provide a detailed understanding of chemometric techniques, how they are currently used in environmental monitoring, and how these techniques can be used to improve current practices. An enhanced ability to monitor environmental conditions and to predict trends would be greatly beneficial to government and research agencies in their ability to develop environmental policies and analytical procedures.

Fish community responses to antecedent hydrological conditions based on long-term data in Mediterranean river basins (Iberian Peninsula)

In recent decades many studies have proven the paramount impact of flow regimes on the structure of lotic ecosystems, both through extreme events (i.e. floods and droughts) but also during intermediate flows, which temporarily and spatially regulate the habitat availability. Human demand for water is steadily increasing and scientists are challenged to define ecosystem needs clearly enough to guide policies and management strategies. However, field studies demonstrated that a variety of interacting factors, such as, presence of barriers (e.g. dams) and temporal changes in habitat structure affect the abundance, composition and distribution of fish assemblages. This work based on quantile regression tested hypotheses to elucidate the effect of antecedent hydrological conditions on fish communities. A large monitoring database collecting and homogenizing the existing information on fish fauna in the Júcar River Basin District (Eastern Iberian Peninsula) was gathered and used to evaluate biological metrics (species richness, Capture Per Unit Effort-CPUE, and CPUE ratio over the total CPUE) related to life history strategies (i.e. periodic, opportunistic or equilibrium) and species origin (i.e. native, translocated or alien). The resulting dataset was complemented with diverse indicators of the measured daily discharge at the nearest gauging site. Most of the significant relationships confirmed the role of antecedent hydrological conditions as limiting factors, although other environmental factors likely play additional roles. In general, richness and abundance of alien species showed the higher proportion of significant associations, particularly spring flows and annual minima and maxima. These flow-ecology relationships shall be particularly useful to manage ecological responses to hydrological alteration. They also provide with clear ecological foundations for developing environmental flows assessments in Mediterranean river basins worldwide, using holistic approaches which can harmonise eco-hydrological approaches with smaller-scale and habitat-based ecohydraulics methods, especially under the current climate trends.

Explore the relationship between fish community and environmental factors by machine learning techniques

In the face of multiple habitat alterations originating from both natural and anthropogenic factors, the fast-changing environments pose significant challenges for maintaining ecosystem integrity. Machine learning is a powerful tool for modeling complex non-linear systems through exploratory data analysis. This study aims at exploring a machine learning-based approach to relate environmental factors with fish community for achieving sustainable riverine ecosystem management. A large number of datasets upon a wide variety of eco-environmental variables including river flow, water quality, and species composition were collected at various monitoring stations along the Xindian River of Taiwan during 2005 and 2012. Then the complicated relationship and scientific essences of these heterogonous datasets are extracted using machine learning techniques to have a more holistic consideration in searching a guiding reference useful for maintaining river-ecosystem integrity. We evaluate and select critical environmental variables by the analysis of variance (ANOVA) and the Gamma test (GT), and then we apply the adaptive network-based fuzzy inference system (ANFIS) for an estimation of fish bio-diversity using the Shannon Index (SI). The results show that the correlation between model estimation and the biodiversity index is higher than 0.75. The GT results demonstrate that biochemical oxygen demand (BOD), water temperature, total phosphorus (TP), and nitrate-nitrogen (NO₃-N) are important variables for biodiversity modeling. The ANFIS results further indicate lower BOD, higher TP, and larger habitat (flow regimes) would generally provide a more suitable environment for the survival of fish species. The proposed methodology not only possesses a robust estimation capacity but also can explore the impacts of environmental variables on fish biodiversity. This study also demonstrates that machine learning is a promising avenue toward sustainable environmental management in river-ecosystem integrity.

Self-organizing maps of typhoon tracks allow for flood forecasts up to two days in advance

Typhoons are among the greatest natural hazards along East Asian coasts. Typhoon-related precipitation can produce flooding that is often only predictable a few hours in advance. Here, we present a machine-learning method comparing projected typhoon tracks with past trajectories, then using the information to predict flood hydrographs for a watershed on Taiwan. The hydrographs provide early warning of possible flooding prior to typhoon landfall, and then real-time updates of expected flooding along the typhoon's path. The method associates different types of typhoon tracks with landscape topography and runoff data to estimate the water inflow into a reservoir, allowing prediction of flood hydrographs up to two days in advance with continual updates. Modelling involves identifying typhoon track vectors, clustering vectors using a self-organizing map, extracting flow characteristic curves, and predicting flood hydrographs. This machine learning approach can significantly improve existing flood warning systems and provide early warnings to reservoir management.

Development of a Heterogeneity Analysis Framework for Collaborative Sponge City Management

Rapid urbanization, inappropriate urban planning and the changing climate in many countries have resulted in flooding, water shortage and water pollution around the world. Although the sponge city concept has been applied in both macro-scales and micro-scales to address those challenges, research on the heterogeneity of different cities for sponge city construction and the collaborative management between cities is insufficient. Therefore, this paper proposes a multivariate cluster analysis framework and conducts an empirical study using 96 Chinese cities. By considering the local infrastructure, economic development, water resource distribution, water quality and precipitation characteristics in each city, and integrating the principal component analysis and a self-organizing feature mapping network, this paper shows the potential of regional and interregional sponge city collaborative management. This will provide an opportunity for developing a new sponge city management mechanism and will promote the establishment of multi-functional departments for urban flood control and water quality improvement.

Estimation of Hydrologic Alteration in Kaligandaki River Using Representative Hydrologic Indices

Anthropogenic activities have led to the transformation of river basins and natural flow alteration around the world. Alteration in flow regimes have adverse effects on river ecosystems. Flow value changes signify the alteration extent and a number of flow related indices can be used to assess the extent of alteration in a river ecosystem. Selection of a few and ecologically relevant indices from a large set of available indices is a daunting task. Principal Component Analysis helps to reduce these large indices to a few ecologically significant indices and removes statistical redundancy of data to give uncorrelated data sets. These representative indices are useful in the primary investigation of a less studied area like the Kaligandaki River basin, Nepal. This paper uses reduced indices from the Kaligandaki River to calculate the alteration on the river section downstream of a hydropower facility using the Histogram Comparison Approach (HCA) combined with Hydrologic Year Types (HYT). The combined approach eliminates the potential underestimation of alteration values which may occur due to the exemption of hydrologic year types from the analysis, a feature equally relevant in river ecology. A new metric is used for the calculation of combined alteration using HCA-HYT in this paper. The analysis showed 60.71 percent alteration in the natural flow regime in the area past a hydropower construction, which is classified in the high alteration category. The study can be a guide for further analysis of the ecological flow management of a river section and a parsimonious approach to other areas where hydrological data is limited to historical flow records only.

Building ANN-Based Regional Multi-Step-Ahead Flood Inundation Forecast Models

A regional inundation early warning system is crucial to alleviating flood risks and reducing loss of life and property. This study aims to provide real-time multi-step-ahead forecasting of flood inundation maps during storm events for flood early warnings in inundation-prone regions. For decades, the Kemaman River Basin, located on the east coast of the West Malaysia Peninsular, has suffered from monsoon floods that have caused serious damage. The downstream region with an area of approximately 100 km2 located on the east side of this basin is selected as the study area. We explore and implement a hybrid ANN-based regional flood inundation forecast system in the study area. The system combines two popular artificial neural networks—the self-organizing map (SOM) and the recurrent nonlinear autoregressive with exogenous inputs (RNARX)—to sequentially produce regional flood inundation maps during storm events. The results show that: (1) the 4 × 4 SOM network can effectively cluster regional inundation depths; (2) RNARX networks can accurately forecast the long-term (3–12 h) regional average inundation depths; and (3) the hybrid models can produce adequate real-time regional flood inundation maps. The proposed ANN-based model was shown to very quickly carry out multi-step-ahead forecasting of area-wide inundation depths with sufficient lead time (up to 12 h) and can visualize the forecasted results on Google Earth using user devices to help decision makers and residents take precautionary measures against flooding.

Determining the macroinvertebrate community indicators and relevant environmental predictors of the Hun-Tai River Basin (Northeast China): A study based on community patterning

It is essential to understand the patterning of biota and environmental influencing factors for proper rehabilitation and management at the river basin scale. The Hun-Tai River Basin was extensively sampled four times for macroinvertebrate community and environmental variables during one year. Self-Organizing Maps (SOMs) were used to reveal the aggregation patterns of the 355 samples. Three community types (i.e., clusters) were found (at the family level) based on the community composition, which showed a clearly gradient by combining them with the representative environmental variables: minimally impacted source area, intermediately anthropogenic impacted sites, and highly anthropogenic impacted downstream area, respectively. This gradient was corroborated by the decreasing trends in density and diversity of macroinvertebrates. Distance from source, total phosphorus and water temperature were identified as the most important variables that distinguished the delineated communities. In addition, the sampling season, substrate type, pH and the percentage of grassland were also identified as relevant variables. These results demonstrated that macroinvertebrates communities are structured in a hierarchical manner where geographic and water quality prevail over temporal (season) and habitat (substrate type) features at the basin scale. In addition, it implied that the local-scale environment variables affected macroinvertebrates under the longitudinal gradient of the geographical and anthropogenic pressure. More than one family was identified as the indicator for each type of community. Abundance contributed significantly for distinguishing the indicators, while Baetidae with higher density indicated minimally and intermediately impacted area and lower density indicated highly impacted area. Therefore, we suggested the use of abundance data in community patterning and classification, especially in the identification of the indicator taxa.

Using self-organizing map for coastal water quality classification: Towards a better understanding of patterns and processes

Self-organizing map (SOM) was used to explore the spatial characteristics of water quality in the middle and southern Fujian coastal area. Nineteen water quality variables (temperature, salinity, pH, dissolved oxygen, alkalinity, chemical oxygen demand, nutrients NH₄-N, H₂SiO₃, PO₄^-, NO₂^-, and NO₃^-, heavy metals/metalloid Cu, Zn, As, Cd, Pb, Hg, and Cr⁶⁺, and oil) were measured in the surface, middle, and bottom water layers at 94 different sampling sites. Patterns of water quality variables were visualized by the SOM planes, and similar patterns were observed for those variables that correlated with each other, indicating a common source. pH, COD, As, Hg, Pb, and Cr⁶⁺ likely originated from industries, while nutrients NH₄-N, NO₂^-, NO₃^-, and PO₄^3- were mainly attributed to agriculture and aquaculture. The k-means clustering in the SOM grouped the water quality data into nine clusters, which revealed three representative water types, ranging from low salinity to high salinity with different levels of heavy metal/metalloid pollution and nutrient pollution. Spatial changes in water quality reflected the impacts of natural factors (riverine outflows, tides, and alongshore currents), as well as anthropogenic activities (mariculture, industrial and urban discharges, and agricultural effluents). Principal component analysis (PCA) confirmed the clustering results obtained by SOM, while the latter provides a more detailed classification and additional information about the dominant variables governing the classification processes. The results of this study suggest that SOM is an effective tool for a better understanding of patterns and processes driving water quality.

Advanced data mining approaches in the assessment of urinary concentrations of bisphenols, chlorophenols, parabens and benzophenones in Brazilian children and their association to DNA damage

Human exposure to endocrine disrupting chemicals (EDCs) has received considerable attention over the last three decades. However, little is known about the influence of co-exposure to multiple EDCs on effect-biomarkers such as oxidative stress in Brazilian children. In this study, concentrations of 40 EDCs were determined in urine samples collected from 300 Brazilian children of ages 6-14 years and data were analyzed by advanced data mining techniques. Oxidative DNA damage was evaluated from the urinary concentrations of 8-hydroxy-2'-deoxyguanosine (8OHDG). Fourteen EDCs, including bisphenol A (BPA), methyl paraben (MeP), ethyl paraben (EtP), propyl paraben (PrP), 3,4-dihydroxy benzoic acid (3,4-DHB), methyl-protocatechuic acid (OH-MeP), ethyl-protocatechuic acid (OH-EtP), triclosan (TCS), triclocarban (TCC), 2-hydroxy-4-methoxybenzophenone (BP3), 2,4-dihydroxybenzophenone (BP1), bisphenol A bis(2,3-dihydroxypropyl) glycidyl ether (BADGE·2H₂O), 2,4-dichlorophenol (2,4-DCP), and 2,5-dichlorophenol (2,5-DCP) were found in >50% of the urine samples analyzed. The highest geometric mean concentrations were found for MeP (43.1 ng/mL), PrP (3.12 ng/mL), 3,4-DHB (42.2 ng/mL), TCS (8.26 ng/mL), BP3 (3.71 ng/mL), and BP1 (4.85 ng/mL), and exposures to most of which were associated with personal care product (PCP) use. Statistically significant associations were found between urinary concentrations of 8OHDG and BPA, MeP, 3,4-DHB, OH-MeP, OH-EtP, TCS, BP3, 2,4-DCP, and 2,5-DCP. After clustering the data on the basis of i) 14 EDCs (exposure levels), ii) demography (age, gender and geographic location), and iii) 8OHDG (effect), two distinct clusters of samples were identified. 8OHDG concentration was the most critical parameter that differentiated the two clusters, followed by OH-EtP. When 8OHDG was removed from the dataset, predictability of exposure variables increased in the order of: OH-EtP > OH-MeP > 3,4-DHB > BPA > 2,4-DCP > MeP > TCS > EtP > BP1 > 2,5-DCP. Our results showed that co-exposure to OH-EtP, OH-MeP, 3,4-DHB, BPA, 2,4-DCP, MeP, TCS, EtP, BP1, and 2,5-DCP was associated with DNA damage in children. This is the first study to report exposure of Brazilian children to a wide range of EDCs and the data mining approach further strengthened our findings of chemical co-exposures and biomarkers of effect.

Assessment of the Eutrophication-Related Environmental Parameters in Two Mediterranean Lakes by Integrating Statistical Techniques and Self-Organizing Maps

During the last decades, Mediterranean freshwater ecosystems, especially lakes, have been under severe pressure due to increasing eutrophication and water quality deterioration. In this article, we compared the effectiveness of different data analysis methods by assessing the contribution of environmental parameters to eutrophication processes. For this purpose, principal components analysis (PCA), cluster analysis, and a self-organizing map (SOM) were applied, using water quality data from two transboundary lakes of North Greece. SOM is considered as an advanced and powerful data analysis tool because of its ability to represent complex and nonlinear relationships among multivariate data sets. The results of PCA and cluster analysis agreed with the SOM results, although the latter provided more information because of the visualization abilities regarding the parameters' relationships. Besides nutrients that were found to be a key factor for controlling chlorophyll-a (Chl-a), water temperature was related positively with algal production, while the Secchi disk depth parameter was found to be highly important and negatively related toeutrophic conditions. In general, the SOM results were more specific and allowed direct associations between the water quality variables. Our work showed that SOMs can be used effectively in limnological studies to produce robust and interpretable results, aiding scientists and managers to cope with environmental problems such as eutrophication.

A linear and non-linear polynomial neural network modeling of dissolved oxygen content in surface water: Inter- and extrapolation performance with inputs' significance analysis

Accurate prediction of water quality parameters (WQPs) is an important task in the management of water resources. Artificial neural networks (ANNs) are frequently applied for dissolved oxygen (DO) prediction, but often only their interpolation performance is checked. The aims of this research, beside interpolation, were the determination of extrapolation performance of ANN model, which was developed for the prediction of DO content in the Danube River, and the assessment of relationship between the significance of inputs and prediction error in the presence of values which were of out of the range of training. The applied ANN is a polynomial neural network (PNN) which performs embedded selection of most important inputs during learning, and provides a model in the form of linear and non-linear polynomial functions, which can then be used for a detailed analysis of the significance of inputs. Available dataset that contained 1912 monitoring records for 17 water quality parameters was split into a "regular" subset that contains normally distributed and low variability data, and an "extreme" subset that contains monitoring records with outlier values. The results revealed that the non-linear PNN model has good interpolation performance (R²=0.82), but it was not robust in extrapolation (R²=0.63). The analysis of extrapolation results has shown that the prediction errors are correlated with the significance of inputs. Namely, the out-of-training range values of the inputs with low importance do not affect significantly the PNN model performance, but their influence can be biased by the presence of multi-outlier monitoring records. Subsequently, linear PNN models were successfully applied to study the effect of water quality parameters on DO content. It was observed that DO level is mostly affected by temperature, pH, biological oxygen demand (BOD) and phosphorus concentration, while in extreme conditions the importance of alkalinity and bicarbonates rises over pH and BOD.

Conservation of groundwater from over-exploitation-Scientific analyses for groundwater resources management

Groundwater over-exploitation has produced many critical problems in the southern Taiwan. The accumulated stresses and demands make groundwater management a complex issue that needs innovative scientific analyses for deriving better water management strategies. In this study, we aimed to provide scientific analyses of the groundwater systems in the Pingtung Plain through soft-computing techniques to explore its spatial-temporal and hydro-geological characteristics for the elaboration of future groundwater management plans and in decision-making process. We conducted a study to assess the essential features of the groundwater systems based on the long-term large datasets of regional groundwater levels by using the principal component analysis (PCA), and the self-organizing map (SOM) with regression analysis. The PCA results demonstrated that two leading components could well present the spatial characteristics of the groundwater systems and classify the region into eastern, western and transition zones. The SOM results could visibly explore the behavior of regional groundwater variations in various aquifers and the multi-relations among climate and hydrogeological variables. Results revealed that the potential of groundwater recharge made by precipitation or river flow was higher in the eastern zone than in the western zone. Analysis results further showed an increase of the groundwater levels in the western zone after year 2006, while there were no obvious increases of the groundwater levels in the eastern or transition zones. Based on the investigated characteristics, we suggest that a sound groundwater management plan should consider zonal difference of the groundwater systems to achieve groundwater conservation.

Characterization and source apportionment of PAHs from a highly urbanized river sediments based on land use analysis

The city-scale land use/land cover change derived by urbanization on the fates of PAHs is of great concerns recently. This study evaluated spatiotemporal variations and sources of PAHs from a highly urbanized river sediments in the Huangpu River, Shanghai. Results indicated that the concentrations of PAHs in the sediments varied greatly across locations and seasons. The concentration of Σ₁₆PAHs in the dry season were 6 times higher than that in wet season. The mainstream and midstream of the Huangpu River were identified as the hotspots in both dry and wet seasons. However, 4-ring PAH compounds were dominated, contributing 42.41% ± 6.81% and 44.70 ± 7.73% in the dry and wet seasons, respectively. Multivariate statistical and land use analysis suggested that the main sources of PAHs derived from the cultivation, traffic and commercial activities. Buffer radii (<750 m) area with cultivated land, road/street and transportation and commercial and business facilities contributed significantly the PAHs in the sediment of the Huangpu River. Population density was also an important variable regulating the PAHs concentrations less than 750 m in the wet season. Risk assessment results revealed that the PAHs toxicity in the sediments was higher in dry season than in wet season. Overall, severe land use changes caused by rapid urbanization can contribute more amount of PAHs emission and complicated sources of PAHs, thus provide insights into the importance of land use types in indicating PAHs source.