Spatio-temporal classification and prediction of land use and land cover change for the Vembanad Lake system, Kerala: a machine learning approach.

Land use and land cover (LULC) change has become a critical issue for decision planners and conservationists due to inappropriate growth and its effect on natural ecosystems. As a result, the goal of this study is to identify the LULC for the Vembanad Lake system (VLS), Kerala, in the short term, i.e., within a decade, utilizing three standard machine learning approaches, random forest (RF), classification and regression trees (CART), and support vector machines (SVM), on the Google Earth Engine (GEE) platform. When comparing the three techniques, SVM performed poor at an average accuracy of around 82.5%, CART being the next at accuracy of 87.5%, and the RF model being good at the average of 89.5%. The RF outperformed the SVM and CART in almost identical spectral classes such as barren land and built-up areas. As a result, RF-classified LULC is considered to predict the spatio-temporal distribution of LULC transition analysis for 2035 and 2050. The study was conducted in Idrisi TerrSet software using the cellular automata (CA)-Markov chain analysis. The model's efficiency is evaluated by comparing the projected 2019 image to the actual 2019 classified image. The efficiency was good with more than 94.5% accuracy for the classes except for barren land, which might have resulted from the recent natural calamities and the accelerated anthropogenic activity in the area.

Spatial and temporal variations in river water quality of the Middle Ganga Basin using unsupervised machine learning techniques.

In this study, cluster analysis (CA), principal component analysis (PCA) and correlation were applied to access the river water quality status and to understand spatiotemporal patterns in the Ganga River Basin, Uttara Pradesh. The study was carried out using data collected over 12 years (2005-2017) regarding 20 water quality parameters (WQPs) covering spatially from upstream to downstream Ankinghat to Chopan, respectively (20 stations under CWC Middle Ganga Basin). The temporal variations of river water quality were established using the Spearman non-parametric correlation coefficient test (Spearman R). The highest Spearman R (-0.866) was observed for temperature with the season and a very significant p value of (0.0000). The parameters EC, pH, TDS, T, Ca, Cl, HCO3, Mg, NO2 + NO3, SiO2 and DO had a significant correlation with the season (p < 0. 05). K-means clustering algorithm grouped the stations into four different clusters in dry and wet seasons. Based on these clusters, box and whisker plots were generated to study individual clusters in different seasons. The spatial patterns of river WQ on both seasons were examined. PCA was applied to screen out the most significant water quality parameters due to spatial and seasonal variations out of a large data set. It is a data reduction process and a more conventional way of speeding up any machine learning algorithms. A reduced number of three principal components (PCs) were drawn for 20 WQPs with an explained total variance of 75.84% and 80.57% is observed in the dry and wet season, respectively. The parameters DO, EC_ Gen, P-Tot, SO4 are the most dominating parameters with PC score more than 0.8 in the dry season; similarly, TDS, K, COD, Cl, Na, SiO2 in the wet season. The different components of water quality monitoring, such as spatiotemporal patterns, scrutinize the most relevant water quality parameters and monitoring stations are well addressed in this study and could be used for the better management of the Ganga River Basin.

Factors influencing streambed hydraulic conductivity and their implications on stream-aquifer interaction: a conceptual review.

The estimation and modeling of streambed hydraulic conductivity (K) is an emerging interest due to its connection to water quality, aquatic habitat, and groundwater recharge. Existing research has found ways to sample and measure K at specific sites and with laboratory tests. The challenge undertaken was to review progress, relevance, complexity in understanding and modeling via statistical and geostatistical approaches, literature gaps, and suggestions toward future needs. This article provides an overview of factors and processes influencing streambed hydraulic conductivity (K) and its role in the stream-aquifer interaction. During our synthesis, we discuss the influence of geological, hydrological, biological, and anthropogenic factors that lead to variability of streambed substrates. Literature examples document findings to specific sites that help to portray the role of streambed K and other interrelated factors in the modeling of hyporheic and groundwater flow systems. However, studies utilizing an integrated, comprehensive database are limited, restricting the ability of broader application and understanding. Examples of in situ and laboratory methods of estimating hydraulic conductivity suggest challenges in acquiring representative samples and comparing results, considering the anisotropy and heterogeneity of fluvial bed materials and geohydrological conditions. Arriving at realistic statistical and spatial inference based on field and lab data collected is challenging, considering the possible sediment sources, processes, and complexity. Recognizing that the K for a given particle size group includes several to many orders of magnitude, modeling of streambed K and groundwater interaction remain conceptual and experimental. Advanced geostatistical techniques offer a wide range of univariate or multi-variate interpolation procedures such as kriging and variogram analysis that can be applied to these complex systems. Research available from various studies has been instrumental in developing sampling options, recognizing the significance of fluvial dynamics, the potential for filtration, transfer, and storage of high-quality groundwater, and importance to aquatic habitat and refuge during extreme conditions. Efforts in the characterization of natural and anthropogenic conditions, substrate materials, sediment loading, colmation, and other details highlight the great complexity and perhaps need for a database to compile relevant data. The effects on streambed hydraulic conductivity due to anthropogenic disturbances (in-stream gravel mining, contaminant release, benthic activity, etc.) are the areas that still need focus. An interdisciplinary (hydro-geo-biological) approach may be necessary to characterize the magnitude and variability of streambed K and fluxes at local, regional scales.

WITHDRAWN: Significance of streambed hydraulic conductivity in stream-aquifer interaction - A systematic review of fundamentals and recent research.

This article has been withdrawn at the request of the editor due to dual submission made by the authors. The Publisher apologizes for any inconvenience this may cause to the readers. The full Elsevier Policy on Article Withdrawal can be found at http://www.elsevier.com/locate/withdrawalpolicy.