Irrigation Water Allocation at Farm Level Based on Temporal Cultivation-Related Data Using Meta-Heuristic Optimisation Algorithms

The present water crisis necessitates a frugal water management strategy. Deficit irrigation can be regarded as an efficient strategy for agricultural water management. Optimal allocation of water to agricultural farms is a computationally complex problem because of many factors, including limitations and constraints related to irrigation, numerous allocation states, and non-linearity and complexity of the objective function. Meta-heuristic algorithms are typically used to solve complex problems. The main objective of this study is to represent water allocation at farm level using temporal cultivation data as an optimisation problem, solve this problem using various meta-heuristic algorithms, and compare the results. The objective of the optimisation is to maximise the total income of all considered lands. The criteria of objective function value, convergence trend, robustness, runtime, and complexity of use and modelling are used to compare the algorithms. Finally, the algorithms are ranked using the technique for order of preference by similarity to ideal solution (TOPSIS). The income resulting from the allocation of water by the imperialist competitive algorithm (ICA) was 1.006, 1.084, and 1.098 times that of particle swarm optimisation (PSO), bees algorithm (BA), and genetic algorithm (GA), respectively. The ICA and PSO were superior to the other algorithms in most evaluations. According to the results of TOPSIS, the algorithms, by order of priority, are ICA PSO, BA, and GA. In addition, the experience showed that using meta-heuristic algorithms, such as ICA, results in higher income (4.747 times) and improved management of water deficit than the commonly used area-based water allocation method

Efficiency of Geographically Weighted Regression in Modeling Human Leptospirosis Based on Environmental Factors in Gilan Province, Iran

It is of little debate that Leptospirosis is verified as the most important zoonosis disease in tropical and humid regions. In North of Iran, maximum reports have been dedicated to Gilan province and it is considered as an endemic problem there. Therefore, modeling or researching about different aspects of it seems indispensable. Hence, this paper investigated various models of Geographically Weighted Regression (GWR) approach and impacts of seven environmental variables on modelling leptospirosis in Gilan. Accordingly, counts of patients were considered as dependent variable during 2009–2011 at village level and environmental variables were utilized as independent variables in the modelling. In addition, performance of two Kernels (Fixed and Adaptive), two Weighting Functions (Bisquare and Gaussian) and three Bandwidth Selection Criteria (AIC (Akaike Information Criterion), CV (Cross Validation) and BIC (Bayesian information criterion)) were compared and assessed in GWR models. Results illustrated: (1) Leptospirosis and effective variables vary locally across the study area (positive and negative); (2) Adaptive kernel in comparison to Fixed kernel, Bisquare weighting function to Gaussian, and also AIC to CV and BIC (due to R2 and Mean Square Error (MSE) validation criteria); (3) Temperature and humidity were founded as impressive factors (include higher values of coefficients); Finally, contain more reliable results consecutively. However, the provided distribution maps asserted that central villages of Gilan not only are more predisposed to leptospirosis prevalence, but also prevention programs should focus on these regions more than others

Underground Land Administration from 2D to 3D: Critical Challenges and Future Research Directions

The development and use of underground space is a necessity for most cities in response to rapid urbanisation. Effective underground land administration is critical for sustainable urban development. From a land administration perspective, the ownership extent of underground assets is essential for planning and managing underground areas. In some jurisdictions, physical structures (e.g., walls, ceilings, and utilities) are also necessary to delineate the ownership extent of underground assets. The current practice of underground land administration focuses on the ownership of underground space and mostly relies on 2D survey plans. This inefficient and fragmented 2D-based underground data management and communication results in several issues including boundary disputes, underground strikes, delays and disruptions in projects, economic losses, and urban planning issues. This study provides a review of underground land administration from three common aspects: legal, institutional, and technical. A range of important challenges have been identified based on the current research and practice. To address these challenges, the authors of this study propose a new framework for 3D underground land administration. The proposed framework outlines the future research directions to upgrade underground land administration using integrated 3D digital approaches

Managing underground legal boundaries in 3D - extending the CityGML standard

Legal boundaries are used for delineating the spatial extent of ownership property’s spaces. In underground environments, these boundaries are defined by referencing physical objects, surveying measurements, or projections. However, there is a gap in connecting and managing these boundaries and underground legal spaces, due to a lack of data model. A 3D data model supporting underground land administration (ULA) should define and model these boundaries and the relationships between them and underground ownership spaces. Prominent 3D data models can be enriched to model underground legal boundaries. This research aims to propose a new taxonomy of underground legal boundaries and model them by extending CityGML, which is a widely used 3D data model in the geospatial science domain. We developed, implemented, and tested the model for different types of underground legal boundaries. The implemented prototype showcased the potential benefits of CityGML for managing underground legal boundaries in 3D. The proposed 3D underground model can be used to address current challenges associated with communicating and managing legal boundaries in underground environments. While this data model was specifically developed for Victoria, Australia, the proposed model and approach can be used and replicated in other jurisdictions by adjusting the data requirements for underground legal boundaries

Optimized Location-Allocation of Earthquake Relief Centers Using PSO and ACO, Complemented by GIS, Clustering, and TOPSIS

After an earthquake, it is required to establish temporary relief centers in order to help the victims. Selection of proper sites for these centers has a significant effect on the processes of urban disaster management. In this paper, the location and allocation of relief centers in district 1 of Tehran are carried out using Geospatial Information System (GIS), the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) decision model, a simple clustering method and the two meta-heuristic algorithms of Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO). First, using TOPSIS, the proposed clustering method and GIS analysis tools, sites satisfying initial conditions with adequate distribution in the area are chosen. Then, the selection of proper centers and the allocation of parcels to them are modelled as a location/allocation problem, which is solved using the meta-heuristic optimization algorithms. Also, in this research, PSO and ACO are compared using different criteria. The implementation results show the general adequacy of TOPSIS, the clustering method, and the optimization algorithms. This is an appropriate approach to solve such complex site selection and allocation problems. In view of the assessment results, the PSO finds better answers, converges faster, and shows higher consistency than the ACO

Evaluation of the International 3D Geospatial Data Models and IFC Standard for Implementing an LADM-based 3D Digital Cadastre

Land Administration Domain Model (LADM) is an international standard for defining both semantic and spatial information connected with rights, restrictions, and responsibilities (RRRs) that affect land, water, built assets, natural resources, underground spaces, and airspaces. Since LADM is currently a conceptual land administration model, one of the main goals for the new version of this standard is to develop technical encodings. These technical encodings would be useful for adopting the LADM in different applications related to land administration. Therefore, the conceptual schema of LADM standard can be implemented in different and varying ways depending on the implementation requirements. The aim of this paper is to evaluate current standards used widely in the domains of geospatial information systems (GIS) and building information modelling (BIM) in terms of their capabilities to serve as an LADM-based technical encoding for 3D digital cadastre implementation. Some of these standards are CityGML, Industry Foundation Classes (IFC), IndoorGML, and LandInfra/InfraGML. There should be a specific use case for each implementation model or technical encoding. For example, a BIM-based implementation of the LADM standard can be useful for 3D digital lodgement of cadastral data when dealing with individual building and property subdivisions. LADM data encoded within a BIM model would be useful during planning, certification, and registration of a new complex subdivision, especially within built environments. In addition, LanInfra/InfraGML can provide another encoding option for 3D digital land registration. More specifically, LanfInfra/InfraGML supports surveying elements which are not well supported in IFC, CityGML and IndoorGML standards. Another option is CityGML technical encoding that can be effective for producing 3D digital property maps for an entire jurisdiction. Current property maps only depict 2D land parcels and ignore spatial and ownership dimensions of vertically placed assets, such as apartments, tunnels, subterranean retail malls, car parks, and utility networks. Developing a CityGML encoding for LADM would be considered a significant milestone towards realising 3D property maps that can provide a fully-integrated representation of underground and aboveground RRRs. Finally, IndoorGML is also another technical encoding which may not an appropriate option for 3D digital cadastre, but it can enable the use of LADM data for lawful indoor navigation. The main contribution of this study is to identify the possible technical encodings for the LADM standard and how various spatial and semantic entities within each encoding can be used to model the equivalent concepts defined in the LADM standard. This would provide guidelines for implementing the conceptual model of LADM using a specific 3D geospatial or BIM standard

Development of an LADM-based conceptual data model for 3D underground land administration in Victoria

Currently, many cities around the world use underground space for different applications such as tunnels, utility networks, parking, walkways, and shopping malls. Due to the increasing use of underground areas, management of this space is very important for decision-makers and stakeholders. A 3D Underground Land Administration (ULA) data model has an underpinning role in the management of spatial and semantic information of underground physical structures (physical aspects) as well as the ownership attributes and the extent of legal spaces in underground (legal aspects). Current data models focus on either physical or legal aspects and are mostly based on 2D approaches. The Land Administration Domain Model (LADM), as an ISO standard (ISO 19152), is a prominent legal 3D model adopted for land administration. Several studies and countries have used this data model for land administration purposes. However, LADM has not been fully implemented for modelling underground assets. In addition, it does not consider the physical aspects of underground objects. Physical structures have significant roles in defining the ownership extent of underground assets in some jurisdictions such as Victoria, Australia. On the other hand, LADM-based data models developed by different studies are based on the current requirements and legislative of different jurisdictions. Although these solutions can be helpful, a comprehensive underground data model customised for Victoria is needed. This research aims to develop an LADM-based conceptual data model for 3D ULA to enable integrated management of underground assets by interlinking legal and physical aspects. It is based on the requirements and legislative of Victoria jurisdiction. These requirements include underground legal objects and boundaries and underground physical objects. The data model developed in this study is one of the first and crucial steps to enable 3D digital management of underground rights, restrictions and responsibilities (RRRs) in Victoria