A regression approach to the mapping of bio-physical characteristics of surface sediment using in situ and airborne hyperspectral acquisitions

Remote sensing has been successfully utilized to distinguish and quantify sediment properties in the intertidal environment. Classification approaches of imagery are popular and powerful yet can lead to site- and case-specific results. Such specificity creates challenges for temporal studies. Thus, this paper investigates the use of regression models to quantify sediment properties instead of classifying them. Two regression approaches, namely multiple regression (MR) and support vector regression (SVR), are used in this study for the retrieval of bio-physical variables of intertidal surface sediment of the IJzermonding, a Belgian nature reserve. In the regression analysis, mud content, chlorophyll a concentration, organic matter content, and soil moisture are estimated using radiometric variables of two airborne sensors, namely airborne hyperspectral sensor (AHS) and airborne prism experiment (APEX) and and using field hyperspectral acquisitions by analytical spectral device (ASD). The performance of the two regression approaches is best for the estimation of moisture content. SVR attains the highest accuracy without feature reduction while MR achieves good results when feature reduction is carried out. Sediment property maps are successfully obtained using the models and hyperspectral imagery where SVR used with all bands achieves the best performance. The study also involves the extraction of weights identifying the contribution of each band of the images in the quantification of each sediment property when MR and principal component analysis are used.     

Self-Learning Random Forests Model for Mapping Groundwater Yield in Data-Scarce Areas

Globally, groundwater plays a major role in supplying drinking water for urban and rural population and is used for irrigation to grow crops and in many industrial processes. A novel self-learning random forest (SLRF) model is developed and validated for groundwater yield zonation within the Yeondong Province in South Korea. This study was conducted with an inventory data initially divided randomly into 70% for training and 30% for testing and 13 groundwater-conditioning factors. SLRF was optimized using Bayesian optimization method. We also compared our method to other machine learning methods including support vector machine (SVM), artificial neural networks (ANN), decision trees (DT), and voting ensemble models. Model validation was accomplished using several methods, including a confusion matrix, receiver operating characteristics, cross-validation, and McNemar’s test. Our proposed self-learning method improves random forest (RF) generalization performance by about 23%, with SLRF success rates of 0.76 and prediction rates of 0.83. In addition, the optimized SLRF performed better [according to a threefold cross-validated AUC (area under curve) of 0.75] than that using randomly initialized parameters (0.57). SLRF outperformed all of the other models for the testing dataset (RF, SVM, ANN, DT, and Voted ANN-RF) when the overall accuracy, prediction rate, and cross-validated AUC metrics were considered. The SLRF also estimated the contribution of individual groundwater conditioning factors and showed that the three most influential factors were geology (1.00), profile curvature (0.97), and TWI (0.95). Overall, SLRF effectively modeled groundwater potential, even within data-scarce regions.

     

Stability evaluation of the Gümü?hane-Ak?akale cave by numerical analysis method

The Ak?akale Cave is located in the vicinity of the Arsa neighborhood within the boundaries of the Ak?akale village, Gümü?hane, Turkey. The cave is rich in cave formations(stalactite, stalagmite, cave pearl, cave flower, wall travertines). Thus, the appropriateness of opening the cave to visitors to boost tourism is of importance for the local and national economy. This study analyzes the stability of the Ak?akale Cave using a numerical analysis method. According to the results of the total displacement analysis, there are displacements in the entrance, ceiling, and sidewalls of the cave ranging from 1 mm to 48 mm. It seems that the entrance, ceiling, and sidewalls of the cave face a high risk of local or sudden collapse. According to the deformation analysis of the length section of the cave examined, local collapses may occur especially in the first 75 m from the entrance of the cave. We believe that this situation would not carry a risk for the Arsa neighborhood for now. In conclusion, the results of the stability analysis and in-situ observations showed clear evidence of former and ongoing cave-ins(collapses) and the Ak?akale Cave faces a high risk of local or sudden collapse. Thus, although the Ak?akale Cave is one of the most prominent karst caves in Turkey, it seems to be not appropriate to open the cave to tourist visits.     

Effect of Saline Sludge on Anaerobic Digestion of Kitchen Waste for Methane Generation: Modeling and Fertilizer Recovery

A comparative study of methane recovery by co-digesting kitchen waste and saline sewage sludge is performed to evaluate its feasibility for waste minimization. The experiment is performed at 37 °C having a mixing speed of 100 rpm and pH of 6.49–7.5 in anaerobic mixed batch reactors. The higher salinity level of the saline sewage sludge reduces the degradation rate of kitchen waste causing an enhancement in soluble chemical oxygen demand by 133% compared with 280% when co-digesting with the non-saline sample. The inhibitory behavior is in line with the low volatile solid elimination efficiency of 31% of saline against 55% of non-saline sludge. The Gompertz modeling, based on the outcomes, fits the cumulative methane generation trends quite well, with a strong correlation coefficient (>0.994). Besides, use of the non-saline sludge results in three times more methane production than the saline sample digestion. Sludge recovery is 0.07 m³ sludge m⁻³ wastewater, and water recovery is 0.84 m³ m⁻³ wastewater. The liquid produced from the fermentation of the slurry can be used for irrigation as well as fertilization. Kitchen waste co-digestion with both sludge samples has been proven to be a practical method for exploiting the extra digestion capacity of wastewater treatment plants currently in operation, but it is more practical for non-saline sludge.     

Sustainable Development Goals (SDGs) Associated with Groundwater Over-Exploitation Vulnerability: Geographic Information System-Based Multi-criteria Decision Analysis

Natural Resources Research - Groundwater over-exploitation is related to various environmental, economic, and social aspects, which should be addressed comprehensively to support a sustainable...     

Probabilistic earthquake hazard analysis for Cairo,Egypt

Cairo is the capital of Egypt and the largest city in the Arab world and Africa, and the sixteenth largest metropolitan area in the world. It was founded in the tenth century (969 ad) and is 1046 years old. It has long been a center of the region’s political and cultural life. Therefore, the earthquake risk assessment for Cairo has a great importance. The present work aims to analysis the earthquake hazard of Cairo as a key input’s element for the risk assessment. The regional seismotectonics setting shows that Cairo could be affected by both far- and near-field seismic sources. The seismic hazard of Cairo has been estimated using the probabilistic seismic hazard approach. The logic tree frame work was used during the calculations. Epistemic uncertainties were considered into account by using alternative seismotectonics models and alternative ground motion prediction equations. Seismic hazard values have been estimated within a grid of 0.1°?×?0.1 ° spacing for all of Cairo’s districts at different spectral periods and four return periods (224, 615, 1230, and 4745 years). Moreover, the uniform hazard spectra have been calculated at the same return periods. The pattern of the contour maps show that the highest values of the peak ground acceleration is concentrated in the eastern zone’s districts (e.g., El Nozha) and the lowest values at the northern and western zone’s districts (e.g., El Sharabiya and El Khalifa).     

Path planning in uncertain flow fields using ensemble method

An ensemble-based approach is developed to conduct optimal path planning in unsteady ocean currents under uncertainty. We focus our attention on two-dimensional steady and unsteady uncertain flows, and adopt a sampling methodology that is well suited to operational forecasts, where an ensemble of deterministic predictions is used to model and quantify uncertainty. In an operational setting, much about dynamics, topography, and forcing of the ocean environment is uncertain. To address this uncertainty, the flow field is parametrized using a finite number of independent canonical random variables with known densities, and the ensemble is generated by sampling these variables. For each of the resulting realizations of the uncertain current field, we predict the path that minimizes the travel time by solving a boundary value problem (BVP), based on the Pontryagin maximum principle. A family of backward-in-time trajectories starting at the end position is used to generate suitable initial values for the BVP solver. This allows us to examine and analyze the performance of the sampling strategy and to develop insight into extensions dealing with general circulation ocean models. In particular, the ensemble method enables us to perform a statistical analysis of travel times and consequently develop a path planning approach that accounts for these statistics. The proposed methodology is tested for a number of scenarios. We first validate our algorithms by reproducing simple canonical solutions, and then demonstrate our approach in more complex flow fields, including idealized, steady and unsteady double-gyre flows.