For well-founded decisions in sustainable timber harvesting, it is important to know the preferences of different stakeholders. The concept of sustainable timber harvesting is to incorporate economic, social, and environmental criteria. In a previous study, 33 criteria were identified by forest experts as relevant for evaluating sustainability. To assess the importance of these criteria, an online survey was conducted among Austrian stakeholders between April and May 2023, in which 610 people were invited to participate and which resulted in a response rate of 47%. The survey participants were primarily male (94%), with an average age of 47 and an average of 20 years of work experience. The key criteria for sustainable harvesting that were unanimously mentioned by the stakeholders on the basis of a Likert scale, included occupational hazards, residual stand damage, loss of wood quality due to poor work performance, biomass regeneration, water erosion, noise exposure, soil rutting, physical workload, working conditions, and vibration exposure. Younger or less experienced workers generally rated the criteria as less important than older and more experienced workers. These identified preferences will inform the development of a decision support model for sustainable timber harvesting using these criteria as input parameters.

Wildfires lead to socio-economic and environmental impacts. These impacts include hydrological instability, which can cause severe damage, especially where infrastructures are present. Post-rehabilitation measures can be useful in reducing or preventing erosion or hydrogeological risks. Decision-makers are called on to prioritize post-fire intervention areas and allocate public funds for this purpose. This work focuses on the assessment of erosion and hydrological risk potential in forested slope areas affected by wildfire using a Multi-Criteria Decision Analysis (MCDA) approach integrated with a GIS environment on a regional scale. Expert perception was considered using the pairwise comparison method as part of the Analytical Hierarchy Process (AHP). This allows expert stakeholders to rank relevant criteria, providing a quantitative metric (weight) for qualitative data. Two MCDA methods are used and compared: Weighted Linear Combination (WLC) and Ordered Weighted Averaging (OWA). Fire frequency, slope (gradient and length), and proximity to infrastructures were found to be the most important factors by the stakeholders. The WLC method provides evidence classified into high and moderate suitability class areas characterized by high values for fire frequency or slope gradient. Conversely, the OWA method, ranging from low to high risks, makes it possible to adapt the method and obtain a range of suitability maps. Novelties of the MCDA-GIS combined methodology adopted in this work are its application on a regional scale and the combination of vulnerability and driving-force factors (namely presence of grey infrastructures, fire frequency). The MCDA-GIS methodology can be suitable for public administrations in that it allows for mapping a regional area more quickly and thus facilitates sector planning.

Seismic events remain a significant threat, causing loss of life and extensive damage in vulnerable regions. Soil liquefaction, a complex phenomenon where soil particles lose confinement, poses a substantial risk. The existing conventional simplified procedures, and some current machine learning techniques, for liquefaction assessment reveal limitations and disadvantages. Utilizing the publicly available liquefaction case history database, this study aimed to produce a rule-based liquefaction triggering classification model using rough set-based machine learning, which is an interpretable machine learning tool. Following a series of procedures, a set of 32 rules in the form of IF-THEN statements were chosen as the best rule set. While some rules showed the expected outputs, there are several rules that presented attribute threshold values for triggering liquefaction. Rules that govern fine-grained soils emerged and challenged some of the common understandings of soil liquefaction. Additionally, this study also offered a clear flowchart for utilizing the rule-based model, demonstrated through practical examples using a borehole log. Results from the state-of-practice simplified procedures for liquefaction triggering align well with the proposed rule-based model. Recommendations for further evaluations of some rules and the expansion of the liquefaction database are warranted.

Lateral spreading is one of the most common secondary earthquake effects that cause severe damage to structures and lifelines. While there is no widely accepted approach to predicting lateral spread displacements, challenges to the existing empirical and machine learning models include obscurity, overfitting, and reluctance of practical users. This study reveals patterns in the available lateral displacement database, identifying rules that describe the significant relationships among various attributes that led to lateral spreading. Seven conditional attributes (earthquake magnitude, epicentral distance, maximum acceleration, fines content, mean grain size, thickness of liquefiable layer, and free -face ratio) and one decision attribute (horizontal displacement) were considered in modeling a binary class rough set machine learning. There are eighteen rules generated in the form of if -then statements. The decision support system reveals that the severity of lateral spreading clearly comes from the combinations of relevant attributes. Moreover, five clusters of rules were also observed from the generated rules. Useful information regarding the different lateral spreading case scenarios emerges from the results. Statistical validation and interpretation of rules using principles of soil mechanics and related studies were also performed. The output of this study, a decision support system, can be very useful to decision -makers and planners in understanding the lateral spreading phenomena. Recommendations for the model improvement and for further studies were discussed.

Inappropriate fertilisation results in the pollution of groundwater with nitrates and phosphates, eutrophication in surface water, emission of greenhouse gasses, and unwanted N deposition in natural environments, thereby harming the whole ecosystem. In greenhouses, the cultivation in closed-loop soilless culture systems (CLSs) allows for the collection and recycling of the drainage solution, thus minimising contamination of water resources by nutrient emissions originating from the fertigation effluents. Recycling of the DS represents an ecologically sound technology as it can reduce water consumption by 20-35% and fertiliser use by 40-50% in greenhouse crops, while minimising or even eliminating losses of nutrients, thereby preventing environmental pollution by NO3- and P. The nutrient supply in CLSs is largely based on the anticipated ratio between the mass of a nutrient absorbed by the crop and the volume of water, expressed as mmol L-1, commonly referenced to as uptake concentration (UC). However, although the UCs exhibit stability over time under optimal climatic conditions, some deviations at different locations and different cropping stages can occur, leading to the accumulation or depletion of nutrients in the root zone. Although these may be small in the short term, they can reach harmful levels when summed up over longer periods, resulting in serious nutrient imbalances and crop damage. To prevent large nutrient imbalances in the root zone, the composition of the supplied nutrient solution must be frequently readjusted, taking into consideration the current nutrient status in the root zone of the crop. The standard practice to estimate the current nutrient status in the root zone is to regularly collect samples of drainage solution and determine the nutrient concentrations through chemical analyses. However, as results from a chemical laboratory are available several days after sample selection, there is currently intensive research activity aiming to develop ion-selective electrodes (ISEs) for online measurement of the DS composition in real-time. Furthermore, innovative decision support systems (DSSs) fed with the analytical results transmitted either offline or online can substantially contribute to timely and appropriate readjustments of the nutrient supply using as feedback information the current nutrient status in the root zone. The purpose of the present paper is to review the currently applied technologies for nutrient and water recycling in CLSs, as well as the new trends based on ISEs and novel DSSs. Furthermore, a specialised DSS named NUTRISENSE, which can contribute to more efficient management of nutrient supply and salt accumulation in closed-loop soilless cultivations, is presented.

Landslides are downward movements of soil, rock, and debris along slopes, and pose significant risks to communities, especially in inhabited areas as they can cause severe damage, including the destruction of infrastructure and loss of life. Solutions for prediction and mitigation strategies are crucial, which often relying on rainfall forecasting and monitoring through sensor and IoT technologies. However, such solutions can be costly and challenging to implement, particularly in developing countries. By taking advantage of 5G networks, this paper proposes an innovative Received Signal Strength Indication (RSSI)-based solution to estimate the landslide risk and assist in mitigation actions in advance. Our solution also incorporates Software-Defined Networks (SDN) to manage the collected real-time data, compute the landslide risk, and notify users in the affected area. Results show that adopting 5G RSSI can significantly improve the accuracy in detecting rains and landslides, as shown by a high Pearson correlation coefficient (0.984), a Mean Squared Error (MSE) of 0.0087, and a coefficient of determination (R-2) of 0.6185 for the RSSI-bases solution.