Determination of conditioning factors for mapping nickel contamination susceptibility in groundwater in Kanchanaburi Province, Thailand, using random forest and maximum entropy.

Groundwater pollution from nickel (Ni) has been a severe concern in Kanchanaburi Province, Thailand. Recent assessments revealed that the Ni concentration in groundwater, particularly in urban areas, often exceeded the permissible limit. The challenge for groundwater agencies is therefore to delineate regions with high susceptibility to Ni contamination. In this study, a novel modeling approach was applied to a dataset of 117 groundwater samples collected from Kanchanaburi Province between April and July 2021. Twenty site-specific initial variables were considered as influencing factors to Ni contamination. The Random Forest (RF) algorithm with Recursive Feature Elimination (RFE) function was used to select the fourteen most influencing variables. These variables were then used as input features to train a ME model to delineate the Ni contamination susceptibility at a high confidence (Area Under the Curve (AUC) validation value of 0.845). Ten input variables of the altitude, geology, land use, slope, soil type, distance to industrial areas, distance to mining areas, electric conductivity, oxidation-reduction potential, and groundwater depth were discovered in the most explaining the variation of spatial Ni contamination at very high (95.47 km2) and high (86.65 km2) susceptibility. This study devises the novel machine learning approach to identify the conditioning factors and map Ni contamination susceptibility in the groundwater, which provides a baseline dataset and reliable methods for the development of a sustainable groundwater management strategy.

Estimation of nitrogen and phosphorus concentrations from water quality surrogates using machine learning in the Tri An Reservoir, Vietnam.

Surface water eutrophication due to excessive nutrients has become a major environmental problem around the world in the past few decades. Among these nutrients, nitrogen and phosphorus are two of the most important harmful cyanobacterial bloom (HCB) drivers. A reliable prediction of these parameters, therefore, is necessary for the management of rivers, lakes, and reservoirs. The aim of this study is to test the suitability of the powerful machine learning (ML) algorithm, random forest (RF), to provide information on water quality parameters for the Tri An Reservoir (TAR). Three species of nitrogen and phosphorus, including nitrite (N-NO2-), nitrate (N-NO3-), and phosphate (P-PO43-), were empirically estimated using the field observation dataset (2009-2014) of six surrogates of total suspended solids (TSS), total dissolved solids (TDS), turbidity, electrical conductivity (EC), chemical oxygen demand (COD), and biochemical oxygen demand (BOD5). Field data measurement showed that water quality in the TAR was eutrophic with an up-trend of N-NO3- and P-PO43- during the study period. The RF regression model was reliable for N-NO2-, N-NO3-, and P-PO43- prediction with a high R2 of 0.812-0.844 for the training phase (2009-2012) and 0.888-0.903 for the validation phase (2013-2014). The results of land use and land cover change (LUCC) revealed that deforestation and shifting agriculture in the upper region of the basin were the major factors increasing nutrient loading in the TAR. Among the meteorological parameters, rainfall pattern was found to be one of the most influential factors in eutrophication, followed by average sunshine hour. Our results are expected to provide an advanced assessment tool for predicting nutrient loading and for giving an early warning of HCB in the TAR.

A Review of Remote Sensing Approaches for Monitoring Blue Carbon Ecosystems: Mangroves, Seagrassesand Salt Marshes during 2010⁻2018.

Blue carbon (BC) ecosystems are an important coastal resource, as they provide a range of goods and services to the environment. They play a vital role in the global carbon cycle by reducing greenhouse gas emissions and mitigating the impacts of climate change. However, there has been a large reduction in the global BC ecosystems due to their conversion to agriculture and aquaculture, overexploitation, and removal for human settlements. Effectively monitoring BC ecosystems at large scales remains a challenge owing to practical difficulties in monitoring and the time-consuming field measurement approaches used. As a result, sensible policies and actions for the sustainability and conservation of BC ecosystems can be hard to implement. In this context, remote sensing provides a useful tool for mapping and monitoring BC ecosystems faster and at larger scales. Numerous studies have been carried out on various sensors based on optical imagery, synthetic aperture radar (SAR), light detection and ranging (LiDAR), aerial photographs (APs), and multispectral data. Remote sensing-based approaches have been proven effective for mapping and monitoring BC ecosystems by a large number of studies. However, to the best of our knowledge, this is the first comprehensive review on the applications of remote sensing techniques for mapping and monitoring BC ecosystems. The main goal of this review is to provide an overview and summary of the key studies undertaken from 2010 onwards on remote sensing applications for mapping and monitoring BC ecosystems. Our review showed that optical imagery, such as multispectral and hyper-spectral data, is the most common for mapping BC ecosystems, while the Landsat time-series are the most widely-used data for monitoring their changes on larger scales. We investigate the limitations of current studies and suggest several key aspects for future applications of remote sensing combined with state-of-the-art machine learning techniques for mapping coastal vegetation and monitoring their extents and changes.

Comparing the performance of machine learning algorithms for remote and in situ estimations of chlorophyll-a content: A case study in the Tri An Reservoir, Vietnam.

Chlorophyll-a (Chl-a) is one of the most important indicators of the trophic status of inland waters, and its continued monitoring is essential. Recently, the operated Sentinel-2 MSI satellite offers high spatial resolution images for remote water quality monitoring. In this study, we tested the performance of the three well-known machine learning (ML) (random forest [RF], support vector machine [SVM], and Gaussian process [GP]) and the two novel ML (extreme gradient boost (XGB) and CatBoost [CB]) models for estimation a wide range of Chl-a concentration (10.1-798.7 µg/L) using the Sentinel-2 MSI data and in situ water quality measurement in the Tri An Reservoir (TAR), Vietnam. GP indicated the most reliable model for predicting Chl-a from water quality parameters (R2 = 0.85, root-mean-square error [RMSE] = 56.65 µg/L, Akaike's information criterion [AIC] = 575.10, and Bayesian information criterion [BIC] = 595.24). Regarding input model as water surface reflectance, CB was the superior model for Chl-a retrieval (R2 = 0.84, RMSE = 46.28 µg/L, AIC = 229.18, and BIC = 238.50). Our results indicated that GP and CB are the two best models for the prediction of Chl-a in TAR. Overall, the Sentinel-2 MSI coupled with ML algorithms is a reliable, inexpensive, and accurate instrument for monitoring Chl-a in inland waters. PRACTITIONER POINTS: Machine learning algorithms were used for both remote sensing data and in situ water quality measurements. The performance of five well-known machine learning models was tested Gaussian process was the most reliable model for predicting Chl-a from water quality parameters CatBoost was the best model for Chl-a retrieval from water surface reflectance.

Inland harmful cyanobacterial bloom prediction in the eutrophic Tri An Reservoir using satellite band ratio and machine learning approaches.

In recent years, Tri An, a drinking water reservoir for millions of people in southern Vietnam, has been affected by harmful cyanobacterial blooms (HCBs), raising concerns about public health. It is, therefore, crucial to gain insights into the outbreak mechanism of HCBs and understand the spatiotemporal variations of chlorophyll-a (Chl-a) in this highly turbid and productive water. This study aims to evaluate the predictable performance of both approaches using satellite band ratio and machine learning for Chl-a concentration retrieval-a proxy of HCBs. The monthly water quality samples collected from 2016 to 2018 and 23 cloud free Sentinel-2A/B scenes were used to develop Chl-a retrieval models. For the band ratio approach, a strong linear relationship with in situ Chl-a was found for two-band algorithm of Green-NIR. The band ratio-based model accounts for 72% of variation in Chl-a concentration from 2016 to 2018 datasets with an RMSE of 5.95 µg/L. For the machine learning approach, Gaussian process regression (GPR) yielded superior results for Chl-a prediction from water quality parameters with the values of 0.79 (R2) and 3.06 µg/L (RMSE). Among various climatic parameters, a high correlation (R2 = 0.54) between the monthly total precipitation and Chl-a concentration was found. Our analysis also found nitrogen-rich water and TSS in the rainy season as the driving factors of observed HCBs in the eutrophic Tri An Reservoir (TAR), which offer important solutions to the management of HCBs in the future.