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.