Physico-Chemical and Microbiological Assessment of Organic Pollution in Plain Salty Lakes from Protected Regions

One of the most important goals of EU nature and water framework directives is to ensure healthy aquatic ecosystems by the protection of the most valuable species and habitats form the Natura 2000 network, while at the same time ensuring a balance between water/nature protection and the sustainable use of nature’s natural resources. The purpose of this study was to evaluate the physico-chemical and microbiological characteristics of four Romanian salty plain lakes included in Natura 2000 Network, in order to assess the degree of organic pollution and to generate the knowledge required for the design and implementation of appropriate measures for maintaining the balance between the water protection and the sustainable use of these protected ecosystems. The water and sediment sampling was performed in two consecutive years (2015 and 2016), in September and the following standard parameters have been determined: pH, chemical oxygen consumption (COC), the degree of trophicity and salinity of the environment, metals content, microbiological indicators and microbial physiological groups involved in nutrient cycling. The pH ranged from 7.56 to 8.93, close or above the upper normal limit of 8.5, being correlated with a high salinity characteristic of chlorinated, sulphated, high sodium and magnesium content waters. Despite the similar values recorded for the physico-chemical parameters in the two consecutive years suggesting a certain degree of stability of the investigated aquatic ecosystems, the COC values indicate a high degree of hypertrophy, which could be attributed to the reduced surface area, ecological pisciculture and agriculture input. The microbiological parameters revealed the existence of both recent and chronic fecal pollution source. The high hypertrophy degree could represent a positive premise for the high productivity of the investigated ecosystems, but also an alarm signal for excessive organic pollution, with the risk of redox potential decrease which can affect the fish and other life forms. Consequently, it is necessary to identify the sources of pollution and implement the appropriate measures to minimize the negative impact of organic contamination on the status of the respective ecosystems (water quality, biotic components) in order to maintain the health of both ecosystems and the surrounding human communities, allowing at the same time a sustainable use of the local resources.

GIS-based landslide susceptibility modeling:A comparison between fuzzy multi-criteria and machine learning algorithms

Hazards and disasters have always negative impacts on the way of life.Landslide is an overwhelming natural as well as man-made disaster that causes loss of natural resources and human properties throughout theworld.The present study aimed to assess and compare the prediction efficiency of different models in landslide susceptibility in the Kysuca river basin,Slovakia.In this regard,the fuzzy decision-making trial and evaluation laboratory combining with the analytic network process(FDEMATEL-ANP),Naïve Bayes(NB)classifier,and random forest(RF)classifier were considered.Initially,a landslide inventory map was produced with 2000 landslide and nonlandslide points by randomly dividedwith a ratio of 70%:30%for training and testing,respectively.The geospatial database for assessing the landslide susceptibility was generated with the help of 16 landslide conditioning factors by allowing for topographical,hydrological,lithological,and land cover factors.The ReliefF methodwas considered for determining the significance of selected conditioning factors and inclusion in the model building.Consequently,the landslide susceptibility maps(LSMs)were generated using the FDEMATEL-ANP,Naïve Bayes(NB)classifier,and random forest(RF)classifier models.Finally,the area under curve(AUC)and different arithmetic evaluation were used for validating and comparing the results and models.The results revealed that random forest(RF)classifier is a promising and optimum model for landslide susceptibility in the study area with a very high value of area under curve(AUC=0.954),lower value of mean absolute error(MAE=0.1238)and root mean square error(RMSE=0.2555),and higher value of Kappa index(K=0.8435)and overall accuracy(OAC=92.2%).

Locally weighted learning based hybrid intelligence models for groundwater potential mapping and modeling: A case study at Gia Lai province, Vietnam

The groundwater potential map is an important tool for a sustainable water management and land use planning,particularly for agricultural countries like Vietnam.In this article,we proposed new machine learning ensemble techniques namely AdaBoost ensemble(ABLWL),Bagging ensemble(BLWL),Multi Boost ensemble(MBLWL),Rotation Forest ensemble(RFLWL)with Locally Weighted Learning(LWL)algorithm as a base classifier to build the groundwater potential map of Gia Lai province in Vietnam.For this study,eleven conditioning factors(aspect,altitude,curvature,slope,Stream Transport Index(STI),Topographic Wetness Index(TWI),soil,geology,river density,rainfall,land-use)and 134 wells yield data was used to create training(70%)and testing(30%)datasets for the development and validation of the models.Several statistical indices were used namely Positive Predictive Value(PPV),Negative Predictive Value(NPV),Sensitivity(SST),Specificity(SPF),Accuracy(ACC),Kappa,and Receiver Operating Characteristics(ROC)curve to validate and compare performance of models.Results show that performance of all the models is good to very good(AUC:0.75 to 0.829)but the ABLWL model with AUC=0.89 is the best.All the models applied in this study can support decision-makers to streamline the management of the groundwater and to develop economy not only of specific territories but also in other regions across the world with minor changes of the input parameters.