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Climate change is expected to change the frequency, duration, intensity, and pattern of precipitation, underscoring the need for accurate predictive tools. Earth system models (ESMs) serve as invaluable instruments in this endeavour, simulating climate variable variations across temporal and spatial dimensions. This study aims to develop a methodology for generating precise daily precipitation maps by rectifying biases inherent in ESM outputs. The proposed methodology includes downscaling ESM outputs to simulate historical daily grid‐based precipitation, thereby enhancing the fidelity of daily precipitation representation. For this purpose, 14 models from the Coupled Model Intercomparison Project Phase 6 (CMIP6) were employed. Random forest (RF) machine learning method was used to correct biases in these ESM outputs. This study's novelty lies in integrating results of a grid‐based RF classification model, employed to distinguish between rainy and non‐rainy days, with those obtained by two RF regression models, to estimate precipitation amounts for grid cells receiving extreme and non‐extreme precipitation, to generate an ensemble of ESM outputs. The resulting method, termed the triple coupling method (EN‐RF), was validated using precipitation data from the Divandareh‐Bijar Basin in western Iran to simulate historical climate conditions. Furthermore, the accuracy of the developed triple coupling approach was compared with that of a commonly used single machine learning‐based downscaling model (EN‐Single‐RF). Comparative analysis against a commonly used single machine learning‐based downscaling model (EN‐Single‐RF) revealed the superior performance of the EN‐RF approach in replicating the intensity and distribution of daily precipitation. Furthermore, within the triple coupling framework, support vector machine (SVM) was utilized to simulate daily historical precipitation (EN‐SVM), while the quantile mapping (QM) method served as a benchmark. Comparison of the results showed superiority of the EN‐RF to other methods (EN‐Single‐RF, EN‐SVM, and QM) in terms of various accuracy metrics (Kling‐Gupta Efficiency = 0.95, mean square error = 0.22). The findings indicated the capability of the proposed triple coupling framework using the RF algorithm to simulate the spatio‐temporal distribution of precipitation using the ESM precipitation outputs. The developed framework can be used to produce reliable projections to gain deeper insights into the potential impacts of climate change on regional precipitation patterns. [ABSTRACT FROM AUTHOR]

The dynamic nature and increasing complexity of the construction projects impose many challenges for project planning and control. As a project progresses, more information becomes available and the level of uncertainty decreases. It can be used to proactively check the validity of the previous decisions and develop revised and more detailed plans for the upcoming activities in construction planning meetings. For this purpose, this study implements ontological knowledge representation and semantic reasoning techniques to propose an intelligent information collection and decision support system framework for short-term collaborative construction and resource planning. Moreover, a new approach is suggested that allows for incorporating resource specifications and limitations, and complex multi-factor constraints in the ontological planning process. The framework was tested based on a real-world construction project and different application cases were discussed. The framework showed a promising performance for analyzing different scenarios and help the planners making informative decisions. Key words: collaborative construction planning, resource planning, ontology, semantic inference, building information modelling, decision support system. La nature dynamique et la complexite croissante des projets de construction posent de nombreux defis au niveau de la planification et du controle des projets. A mesure qu'un projet progresse, de plus amples donnees deviennent disponibles et le niveau d'incertitude diminue. Elles peuvent servir a verifier de facon proactive la validite des decisions anterieures et a elaborer des plans revises et plus detailles pour les activites a venir dans les reunions de planification de la construction. A cette fin, cette etude met en aeuvre des techniques de representation des connaissances ontologiques et de raisonnement semantique afin de proposer un cadre intelligent de collecte de donnees et de soutien a la decision pour la construction collaborative a court terme et la planification des ressources. De plus, on propose une nouvelle approche qui permet d'integrer les specifications et les limites des ressources, ainsi que les contraintes complexes multifactorielles dans le processus de planification ontologique. Le cadre a ete mis a l'essai sur la base d'un projet de construction reel et differents cas d'application ont ete discutes. Le cadre a montre une performance prometteuse pour analyser differents scenarios et aider les planificateurs a prendre des decisions en connaissance de cause. [Traduit par la Redaction] Mots-cles: planification collaborative de la construction, planification des ressources, ontologie, inference semantique, modelisation des donnees du batiment, systeme d'aide a la decision., Introduction With increasing complexity and due to the dynamic nature of construction projects, planners will need to be able to cope with a large number of uncertainties. These uncertainties should [...]

Water balance models are known as major tools for simulation of basin‐wide water cycle components. Considering the complexity of the runoff generation processes in karstic basins, it is crucial to bring the model processes closer to the physical reality of karstic springs and their contribution to runoff formation. One of the challenges in basin‐scale modelling of karstic basins is that the basin response to precipitation is highly influenced by the soil saturation condition and its seasonal variability. One way to address this challenge is to include a dynamic structure for the water balance model. In this research, the structure of SAM‐KARST water balance model for estimation of monthly streamflows in the outlet of karst basins was presented. Moreover, to better simulate the basin response fluctuations in different seasons, the SAMdyn‐KARST model with a dynamic storage structure was introduced and developed. The results of the SAMdyn‐KARST model were compared with the saturation area model (SAM) and SAM‐KARST models for the Shapur river basin in southwest of Iran. The results confirmed the importance of considering larger saturation storage in the karst basins. The case study results also showed that SAMdyn‐KARST outperformed SAM‐KARST model and provided higher accuracy in estimating seasonal variations of surface streamflows at the basin outlet. [ABSTRACT FROM AUTHOR]

In a construction project, the cost and duration of activities could change due to different uncertain variables such as weather, resource availability, etc. Resource leveling and allocation strategies also influence total time and costs of projects. In this paper, two concepts of time-cost trade-off and resource leveling and allocation have been embedded in a stochastic multiobjective optimization model which minimizes the total project time, cost, and resource moments. In the proposed time-cost-resource utilization optimization (TCRO) model, time and cost variables are considered to be fuzzy, to increase the flexibility for decision makers when using the model outputs. Application of fuzzy set theory in this study helps managers/planners to take these uncertainties into account and provide an optimal balance of time, cost, and resource utilization during the project execution. The fuzzy variables are discretized to represent different options for each activity. Nondominated sorting genetic algorithm (NSGA-II) has been used to solve the optimization problem. Results of the TCRO model for two different case studies of construction projects are presented in the paper. Total time and costs of the two case studies in the Pareto front solutions of the TCRO model cover more than 85% of the ranges of total time and costs of solutions of the biobjective time-cost optimization (TCO) model. The results show that adding the resource leveling capability to the previously developed TCO models provides more practical solutions in terms of resource allocation and utilization, which makes this research relevant to both industry practitioners and researchers. DOI: 10.1061/(ASCE)CO.1943-7862.0000092 CE Database subject headings: Optimization; Costs; Resource allocation: Stochastic processes; Fuzzy sets: Scheduling; Construction management.

This paper presents a decision support system for multipurpose reservoir operation. The mathematical models in the system are formulated for monthly operation of hydropower reservoirs. The key components of the system are four main modules: database management, inflow modeling and forecasting, operation management, and real-time operation. Flexibility is the key feature of the system, providing the users with different decision tools and different indices for measuring the performance of each tool. A cost function is developed based on the present value of the total capital cost and the cost of operation and maintenance of the system. This cost function, which is developed based on 'reasonable' estimates of water and energy prices, is used to measure the performance of reservoir operation policies. A utility function based on multicriterion decision making (MCDM) that uses an analytical hierarchy process is also developed. The MCDM utility function enables decision makers to incorporate the priority of different objectives in developing optimal operating policies and can be effectively used when the priority of objectives is not clear and the decision-making process relies mainly on the decision maker's preferences. Both economic and MCDM utility functions are implemented and coupled with deterministic and stochastic optimization models. The decision support system (DSS) is applied to the largest surface water resources system in Iran, namely, the Dez and Karoon river-reservoir system. The results of the case study have shown that the DSS has been able to significantly increase the long-term power generation of the system while satisfying water demands for different purposes. DOI: 10.1061/(ASCE)0887-3801(2005)19:2(194) CE Database subject headings: Decision support systems; Dynamic programming; Reservoir operation; Hydroelectric powerplants.

The Tehran metropolitan area is one of the mega cities of the world and has an annual domestic water consumption close to one billion cubic meters. The sewer system mainly consists of traditional absorption wells. Therefore, the return flow from the domestic consumption has been one of the main sources of groundwater recharge. Some part of this sewage is drained into local rivers and drainage channels and partially contaminates the surface runoff and local flows. These polluted surface waters are used in conjunction with groundwater for irrigation purposes in the southern part of the Tehran. In this paper, a systematic approach to surface and groundwater resources modeling in the study area, with its complex system of water supply, groundwater recharge, and discharge, is discussed. A dynamic programming optimization model is developed for conjunctive use planning. The objective function of this model is developed to supply the agricultural water demands, to reduce pumping costs, and to control groundwater table fluctuations. To develop the response function of the aquifers located in the study area, a mathematical model for simulation of the Tehran aquifer water table fluctuations has been developed and calibrated with the available data. Different scenarios are defined to study the long-term impacts of the development projects on conjunctive use policies and water table fluctuations. Comparison of the results showed how significant is the effects of an integrated approach to the surface and groundwater resources allocation in Tehran metropolitan area. The proposed model is a useful tool for irrigation planning in this region. DOI: 10.1061/(ASCE)0733-9437(2004)130:5(391) CE Database subject headings: Dynamic programming; Water quality; Water resources management; Irrigation: Case reports; Iran: Urban areas.

The application of monitoring and evaluation (M&E) systems to assess agricultural projects has received some attention in recent years. These systems provide valuable information for managers and decision makers by analyzing the implementation process, the progress of trends, and the long-term and short-term effectiveness of these projects. An algorithm is developed to monitor and evaluate drip and pressure irrigation projects in Iran. Different indicators are identified and the framework of an integrated evaluation system is demonstrated using an analytical hierarchy process for multiple-criteria-decision making. There is much subjective information that is quantified and normalized in order to remove any bias in evaluators' assessment of qualitative measures or sensitivity to linguistic expressions. The application of this system to rank projects in different regions (zones) in Iran is also presented in this paper. The results have shown the significant value of such systems in providing information and input for different decision-making levels. CE Database keywords: Monitoring; Evaluation; Irrigation systems; Fuzzy sets.