Predicting optical water quality indicators from remote sensing using machine learning algorithms in tropical highlands of Ethiopia

Water quality degradation of freshwater bodies is a concern worldwide, particularly in Africa, where data are scarce and standard water quality monitoring is expensive. This study explored the use of remote sensing imagery and machine learning (ML) algorithms as an alternative to standard field measuring for monitoring water quality in large and remote areas constrained by logistics and finance. Six machine learning (ML) algorithms integrated with Landsat 8 imagery were evaluated for their accuracy in predicting three optically active water quality indicators observed monthly in the period from August 2016 to April 2022: turbidity (TUR), total dissolved solids (TDS) and Chlorophyll a (Chl-a). The six ML algorithms studied were the artificial neural network (ANN), support vector machine regression (SVM), random forest regression (RF), XGBoost regression (XGB), AdaBoost regression (AB), and gradient boosting regression (GB) algorithms. XGB performed best at predicting Chl-a, with an R2 of 0.78, Nash–Sutcliffe efficiency (NSE) of 0.78, mean absolute relative error (MARE) of 0.082 and root mean squared error (RMSE) of 9.79 µg/L. RF performed best at predicting TDS (with an R2 of 0.79, NSE of 0.80, MARE of 0.082, and RMSE of 12.30 mg/L) and TUR (with an R2 of 0.80, NSE of 0.81, and MARE of 0.072 and RMSE of 7.82 NTU). The main challenges were data size, sampling frequency, and sampling resolution. To overcome the data limitation, we used a K-fold cross validation technique that could obtain the most out of the limited data to build a robust model. Furthermore, we also employed stratified sampling techniques to improve the ML modeling for turbidity. Thus, this study shows the possibility of monitoring water quality in large freshwater bodies with limited observed data using remote sensing integrated with ML algorithms, potentially enhancing decision making

Predicting discharge and sediment for the Abay (Blue Nile) with a simple model

Models accurately representing the underlying hydrological processes and sediment dynamics in the Nile Basin are necessary for optimum use of water resources. Previous research in the Abay (Blue Nile) has indicated that direct runoff is generated either from saturated areas at the lower portions of the hillslopes or from areas of exposed bedrock. Thus, models that are based on infiltration excess processes are not appropriate. Furthermore, many of these same models are developed for temperate climates and might not be suitable for monsoonal climates with distinct dry periods in the Nile Basin. The objective of this study is to develop simple hydrology and erosion models using saturation excess runoff principles and interflow processes appropriate for a monsoonal climate and a mountainous landscape. We developed a hydrology model using a water balance approach by dividing the landscape into variable saturated areas, exposed rock and hillslopes. Water balance models have been shown to simulate river flows well at intervals of 5 days or longer when the main runoff mechanism is saturation excess. The hydrology model was developed and coupled with an erosion model using available precipitation and potential evaporation data and a minimum of calibration parameters. This model was applied to the Blue Nile. The model predicts direct runoff from saturated areas and impermeable areas (such as bedrock outcrops) and subsurface flow from the remainder of the hillslopes. The ratio of direct runoff to total flow is used to predict the sediment concentration by assuming that only the direct runoff is responsible for the sediment load in the stream. There is reasonable agreement between the model predictions and the 10-day observed discharge and sediment concentration at the gauging station on Blue Nile upstream of Rosaries Dam at the Ethiopia-Sudan border

Role of 1,3-Propane Sultone and Vinylene Carbonate in Solid Electrolyte Interface Formation and Gas Generation

Lithium-ion coin cells containing electrolytes with and without 1,3-propane sultone (PS) and vinylene carbonate (VC) were prepared and investigated. The electrochemical performance of the cells is correlated with ex situ surface analysis of the electrodes conducted by Fourier transform infrared and X-ray photoelectron spectroscopies and in situ gas analysis by online electrochemical mass spectrometry (OEMS). The results suggest that incorporation of both PS and VC results in improved capacity retention upon cycling at 55 °C and lower impedance. Ex situ surface analysis and OEMS confirm that incorporation of PS and VC alter the reduction reactions on the anode inhibiting ethylene generation and changing the structure of the solid electrolyte interface. Incorporation of VC results in CO₂ evolution, formation of poly­(VC), and inhibition of ethylene generation. Incorporation of PS results in generation of lithium alkylsulfonate (RSO₂Li) and inhibition of ethylene generation. The combination of PS and VC reduces the ethylene gassing during formation by more than 60%