Laboratory of Agricultural Hydraulics-U.Th.

11/05/2026

The Laboratory of Agricultural Hydraulics participated in the 9th Environmental Conference of Macedonia with two studies, related to the hydrodynamic behavior of soils and changes in hydraulic properties in the presence of perlite, as well as the potential pollution of groundwater

31/03/2026

Dear Colleagues,

Escalating global climate change, synergistically coupled with the trajectory of rapid urbanisation across the globe and a discernible surge in the frequency and intensity of extreme rainfall events; increasing temperature trends; and severe stress in water balance, have collectively driven a state of pronounced uncertainty and inherent vulnerability within the domain of water resource management.

In direct response to this paramount challenge, this Special Issue extends an invitation for the submission of high-quality, meticulously conducted research. It is particularly interested in and warmly welcomes original manuscripts that successfully establish and articulate a clear and powerful linkage between the cutting-edge capabilities of artificial intelligence (AI), machine learning (ML), and deep learning (DL) with sophisticated, state-of-the-art methodologies within the specialised field of sustainable water resource management, in both urban and agricultural sectors. Topics of specific interest that leverage AI/ML/DL-driven approaches for sustainable water resource management include, but are not limited to, the following:

Prediction and forecasting: Novel models for high-resolution forecasting of hydrological extremes, including flash floods, flash droughts, pluvial sediment transport and seasonal water availability.
Optimal resource allocation: Intelligent systems for real-time optimisation of water distribution networks, reservoir operation, and irrigation scheduling under uncertainty.
Infrastructure resilience: Application of DL for anomaly detection and predictive maintenance in critical water infrastructure (e.g., pipelines, treatment plants).
Water quality monitoring: AI-based solutions for the continuous and automated analysis of water quality parameters and the identification of contaminant sources.
Integrated modelling: Coupling of physical-based hydrological models with ML/DL algorithms for enhanced simulation of complex human–water interactions and Nexus dynamics.
Data fusion and assimilation: Utilising ML/DL algorithms to synthesise disparate and heterogeneous data sources (satellite imagery, sensor networks, climate model outputs) for comprehensive situational awareness in water systems.
IoT in irrigation, precise irrigation, rational irrigation, and water management in agriculture.
We look forward to receiving your original research articles and review papers.

Prof. Dr. Aris Psilovikos
Prof. Dr. Mohamed Elhag
Assist. Prof. Dr. Anastasia Angelaki
Guest Editors

Special Issue in journal Water: Sustainable Water Resource Management Using Cutting-Edge Technologies

31/03/2026

Dear Colleagues,

Water shortage is a serious worldwide issue, and the demand–supply balance for water is approaching a tipping point in the majority of regions, particularly where natural resources are limited. As water shortages intensify to critical levels in most regions, the demand outpaces the supply. We still do not completely understand the natural water–soil system; therefore, adopting an eco-friendly strategy to utilize the irrigation water is most crucial, ensuring that the maximum water efficiency and crop yield are attained. Over the past few decades, in anticipation of the incoming climate change, the management of water has become a high-priority area. Timely irrigation water management, soil pollution monitoring, and physical and chemical soil status examination are fundamental for the future. Future generations require sustainable soil–water management, and other aspects such as the rising climatic crisis, soil and water pollution, soil fertility loss, etc., must be considered. As artificial intelligence and machine learning are increasingly part of everyday life, we expect their further integration into agriculture. Thus, research associated with efficient water management in agriculture, intelligent irrigation planning, soil health, efficient land use planning, improved yield through intelligent approaches, water quality, physical, chemical and hydraulic properties of soils in agriculture may provide key solutions for the future.

The main goal of this Special Issue is to collect papers (original research articles and review papers) that provide insights into the following areas: water saving in agriculture; efficient land use planning; agricultural hydraulics; smart irrigation planning; improved yield using intelligent techniques; water quality; the physical, chemical, and hydraulic properties of agricultural soils; and the application of machine learning and artificial intelligence techniques in the soil–water complex.

This Special Issue will welcome manuscripts that link the following themes:

Agricultural hydraulics; hydraulic parameters of agricultural soils; comparison with urban soils; water movement into the vadose zone; water movement in saturated and unsaturated soils; canals; flow control; and drainage systems.
Irrigation (surface irrigation, sprinkler irrigation, drip irrigation, individual and collective irrigation systems); smart irrigation systems; and automation and AI in irrigation.
Water saving in agriculture using smart irrigation scheduling, mulching, precision agriculture, and smart sensors.
Effects of soil pollution on soil structure, water holding capacity, leaching, and runoff; the impact of soil remediation on agricultural water use and sustainable irrigation.
Drift analysis, remote sensing, real-time decision making, predictive models for drift, and water saving through AI and drift control.
Land uses, land cover, soil degradation and improvement, soil health, efficient land use planning, crop rotation, and intercropping for reducing stress on water resources.
We look forward to receiving your original research articles and reviews.

Dr. Anastasia Angelaki, Assisant Professor
Dr. Parveen Sihag, Assistant Professor
Guest Editors

Special Issue in journal Land: Sustainable and AI-Driven Approaches to Managing the Soil-Water Complex in Agriculture