Resource Conditions Research Articles

Tax incentives in agricultural sector: indicators and performance results

The goal is to show the current state and prospects for tax incentives in agricultural sector. Methods – economic-statistical, factor analysis, comparison and synthesis, logical justification. Results – it is substantiated that direct methods of supporting certain sectors of agroindustrial complex can be effectively replaced by indirect methods due to differences in the rate of unified land tax for peasant (farm) enterprises, and for other forms of business there are differences in the corporate income tax base. In particular, direct support is compensated by tax mechanisms in agricultural formations producing crop products (the average level of profitability for 202-2022 was 49,6%), with the exception of rice cultivation, as well as in individual entrepreneurship and peasant (private) farms specializing in production of livestock products (profitability – 35,6%). An assessment of possible budget revenues is given, in conditions of full control and application of liability measures for improper or inappropriate use of agricultural land. Conclusions – calculation of the potential funding volumes that can be used to improve the land use is provided. The irrational use of agricultural land is associated with their ineffective exploitation: imperfect methods of cultivation or fertilization, improper planning of crop rotations. Regional characteristics, which cause different problems and needs that require adaptation of coordination measures and regulation should be taken into account. It is necessary to continue the work to cover the entire territory of Kazakhstan with agricultural maps to provide complete and accurate information on the condition and use of land resources, which will prevent possible violations for which fines and increased tax rates may be levied.

A MOBILE APPLICATION FOR THE AUTOMATED DIAGNOSIS OF DISEASES OF AGRICULTURAL CROPS AND THE SELECTION OF RECOMMENDATIONS FOR THEIR TREATMENT

The aim of the study was a mobile development based on computer vision technologies and site parsing, which allows automating the process of diagnosing diseases of agricultural crops and issuing recommendations for treatment. The article discusses methods for recognizing plant diseases using computer vision, describes the principles of convolutional neural networks, selects the most appropriate machine learning model based on the accuracy, speed and efficiency of the model in conditions of limited resources of a mobile device, describes the tools: libraries and frameworks used for development. The detailed architecture of the application is presented, as well as the results of the developed software are demonstrated. A new contribution to the development of this topic is the experimental substantiation of the choice of a neural network model based on the analysis of its effectiveness on a prepared dataset, as well as the introduction of an automatic search for recommendations for a certain disease of agriculture. In the future, it is planned to introduce a voice assistant into this mobile application.

Leasing in Russian agricultural economy: current stage of development

The purpose of the study is to analyze leasing sustainable development as the main investment mechanism of the Russian agrarian sector technical re-equipment. The author uses general scientific methods (analysis and synthesis, economic and statistical methods, graphical methods, etc.). The current state of the domestic agrarian sector technical equipment, the development of agricultural machinery leasing market development, issues and prospects for the use of agrarian leasing at the present stage have been considered. Considering the current situation in the domestic market, decreased prices for agricultural products and outstripping growth of prices for equipment, domestic agricultural producers need systemic state support to maintain the machinery and tractor fleet renewal pace. In conditions of agricultural machinery and investment resources for its reproduction deficit, leasing is one of the effective tools in solving the technical potential of the agricultural sector technical potential strengthening issues. The main promising areas of leasing mechanisms application, contributing to technical re-equipment acceleration in the agricultural sector have been proposed (sustainable development projects, national “green taxonomy”, “green” bonds; agricultural machinery market digitalization development; use of leasing mechanisms for the machinery and technology complexes development as a modern form of joint machinery use in agriculture). The proposed areas will ensure systemic development and acceleration of Russian agrarian sector technical re-equipment.

Composite estimation to combine spatially overlapping environmental monitoring surveys.

Long-term environmental monitoring surveys are designed to achieve a desired precision (measured by variance) of resource conditions based on natural variability information. Over time, increases in resource variability and in data use to address issues focused on small areas with limited sample sizes require bolstering of attainable precision. It is often prohibitive to do this by increasing sampling effort. In cases with spatially overlapping monitoring surveys, composite estimation offers a statistical way to obtain a precision-weighted combination of survey estimates to provide improved population estimates (more accurate) with improved precisions (lower variances). We present a composite estimator for overlapping surveys, a summary of compositing procedures, and a case study to illustrate the procedures and benefits of composite estimation. The study uses the two terrestrial monitoring surveys administered by the Bureau of Land Management (BLM) that entirely overlap. Using 2015-18 data and 13 land-health indicators, we obtained and compared survey and composite indicator estimates of percent area meeting land-health standards for sagebrush communities in Wyoming's Greater Sage-Grouse (Centrocercus urophasianus) Core and NonCore conservation areas on BLM-managed lands. We statistically assessed differences in indicator estimates between the conservation areas using composite estimates and estimates of the two surveys individually. We found composite variance to be about six to 24 units lower than 37% of the survey variances and composite estimates to differ by about six to 10 percentage points from six survey estimates. The composite improvements resulted in finding 11 indicators to statistically differ (p <0.05) between the conservation areas compared to only six and seven indicators for the individual surveys. Overall, we found composite estimation to be an efficient and useful option for improving environmental monitoring information where two surveys entirely overlap and suggest how this estimation method could be beneficial where environmental surveys partially overlap and in small area applications.

Identifying standard and simple designs of Power-to-Methanol processes: The costs of complexity reduction

First industrial-scale Power-to-Methanol plants are starting to be deployed in various geographic locations to tackle the problem of high greenhouse gas emissions of the predominantly fossil-based production of methanol. With the aim to speed up the deployment by streamlining their engineering and construction, we explore the potential of reducing the complexity of designs to be distributed across locations with different renewable energy conditions. A multi-objective optimization-based method incorporating a broad process network for early-stage process synthesis is proposed, which by determining the installed capacities of technologies from the chemical production, utility and storage subsystems, identifies alternative designs with different levels of complexity along two dimensions: 1) the number of different technologies used, 2) standardization of designs across different locations. The method was applied to case studies, which paired together design locations with either wind- or solar-dominant renewable resource conditions in the US and Chile for standardization. As per the method, the increases of methanol production costs due to reduced design flexibility, inherently bound to complexity reduction, were quantified and Pareto fronts were constructed. These uncovered the possibility to significantly reduce the complexity of the designs with only small increases of the production costs. By comparing the results of the case studies under different cost and operation scenarios, we characterized general aspects, which need to be considered for such design simplification. One of the main outcomes were the quantified cost-increases due to standardization, which were around 7 and 15 % relative to the specifically designed plants for each location in the US and Chile case studies respectively. A subsequent analysis of the economies of numbers through learning rates reported in academic literature suggested that the proposed standardization, even across extremely different locations, could compensate these cost-increases and be economically beneficial. Yet, more specific data on achievable cost-reductions are needed, requiring more interaction with the industry and further research, for which we highlighted promising directions.

Reservoir Computing Using Measurement-Controlled Quantum Dynamics

Physical reservoir computing (RC) is a machine learning algorithm that employs the dynamics of a physical system to forecast highly nonlinear and chaotic phenomena. In this paper, we introduce a quantum RC system that employs the dynamics of a probed atom in a cavity. The atom experiences coherent driving at a particular rate, leading to a measurement-controlled quantum evolution. The proposed quantum reservoir can make fast and reliable forecasts using a small number of artificial neurons compared with the traditional RC algorithm. We theoretically validate the operation of the reservoir, demonstrating its potential to be used in error-tolerant applications, where approximate computing approaches may be used to make feasible forecasts in conditions of limited computational and energy resources.

The modern concept of improving the legal regulation of social security in Ukraine in the context of the development of the concept of civil society

This article focuses on developing the content of a contemporary, concept and strategic act aimed at enhancing the legal regulation of social security in Ukraine, contextualized within the evolution of the concept of civil society. The author advocates for the creation and endorsement of a Concept to facilitate the participation of civil society actors in bolstering the social security of modern Ukraine. Structurally, this Concept is composed of six parts. The first, ‘Problems that Require Solutions,' highlights issues such as the subpar quality of current legislation's legal regulation concerning the activities of civil society entities, leading to a legal vacuum in the legal framework and regulation of social security involving these entities. The second part, ‘Value Guidelines, Goals, and Objectives for the Implementation of this Concept,' delineates the foundational principles of the social security legal regulation mechanism in the context of civil society's development. Furthermore, it identifies the establishment of institutional, regulatory, and other resource conditions as a goal, enabling effective civil society participation in legally regulating conditions for a socially safe human existence in Ukraine.&#x0D; The third section of the Concept, 'Time Frame for the Implementation of this Concept,’ specifies that while the Concept is valid indefinitely, the resolution of key issues addressed therein should be accomplished before 2026. The fourth part, 'Methods and Approaches for Problem,’ includes strategies such as enhancing current legislation on the legal regulation of civil society actors’ participation in legal processes, especially in social security regulation; the development of a Strategy for Social Security Assurance by Civil Society Actors and its corresponding implementation program, among others. The fifth and sixth parts, 'Anticipated Outcomes’ and 'Estimation of Financial, Material, Technical, and Human Resources,’ respectively, complete the Concept. The article’s conclusion summarizes the research findings and identifies a list of regulations that should be developed and enacted as part of the Concept’s implementation. These include proposed legislative drafts such as the Laws of Ukraine 'On Social Security,’ 'On the Activities of Civil Society Entities in Ukraine,’ and 'On Public Lobbying Activities.’

Variation in root traits and phenotypic plasticity between native and introduced populations of the invasive plant Chromolaena odorata

Understanding intraspecific trait variations, particularly for invasive species that occupy large geographic areas with different resource conditions, can enhance our understanding of plant responses to changes in environmental resources. However, most related studies have focused on aboveground traits, while variations in root traits and responses to changes in resources during biological invasion have not been clarified. To fill this knowledge gap, we compared the root traits of Chromolaena odorata from 10 introduced populations in Southeast Asia and 12 native populations in North and Central America under different soil nutrients. The introduced populations of the invader exhibited greater resource-acquisitive root traits, characterized by reduced fine root diameter but increased proportions of absorbing root length and specific root length, compared to the native populations. Although nutrient addition significantly affected root traits, the introduced populations showed greater phenotypic plasticity in four traits (root / shoot ratio, specific root length, absorbing root length proportion, and branching intensity) than the native populations. Different root trait syndromes were observed between the introduced and native populations. These results indicate that after introduction, C. odorata may shift towards a more soil resource-acquisitive strategy and thus respond more positively to increased soils nutrients, thereby showing better performance in high-resource environments. This study provides a better understanding of how species respond to environment changes and reveals the factors underlying exotic plant invasion success.

ACHIEVEMENTS AND PROSPECTS OF DECENTRALIZATION REFORMS IN THE CARPATHIAN REGION

The article analyzes the state of state-building, particularly decentralization in Ukraine and the Carpathian region, as the basis for forming self-sufficient public authorities through the amalgamation of territorial communities and fundamental changes in the principles of forming their resources, including financial potential. The article highlights current theoretical and practical problems of state-building, decentralization, regulation of socio-economic and budgetary processes, and their improvement for the significant enhancement of providing public services to the population and engaging it in state-building processes. It is emphasized that the reform of decentralization and administrative-territorial structure is crucial in building a state, allowing the construction of a democratic governance system, and creating resource conditions for the development of civil society.&#x0D; The implementation of the decentralization policy in Ukraine is an effective factor in stabilizing the socio-economic situation, overcoming the financial crisis, overcoming contradictions between different levels of government, organizing relations between the center, regions and territorial communities on the principles of distribution of responsibilities and competencies, as well as partnership in providing Ukrainian citizens with state and local benefits, and will help to increase the efficiency of using budget funds at all levels of government.

ANALISIS GAP BIDANG SUMBER DAYA MANUSIA (SDM) RUMAH SAKIT PKU MUHAMMADIYAH YOGYAKARTA UNIT II MENUJU RS TIPE B

Background : Issues of ASEAN Free Trade Area ( AFTA ) and globalization of the world economy demands which is every profit and non-profit organizations to compete in marketing and resource. The changes, requires hospitals to survive among competitors that would appear at any moment. Therefore , Human Resources (HR) hospitals must be able to respond it. HR needs proper planning in accordance with the service requirements of each unit in accordance with the classification of hospitals based on the Regulation of the Minister of Health ( Permenkes ) number 56 of 2014 as well as national accreditation standard version of the Commission on Accreditation of Hospitals 2012 ( KARS ). So the RS PKU Muhammadiyah Yogyakarta Unit II will head the hospital type B and accreditation .Methods: The study design was qualitative used in-depth interviews and quantitative method for analyzing a plan about between condition HR today and the need for hospital accreditation of type B and KARS version 2012. While quantitative data obtained through questionnaires Need Assessment journal guidelines on Managerial Competency Development at Provincial and District HospitalsResults : Based on the research results, there is a shortage of human resources about the type and number according to hospital type B and for the accreditation of 99 elements document ratings are 96 % complete . As for performance assessment PKU Muhammadiyah Yogyakarta Unit II responsibility for the examination and treatment, training, science and research, issues of hospital management which is indispensable. To support the referral level, in terms of both treatment and prevention. As well as for international cooperation needs to be improved. While the assessment of training needs at the level of priority on the entire item, respondents give a high priority level assessment.Conclusion : There is a shortage of the type and quantity of human resources between the condition of human resources today and the provisions of hospital type B and there are some completeness of documents for the accreditation of qualifications assessment and education staff KARS 2012 version. Keywords : hospital type B, Permenkes 56/2014, accreditation of KARS version 2012, human resources.

DE3TC: Detecting Events with Effective Event Type Information and Context

Event Detection (ED) is a crucial information extraction task that aims to identify the event triggers and classify them into predefined event types. However, most existing methods did not perform well when processing events with implicit triggers. And most methods considered ED as a sentence-level task, lacking effective context for event semantics. Moreover, how to maintain good performance under low resource conditions still needs further study. To address these problems, we propose a novel end-to-end ED model called DE3TC, which Detects Events with Effective Event Type Information and Context. We construct an event type-specific Clue to capture the interaction between event type name and trigger words, providing event type information for implicit triggers. For accessing the effective context of event semantics for sentence-level ED, we consider the correlations between types and select similar types’ descriptions as context. With contextualized representation from a contextual encoder, DE3TC learns the event type information for all events including implicit ones. And it performs sentence-level ED efficiently with effective contexts. The empirical results on ACE 2005 and MAVEN datasets show that: (i) DE3TC obtains state-of-the-art performance compared with previous methods. (ii) DE3TC is also excelled under low-resource conditions.

Dynamic Load Balancing in Cloud Computing: Optimized RL-Based Clustering with Multi-Objective Optimized Task Scheduling

Dynamic load balancing in cloud computing is crucial for efficiently distributing workloads across available resources, ensuring optimal performance. This research introduces a novel dynamic load-balancing approach that leverages a deep learning model combining Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) to calculate load values for each virtual machine (VM). The methodology aims to enhance cloud performance by optimizing task scheduling and stress distribution. The proposed model employs a dynamic clustering mechanism based on computed loads to categorize VMs into overloaded and underloaded clusters. To improve clustering efficiency, the approach integrates Reinforcement Learning (RL) with a sophisticated Hybrid Lyrebird Falcon Optimization (HLFO) algorithm. HLFO merges the Lyrebird Optimization Algorithm (LOA) and Falcon Optimization Algorithm (FOA), enhancing the effectiveness of load balancing. A Multi-Objective Hybrid Optimization model is introduced to optimize task scheduling while considering Quality of Service (QoS) parameters, including makespan minimization, energy consumption reduction, balanced CPU utilization, efficient memory usage, and task prioritization. The implementation, conducted in Python and CloudSim, demonstrates the model’s ability to effectively allocate work between virtual machines (VMs) and physical machines (PMs), resulting in improved resource utilization, shortened makespan, enhanced CPU usage, and rigorous assessments affirming its efficacy. This research addresses the complexity of dynamic load balancing in cloud environments by combining deep learning, reinforcement learning, and hybrid optimization techniques, offering a comprehensive solution to optimize cloud performance under varying workloads and resource conditions.

Evaluation of an Indoor Location System Using Edge Computing and Machine Learning Algorithms

Evaluation of an Indoor Location System Using Edge Computing and Machine Learning Algorithms

Priorities of Russia's social policy in the context of global social changes

The relevance of the topic chosen by the authors is determined by the very fact of global changes in the established social order taking place in the world, which create serious risks for the "welfare state" formed in the West after the Second World War. There is a transformation of the basic principles of the modern welfare state – the principles of social solidarity and social justice, the external and internal conditions for the modern state to perform its social functions are becoming more complicated. The ongoing global social transformations fully affect Russian society, especially since it was Russia that acted as their catalyst. Their socio–economic consequences for Russia are ambiguous. The purpose of this work is to analyze the identification of priorities of social policy in modern Russia in conditions associated with both risks and new opportunities in the context of global social changes. The main methodological basis of the research is theoretical scientific methods and a systematic approach. The interdisciplinary nature of the research within the framework of the identified issues requires the involvement of both the results of sociological research and a significant amount of data from domestic and international economic statistics. It is emphasized that in the field of social policy, which is initially based on the principles of redistribution of resources through the budget system, there are no solutions that provide all Russians with prospects for economic and social well-being. Given the complexity of developing universal government solutions capable of ensuring the realization of the social interests of all population groups, in order to preserve the solidarity of Russians in conditions of instability and resource constraints, it is necessary to identify the right priorities of social policy. Accordingly, the authors analyzed the main problems and prospects in the implementation of the main priorities of the social policy of the modern Russian state: overcoming inequality and poverty, support for the participants of the SVO and their families, family and demographic policy. The authors conclude that when developing and implementing social measures, the Russian state should avoid inflating "bubbles" of social obligations. In addition, it is necessary to keep in mind the limited budget resources and avoid "life on loan". Consequently, the authors substantiate the position on the need to implement the principles of rationality, targeting, and orientation towards solving the most acute social problems of modern Russian society in conditions of limited resources.

Is 1 Day of Focused Training in Echocardiographic Assessment Using Subxiphoid-Only (EASy) Examination Enough? A Tertiary Hospital Response to the COVID-19 Crisis and the Use of the EASy Examination to Support Unit-Wide Image Acquisition.

We assessed the efficacy of 1-day training in echocardiography assessment using subxiphoid-only (EASy) followed by supervised image interpretation and decision-making during patient rounds as a novel approach to scaling up the use of point-of-care ultrasound (POCUS) in critically ill patients. Retrospective analysis of medical records and EASy examination images. Tertiary care academic hospital. A total of 14 adults (> 18 yr old) with COVID-19-associated respiratory failure under the care of Albany Medical Center's surge response team from April 6-17, 2020 who received at least one EASy examination. Residents (previously novice sonographers) were trained in EASy examination using 1 day of didactic and hands-on training, followed by independent image acquisition and supervised image interpretation, identification of hemodynamic patterns, and clinical decision-making facilitated by an echocardiography-certified physician during daily rounds. We recorded the quality of resident-obtained EASy images, scanning time, and frequency with which the supervising physician had to repeat the examination or obtain additional images. A total of 63 EASy examinations were performed; average scanning time was 4.3 minutes. Resident-obtained images were sufficient for clinical decision-making on 55 occasions (87%), in the remaining 8 (13%) the supervising physician obtained further images. EASy examination is an efficient, valuable tool under conditions of scarce resources. The educational model of 1-day training followed by supervised image interpretation and decision-making allows rapid expansion of the pool of sonographers and implementation of bedside echocardiography into routine ICU patient management.

Grain industrial agglomeration and grain green total factor productivity in China: A dynamic spatial durbin econometric analysis

Appropriate industrial agglomeration has several benefits, including reducing environmental pollution, promoting innovation and enhancing grain green total factor productivity (GTFP). It is an effective strategy for promoting high-quality and sustainable development in the grain industry. In this study, the slacks-based measure of the global Malmquist–Luenberger (SBM-GML) model is used to estimate China's grain GTFP using the panel data of 31 provinces from 2001 to 2020. Furthermore, a dynamic spatial econometric model is employed to empirically investigate the impact of grain industry agglomeration on grain GTFP and its regional heterogeneity. The results show that GTFP exhibits a fluctuating growth tendency, with advancements in green technology serving as the primary engine of that expansion. (2) The deepening of grain industrial agglomeration has a long-term promoting effect on the grain GTFP of local and neighbouring areas, with the long-term effect being more significant than the short-term effect, as revealed by the dynamic spatial Durbin model. (3) According to the heterogeneity analysis, industrial agglomeration's impact on grain GTFP is most noticeable in the production and sales balance. Therefore, encouraging regional cooperation and communication while raising the grain industrial agglomeration standard is crucial. The degree of regional economic growth, the state of the agricultural infrastructure and the conditions of the natural resources should all be considered by policymakers when developing distinct and focused policy assistance for each region.

Quantitative methods for integrating climate adaptation strategies into spatial decision support models

With the onset of rapid climate change and the legacy of past forest management and fire suppression policies, the capacity for forested landscapes to maintain core functionality and processes is being challenged. As such, managers are tasked with increasing the pace and scale of management to mitigate negative impacts of future large disturbances and improve resilience and climate adaptation of large landscapes. Such efforts require consensus building, with partners and stakeholders to determine where to allocate scarce resources. We present a methodology to identify strategic (where to go) and tactical (what to do) priorities across large landscapes to assist in project level planning. The model integrates a spatial assessment of current ecosystem resource conditions and spatial outputs from a landscape succession and disturbance simulation model (LANDIS-II) to assess the potential to achieve desired conditions under climate change with ongoing disturbances. Based on the expected trajectory of landscape conditions over time, the model applies fuzzy logic modeling to provide quantitative support for four management strategies (Monitor, Protect, Adapt, and Transform) across the landscape. We provide an example application of these methods targeting sustainable carbon loads across a 970,000 ha landscape in the central Sierras in California. By including future landscape conditions in the model, decisions made at the stand-level are inherently tied to and influenced by larger landscape-level processes that are likely to have the greatest impact on future landscape dynamics. The methods outlined here are able to incorporate multiple metrics to capture the many resources targeted by management. Model outputs could also be used as inputs into spatial optimization models to assess tradeoffs and synergies among treatment options and to aid in long-term planning.

Internal control as a tool for identifying in-house reserves

Subject. This article considers on-farm reserves as an effective tool for improving the performance of economic entities, helping look for additional opportunities to achieve their goals in conditions of insufficient resources and investments in the development of production processes. Objectives. The article aims to describe the importance of internal reserves in the activities of companies and develop a way to most effectively identify and implement them using internal control. Methods. For the study, we used the methods of modeling, induction, deduction, and logical analysis. Results. The article offers an author-developed definition of the term On-Farm Reserve, as well as a set of internal control tools, which is a set of pairs of control procedures aimed at identifying a potential reserve and assessing its effectiveness. Relevance. The practical significance of the study lies in the proposed tools, namely a set of control procedures that help minimize the risks of failure to achieve the goals of an economic entity as a result of the use of inefficient savings. The results of the article can be used for the organization of internal control of companies, as well as for further scientific developments and practical application.

Visitor Perception on Urban Green Space and its Contribution for Human Well-being, A Case Study for Henarathgoda Botanic Garden

&#x0D; &#x0D; &#x0D; Botanical gardens vary widely in design and purpose, but most are typically associated with Ex-situ conservation, education, or historical interpretation. Yet, few studies have shown that botanic garden visitors are often motivated by recreational and leisure interests. While visitor motivations and benefits have been the focus of a number of botanical garden and green space visitor studies. The main objective of the study was to investigate the perceptions of benefits for wellbeing of community, by visiting Henarathgoda Botanic Garden (HBG) and the Specific objectives were; to identify the range of different specialized visitors to the HBG, identify the activity patterns of community visitation associated with HBG and to examine visitor preferences/perceptions for various combinations of social, resource and management conditions, and study the different wellbeing benefits obtained by visiting the garden related to physical and aesthetics aspects. This study employed means-end theory to investigate the links visitors make between garden attributes, consequences, and values. Structured questionnaire based in-person interviews were conducted with garden visitors (n=200) and surveys took place representing whatsoever the community visiting the HBG during February to March 2022. The overall study resulted that the garden visitation has been drastically affected by COVID-19 pandemic situation before, during, and after the pandemic respectively. This study reveals that the main top two reasons for visiting the HBG of respondent were because of relaxation and pleasure. This shows HBG should provide a suitable place for recreational activities for visitors. Further, the highest percentage of the respondents (36%) were commented that HBG‘s management should consider improving the services providing and maintaining more facilities and need for the visitors. The most positive benefit wanted to gain responders by visiting HBG, which was understanding and enjoying nature in different garden sections established at HBG (42%).&#x0D; Keywords: Urban green spaces, Well-being benefits, Botanic garden, Psychological benefits, Questionnaire survey&#x0D; &#x0D; &#x0D;

Current Condition of Water Resources and Their Saving

The article presents analytical data on water resources in Uzbekistan and other countries. Based on the system of artificial intelligence, opinions were expressed about the preservation of water resources and the ecosystem in it. Considerations on the wise use of water resources using artificial intelligence, deterministic - stochastic models are presented.