MORPHOMETRIC TERRAIN ANALYSIS TO EXPLORE PRESENT DAY GEOHAZARDS AND PALEOLANDSCAPE FORMS AND FEATURES IN THE SURROUNDINGS OF THE MELKA KUNTURE PREHISTORIC SITE, UPPER AWASH VALLEY, CENTRAL ETHIOPIA

Morphometric Terrain Analysis was successfully applied in different sectors of environmental studies. However, other disciplines, such as archaeology, might also profit from spatially distributed high-resolution terrain information. In this paper, we show how detailed topographic analysis and simple hydrological modelling approaches help to explain complex terrain pattern and to assess geohazards affecting archaeological sites. We show that Melka Kunture, a cluster of Pleistocene sites in the Upper Awash valley of Ethiopia, is affected by flooding and erosion/sedimentation processes. Moreover, we identified paleo-landscape features, such as changes in drainage pattern and evidences of tectonic activity. The topographic indices indicate especially a different paleo-drainage pattern with a lake or palustrine environment in the upstream areas. Furthermore, a different drainage of the paleo-lake via the Atabella tributary is likely and might be also stressed by the dimensions of the lower Atabella valley with quite large cross sections not corresponding to the present-day drainage situation

Flood susceptibility assessment using artificial neural networks in Indonesia

Flood incidents can massively damage and disrupt a city economic or governing core. However, flood risk can be mitigated through event planning and city-wide preparation to reduce damage. For, governments, firms, and civilians to make such preparations, flood susceptibility predictions are required. To predict flood susceptibility nine environmental related factors have been identified. They are elevation, slope, curvature, topographical wetness index (TWI), Euclidean distance from a river, land-cover, stream power index (SPI), soil type and precipitation. This work will use these environmental related factors alongside Sentinel-1 satellite imagery in a model intercomparison study to back-predict flood susceptibility in Jakarta for the January 2020 historic flood event across 260 key locations. For each location, this study uses current environmental conditions to predict flood status in the following month. Considering the imbalance between instances of flooded and non-flooded conditions, the Synthetic Minority Oversampling Technique (SMOTE) has been implemented to balance both classes in the training set. This work compares predictions from artificial neural networks (ANN), k-Nearest Neighbors algorithms (k-NN) and Support Vector Machines (SVM) against a random baseline. The effects of the SMOTE are also assessed by training each model on balanced and imbalanced datasets. The ANN is found to be superior to the other machine learning models

Borough-level COVID-19 forecasting in London using deep learning techniques and a novel MSE-Moran’s I loss function

Following its identification in late 2019, COVID-19 has spread around the globe, and been declared a pandemic. With this in mind, modelling the spread of COVID-19 remains important for responding effectively. To date research has focused primarily on modelling the spread of COVID-19 on national and regional scales with just a few studies doing so on a city and sub-city scale. However, no attempts have yet been made to design and optimize a model explicitly for accurately forecasting the spread of COVID-19 at sub-city scale. This research aimed to address this research gap by developing an experimental LSTM-ANN deep learning model. The model is largely autoregressive in nature as it considers temporally lagged borough-level COVID-19 cases data from the last 9 days, but also considers temporally lagged (i) borough-level NO2 concentration data, (ii) government stringency data, and (iii) climatic data from the last 9 days, as well as non-temporally variable borough-level urban characteristics data when modelling and forecasting the spread of the disease. The model was also encouraged to learn the spatial relationships between boroughs with regards to the spread of COVID-19 by a novel MSE-Moran's I loss function. Overall, the model's performance appears promising and so the model represents a useful tool for assisting the decision making and interventions of governing bodies within cities. A sensitivity analysis also indicated that of the non COVID-19 variables, the government stringency is particularly important in the modelling process, with this being closely followed by the climatic variables, the NO2 concentration data, and finally the urban characteristics data. Additionally, the introduction of the novel MSE-Moran's I loss function appeared to improve the model's forecasting performance, and so this research has implications at the intersection of deep learning and disease modelling. It may also have implications within spatio-temporal forecasting more generally because such a feature may have the potential to improve forecasting in other spatio-temporal applications

Bibliometric Analysis of Soil and Landscape Stability, Sensitivity and Resistivity

In times of global change, it is of fundamental importance to understand the sensitivity, stability and resistivity of a landscape or ecosystem to human disturbance. Landscapes and ecosystems have internal thresholds, giving them the ability to resist such disturbance. When these thresholds are quantified, the development of countermeasures can help prevent irreversible changes and support adaptations to the negative effects of global change. The main objective of this analysis is to address the lack of recent studies defining terms like sensitivity, resistivity and stability in reference to landscapes and ecosystems through a Bibliometric analysis based on Scopus and Web of Science peer-reviewed articles. The present research also aims to quantify landscape statuses in terms of their sensitivity, stability and resistivity. The term “landscape stability” is mainly related to quantitatively measurable properties indicating a certain degree of stability. In contrast, the term “landscape sensitivity” is often related to resilience; however, this definition has not substantially changed over time. Even though a large number of quantification methods related to soil and landscape stability and sensitivity were found, these methods are rather ad hoc. This study stresses the importance of interdisciplinary studies and work groups

Deep Learning Based Burnt Area Mapping Using Sentinel 1 for the Santa Cruz Mountains Lightning Complex (CZU) and Creek Fires 2020

The study presented here builds on previous synthetic aperture radar (SAR) burnt area estimation models and presents the first U-Net (a convolutional network architecture for fast and precise segmentation of images) combined with ResNet50 (Residual Networks used as a backbone for many computer vision tasks) encoder architecture used with SAR, Digital Elevation Model, and land cover data for burnt area mapping in near-real time. The Santa Cruz Mountains Lightning Complex (CZU) was one of the most destructive fires in state history. The results showed a maximum burnt area segmentation F1-Score of 0.671 in the CZU, which outperforms current models estimating burnt area with SAR data for the specific event studied models in the literature, with an F1-Score of 0.667. The framework presented here has the potential to be applied on a near real-time basis, which could allow land monitoring as the frequency of data capture improves

Soil organic carbon under conservation agriculture in Mediterranean and humid subtropical climates: Global meta‐analysis

Conservation agriculture (CA) is an agronomic system based on minimum soil disturbance (no-tillage, NT), permanent soil cover, and species diversification. The effects of NT on soil organic carbon (SOC) changes have been widely studied, showing somewhat inconsistent conclusions, especially in relation to the Mediterranean and humid subtropical climates. These areas are highly vulnerable and predicted climate change is expected to accentuate desertification and, for these reasons, there is a need for clear agricultural guidelines to preserve or increment SOC. We quantitively summarized the results of 47 studies all around the world in these climates investigating the sources of variation in SOC responses to CA, such as soil characteristics, agricultural management, climate, and geography. Within the climatic area considered, the overall effect of CA on SOC accumulation in the plough layer (0–0.3 m) was 12% greater in comparison to conventional agriculture. On average, this result corresponds to a carbon increase of 0.48 Mg C ha−1 year−1. However, the effect was variable depending on the SOC content under conventional agriculture: it was 20% in soils which had ≤ 40 Mg C ha−1, while it was only 7% in soils that had > 40 Mg C ha−1. We proved that 10 years of CA impact the most on soil with SOC ≤ 40 Mg C ha−1. For soils with less than 40 Mg C ha−1, increasing the proportion of crops with bigger residue biomasses in a CA rotation was a solution to increase SOC. The effect of CA on SOC depended on clay content only in soils with more than 40 Mg C ha−1 and become null with a SOC/clay index of 3.2. Annual rainfall (that ranged between 331–1850 mm y−1) and geography had specific effects on SOC depending on its content under conventional agriculture. In conclusion, SOC increments due to CA application can be achieved especially in agricultural soils with less than 40 Mg C ha−1 and located in the middle latitudes or in the dry conditions of Mediterranean and humid subtropical climates

Geomorphological processes, forms and features in the surroundings of the Melka Kunture Palaeolithic site, Ethiopia

The landscape of the surroundings of the Melka Kunture prehistoric site, Upper Awash Basin, Ethiopia, were studied intensively in the last decades. Nonetheless, the area was mainly characterized under a stratigraphic/geological and archaeological point of view. However, a detailed geomorphological map is still lacking. Hence, in this study, we identify, map and visualize geomorphological forms and processes. The morphology of the forms, as well as the related processes, were remotely sensed with available high-resolution airborne and satellite sources and calibrated and validated through extensive field work conducted in 2013 and 2014. Furthermore, we integrated multispectral satellite imagery to classify areas affected by intensive erosion processes and/or anthropic activities. The Main Map at 1:15,000 scale reveals structural landforms as well as intensive water-related degradation processes in the Upper Awash Basin. Moreover, the map is available as an interactive WebGIS application providing further information and detail (www.roceeh.net/ethiopia_ geomorphological_map/)

A Novel Peptide with Antifungal Activity from Red Swamp Crayfish Procambarus clarkii

The defense system of freshwater crayfish Procambarus clarkii as a diversified source of bioactive molecules with antimicrobial properties was studied. Antimicrobial activity of two polypeptideenriched extracts obtained from hemocytes and hemolymph of P. clarkii were assessed against Gram positive (Staphylococcus aureus, Enterococcus faecalis) and Gram negative (Pseudomonas aeruginosa, Escherichia coli) bacteria and toward the yeast Candida albicans. The two peptide fractions showed interesting MIC values (ranging from 11 to 700 g/mL) against all tested pathogens. Polypeptideenriched extracts were further investigated using a high-resolution mass spectrometry and database search and 14 novel peptides were identified. Some peptides and their derivatives were chemically synthesized and tested in vitro against the bacterial and yeast pathogens. The analysis identified a synthetic derivative peptide, which showed an interesting antifungal (MIC and MFC equal to 31.2 g/mL and 62.5 g/mL, respectively) and antibiofilm (BIC50 equal to 23.2 g/mL) activities against Candida albicans and a low toxicity in human cells