Scheduling Tool Research Articles

Data-Driven Approach to Grade Change Scheduling Optimization in a Paper Machine

This paper proposes an efficient decision support tool for the optimal production scheduling of a variety of paper grades in a paper machine. The tool is based on a continuous-time scheduling model and generalized disjunctive programming. As the full-space scheduling model corresponds to a large-scale mixed integer linear programming model, we apply data analytics techniques to reduce the size of the decision space, which has a profound impact on the computational efficiency of the model and enables us to support the solution of large-scale problems. The data-driven model is based on an automated method of identifying the forbidden and recommended paper grade sequences, as well as the changeover durations between two paper grades. The results from a real industrial case study show that the data-driven model leads to good results in terms of both solution quality and CPU time in comparison to the full-space model.

Branchless Banking an Effective Device Towards Financial Inclusion: A Study Conducted at Bangalore Rural District

In order to achieve greater financial inclusion in India, and at the same time to bridge the gap between demand and supply of financial service delivery in unbanked areas the Reserve Bank of India, based on the recommendations of the Khan Committee report proposed Business Correspondent model in the year 2006. The present study was conducted to know the awareness level of the services provided by business correspondent model among the rural poor and the challenges faced while availing those services. Using random sampling method data was collected from 315 respondents availing the services of business correspondent model from all four blocks of Bangalore rural district. The primary data was collected using interview schedule and statistical tools like simple percentage and Friedman rank test were used for analysing the primary data. Most of the respondents were aware of the services provided by BC’s was the major finding of the study.

Impact of Energy Storage Useful Life on Intelligent Microgrid Scheduling

Planning the operation scheduling with optimization heuristic algorithms allows microgrids to have a convenient tool. The developments done in this study attain this scheduling taking into account the impact of energy storage useful life in the microgrid operation. The scheduling solutions, proposed for the answer of an optimization problem, are obtained by using a metaheuristic algorithm called Differential Evolutionary Particle Swarm Optimization (DEEPSO). Thanks to the optimization that is conducted in this study, it is possible to formulate dispatches of the existent microgrid (MG) by always looking for the ideal dispatch that implies a lower cost and provides a greater viability to any project related to renewable energy, electric vehicles and energy storage. These advances oblige the battery manufacturers to start looking for more powerful batteries, with lower costs and longer useful life. In this way, this paper proposes a scheduling tool considering the energy storage useful life.

Preparing for CBME: How often are faculty observing residents?

The Royal College of Physicians and Surgeons of Canada officially launched 'Competence by Design' in July 2017, moving from time-based to outcomes-based training. Transitioning to competency-based medical education (CBME) necessitates change in resident assessment. A greater frequency of resident observation will likely be required to adequately assess whether entrustable professional activities have been achieved. Characterize faculty and resident experiences of direct observation in a single paediatric residency program, pre-CBME implementation. Qualitatively describe participants' perceived barriers and incentives to participating in direct observation. Surveys were sent to paediatric residents and faculty asking for demographics, the frequency of resident observation during an average 4-week rotation, perceived ideal frequency of observation, and factors influencing observation frequency. Descriptive data were analyzed. Institutional research ethics board approval was received. The response rate was 54% (34/68 faculty and 16/25 residents). When asked the MAXIMUM frequency FACULTY observed a resident take a history, perform a physical examination, or deliver a plan, the median faculty reply was 1, 2, and 3, for outpatient settings and 0, 1, and 2, for inpatient settings. The median RESIDENT reply was 2, 4, and 10 for outpatient settings and 1, 2, and 20 for inpatient settings. When asked the MINIMUM frequency for each domain, the median FACULTY and RESIDENT reply was 0, except for delivering a plan in the inpatient setting. Faculty reported observing seniors delivering the plan more frequently than junior residents. Faculty and resident median replies for how frequently residents should be observed for each domain were the same, three to four, three to four, and five to six times. Four per cent of faculty reported regularly scheduling observations, and 77% of residents regularly ask to be observed. The most common barriers to observation were too many patients to see and both faculty and residents were seeing patients at the same time. Most faculty and resident responders felt that observation frequency could be improved if scheduled at the start of the rotation; faculty were provided a better tool for assessment; and if residents asked to be observed. This study provides baseline data on how infrequent faculty observation is occurring and at a frequency lower than what faculty and residents feel is necessary. The time needed for observation competes with clinical service demands, but better scheduling strategies and assessment tools may help.

Floating photovoltaic plants: Ecological impacts versus hydropower operation flexibility

Floating photovoltaic power plants are a quickly growing technology in which the solar modules float on water bodies instead of being mounted on the ground. This provides an advantage, especially in regions with limited space. Floating modules have other benefits when compared to conventional solar power plants, such as reducing the evaporation losses of the water body and operating at a higher efficiency because the water reduces the temperature (of the modules). So far, the literature has focused on these aspects as well as the optimal design of such solar power plants. This study contributes to the body of knowledge by i) assessing the impact of floating solar photovoltaic modules on the water quality of a hydropower reservoir, more specifically on the development of algal blooms, and by ii) studying the impact that these modules have on the hydropower production. For the first part, a three-dimensional numerical-hydrodynamic water-quality model is used. The current case (without solar modules) is compared to scenarios in which the solar modules increasingly cover the lake, thus reducing the incident sunlight from 0% to finally 100%. The focus is on microalgal growth by monitoring total chlorophyll-a as a proxy for biomass. For the second part, as the massive installation of solar modules on a reservoir may constrain the minimum water level (to avoid the stranding of the structures), the impact on hydropower revenues is examined. Here, a tool for optimal hydropower scheduling is employed, considering both different water and power price scenarios. The Rapel reservoir in central Chile serves as a case study. The response of the system strongly depends on the percentage that the modules cover the lake: for fractions below 40%, the modules have little or no effect on both microalgal growth and hydropower revenue. For moderate covers (40–60%), algal blooms are avoided because of the reduction of light in the reservoir (which controls algal growth), without major economic hydropower losses. Finally, a large solar module cover can eradicate algal blooms entirely (which might have other impacts on the ecosystem health) and results in severe economic hydropower losses. Altogether, an optimum range of solar module covers is identified, presenting a convenient trade-off between ecology health and costs. However, a massive deployment of these floating modules may affect the development of touristic activities in the reservoir, which should be examined more closely. In general, the findings herein are relevant for decision-makers from both the energy sector and water management.

Schedule-based Family-centered Rounds: A Novel Approach to Achieve High Nursing Attendance and Participation.

Bedside nurse (RN) presence during family-centered rounds (FCR) enhances communication and collaboration for safer, higher-quality care.1-3 At our institution, RN participation in FCR was variable and lower than desired. The content discussed at each bedside during rounds was inconsistent, contributing to the irregular achievement of established FCR checklist items. Using a scheduling tool with a prioritization algorithm and set time allotment/patient, we implemented schedule-based family-centered rounds (SBFCR) on a pediatric acute care unit. Primary outcome metrics included RN attendance and participation. We tracked rounding checklist compliance, parent presence on rounds, and adherence to the schedule. Surveys provided information on provider and family satisfaction. Perceived impact on teaching was the balancing measure because the structure discouraged spending extra time at the cost of team tardiness for the next patient. We created a schedule for 95% workweek days, with the rounding order kept for 93%. Mean RN attendance increased from 69% to 87% and participation increased from 48% to 80% with SBFCR (P < 0.001 for each). FCR checklist compliance increased from 60% to 94% (P < 0.001). Families felt more informed and able to attend; their presence at rounds rose from 66% to 85% (P < 0.001). Most faculty and trainees felt SBFCR was efficient and observed increased teaching with SBFCR. SBFCR provides an organizational framework for increased RN attendance and participation as well as greater family presence during rounds. The system elevated provider satisfaction with rounding without degrading the perceived educational experience.

Evaluation of Two Recurrent Neural Network Methods for Prediction of Irrigation Rate and Timing

HighlightsNARX and LSTM recurrent neural networks were evaluated for prediction of irrigation prescriptions.LSTM neural networks presented the best performance for irrigation scheduling using soil matric potential sensors.NARX neural networks had the best performance for predicting irrigation prescriptions using weather data.High performance for several time-ahead predictions using both recurrent neural networks, with R2 &amp;gt; 0.94.The results can be adopted as a decision-support tool in irrigation scheduling for fields with different types of soils.Abstract. The implementation of adequate irrigation strategies could be done through real-time monitoring of soil water status at several soil depths; however, this could also represent a complex nonlinear problem due to the plant-soil-weather relationships. In this study, two recurrent neural network (RNN) models were evaluated to estimate irrigation prescriptions. Data for this study were collected from an on-farm corn irrigation study conducted between 2017 and 2019 in Samson, Alabama. The study used hourly data of weather and soil matric potential (SMP) monitored at three soil depths from 13 sensor probes installed on a loamy fine sand soil and a sandy clay loam soil. Two neural network methods, i.e., a nonlinear autoregressive with exogenous (NARX) input system and long short-term memory (LSTM), were trained, validated, and tested with a maximum dataset of 20,052 records and a maximum of eight categorical attributes to estimate one-step irrigation prescriptions. The performance of both methods was evaluated by varying the model development parameters (neurons or blocks, dropout, and epochs) and determining their impact on the final model prediction. Results showed that both RNN models demonstrated good capability in the prediction of irrigation prescriptions for the soil types studied, with a coefficient of determination (R2) &amp;gt; 0.94 and root mean square error (RMSE) &amp;lt; 1.2 mm. The results of this study indicate that after training the RNNs using the dataset collected in the field, models using only SMP sensors at three soil depths obtained the best performance, followed by models that used only data of solar radiation, temperature, and relative humidity in the prediction of irrigation prescriptions. For future applicability, the RNN models can be extended using datasets from other places for training, which would allow the adoption of a unique data-driven soil moisture model for irrigation scheduling useful in a wide range of soil types. Keywords: Corn, Irrigation scheduling, Machine learning, Modeling, Soil matric potential sensor.

An overview of different approaches in hydrogen network optimization via mathematical programming

Goal: Hydrogen has shown increasing demand in oil refineries, due to the importance of its use as a sulfur capture element. As different oils and products require different amounts of hydrogen, their use optimally is an essential tool for refinery production scheduling. A comparison was made between the different approaches used in optimization via mathematical programming.&#x0D; Design / Methodology / Approach: One of the most used methods for hydrogen network optimization is through mathematical programming. Linear and non-linear models are discussed, positive aspects of each formulation and different initialization techniques for non-linear modeling were considered.&#x0D; Results: The optimization through the linear model was more satisfactory, taking into account the payback of the new proposed design, combined with the use of compressor rearrangement, which reduces the investment cost.&#x0D; Limitations of the investigation: The objective function chosen is based on the operational cost, but another approach to be considered would be the total annual cost. In addition, the parameters related to costs are obtained from the literature and may change over the years.&#x0D; Practical implications: The proposal is to discuss the main aspects of each model, showing which models more robust and easier to converge are capable of providing competitive results. Also, different initialization techniques that can be used in future works.&#x0D; Originality / Value: The main contribution is the relationship between hydrogen management and production scheduling and for that, a discussion is made about possible formulations. Linear model is sufficient to optimize the problem, due to its main characteristics discussed.

SMART CRITICAL PATH METHOD AS A MODIFIED DETAILED SCHEDULING TECHNIQUE

Now more Construction managers depends on scheduling tools based Critical Path Method (CPM) to can obtain total project duration plus activities’ float that leads to more efficiency for controlling and planning the studied construction projects. CPM was the best tool for construction projects time schedule but nowadays researchers were focused on drawbacks that loose its use as a decision making technique. This paper shows most of CPM drawbacks and discusses a modified tool for CPM model that is named Critical Path Segments (CPS). This CPS scheduling technique solves CPM drawbacks by three main ways: (1) Dividing duration of project activity into separated time segments; (2) All activities relationships were switched into sequential relation as finish to start and (3) Calculations and analysis were done by using forward path. Sample cases of studies were included to focus on CPM shortages and CPS detailed calculations and analysis were illustrated showing this CPS technique can handle the schedules by best way.

Comparison of Lysimeter-Derived Crop Coefficients for Legacy and Modern Drought-Tolerant Maize Hybrids in the Texas High Plains

HighlightsDaily maximum crop coefficient (Kc) values were similar for legacy hybrids and a modern drought-tolerant (DT) hybrid.Later planting dates for a DT hybrid resulted in average season lengths ~25 days shorter than those of legacy hybrids.Results illustrated the effects of environment, planting date, interannual variation in temperature, and the importance of climate-specific Kc functions.DT hybrids may be more effective at reproductive growth during periods of heat stress in semi-arid environments, although additional data are needed to support this conclusion.Abstract. Corn (Zea mays L.) is a major irrigated crop grown in the Southern High Plains including the Texas Panhandle. Irrigation from the Ogallala Aquifer is required to sustain profitable corn production in the region by supplementing inadequate and erratic rainfall. Effective irrigation scheduling works to extend limited groundwater resources by avoiding water losses associated with runoff and/or percolation below the root zone. The use of crop coefficient (Kc) and reference evapotranspiration (ETo) values to estimate daily crop water use (ETc) remains an effective scheduling tool that can complement other irrigation scheduling approaches. Both Food and Agriculture Organization (FAO-56) piecewise and curvilinear Kc values for corn are found in the literature. However, advances in corn genetics have led to questions about the applicability of Kc values developed using legacy corn hybrids to irrigation of modern drought-tolerant (DT) hybrids. Lysimeter-derived Kc values for legacy corn hybrids grown in large weighing lysimeter fields at the USDA-ARS Conservation and Production Research Laboratory at Bushland, Texas, were compared with those derived from a modern DT corn hybrid recently grown in the same fields. Results indicated that although midseason daily Kc values were similar for all hybrids, average season length was ~25 days shorter for the modern DT hybrid, characterized by a shortened initial growth period followed by more rapid increase of Kc during the development period. However, plots of Kc over thermal time illustrated that the differences in season length were likely attributable to later planting dates associated with the DT corn hybrids. Average seasonal water use was 730 and 811 mm for the legacy and modern DT hybrids, respectively (three years each), with corresponding average yields of 1.2 and 1.4 kg ha-1. Results suggest that published Kc and Kcb values developed with legacy corn hybrids remain largely applicable to modern DT corn hybrids when used with accurate estimates of effective canopy-based growth stages and climate-specific Kc functions. Keywords: Crop coefficients, Drought-tolerant, Evapotranspiration, Maize, Weighing lysimeters.

A Decade of Unmanned Aerial Systems in Irrigated Agriculture in the Western U.S.

Highlights Unmanned aerial systems (UAS) are able to provide data for precision irrigation management. Improvements are needed regarding UAS platforms, sensors, processing software, and regulations. Integration of multi-scale imagery into scientific irrigation scheduling tools are needed for technology adoption. Abstract . Several research institutes, laboratories, academic programs, and service companies around the United States have been developing programs to utilize small unmanned aerial systems (sUAS) as an instrument to improve the efficiency of in-field water and agronomical management. This article describes a decade of efforts on research and development efforts focused on UAS technologies and methodologies developed for irrigation management, including the evolution of aircraft and sensors in contrast to data from satellites. Federal Aviation Administration (FAA) regulations for UAS operation in agriculture have been synthesized along with proposed modifications to enhance UAS contributions to irrigated agriculture. Although it is feasible to use sUAS technology to produce maps of actual crop coefficients, actual crop evapotranspiration, and soil water deficits, for irrigation management, the technology and regulations need to evolve further to facilitate a successful wide adoption and application. Improvements and standards are needed in terms of cameras’ spectral (bands) ranges, radiometric resolutions and associated calibrations, fuel/power technology for longer missions, better imagery processing software, and easier FAA approval of higher altitudes flight missions among other issues. Furthermore, the sUAS technology would play a larger role in irrigated agriculture when integrating multi-scale data (sUAS, ground-based or proximal, satellite) and soil water sensors is addressed, including the need for advances on processing large amounts of data from multiple and different sources, and integration into scientific irrigation scheduling (SIS) systems for convenience of decision making. Desirable technological innovations, and features of the next generation of UAS platforms, sensors, software, and methods for irrigated agriculture, are discussed. Keywords: Agricultural water management, Irrigation prescription mapping, Irrigation scheduling, Precision irrigation, Remote sensing, Sensors, Spatial crop evaOotranspiration, Unmanned aerial systems.

Comparison of Sensor-Based and Weather-Based Irrigation Scheduling

HighlightsSensor-based irrigation scheduling methods (SBISM) were compared with computerized water balance scheduling.Number and time of irrigation events scheduled using the SBISM were often different from those predicted by the computerized method.The highly variable soils at the Missouri site complicated interpretation of the sensor values.Both SBISM and computerized water balance scheduling could be used for irrigation scheduling with close attention to soil texture and effective rainfall or irrigation.Abstract. Sensor-based irrigation scheduling methods (SBISM) measure soil moisture to allow scheduling of irrigation events based on the soil-water status. With rapid development of soil moisture sensors, more producers have become interested in SBISM, but interpretation of the sensor data is often difficult. Computer-based methods attempt to estimate soil water content and the Arkansas Irrigation Scheduler (AIS) is one example of a weather-based irrigation scheduling tool that has been used in the Mid-South for many years. To aid producers and consultants interested in learning more about irrigation scheduling, field studies were conducted for two years in Mississippi and a year in Missouri to compare SBISM with the AIS. Soil moisture sensors (Decagon GS-1, Acclima TDR-315, Watermark 200SS) were installed in multiple locations of a soybean field (Mississippi) and cotton field (Missouri). Soil water contents of the fields were measured hourly at multiple depths during the growing seasons. The AIS was installed on a computer to estimate soil water content and the required data were obtained from nearby weather stations at both locations and manually entered in the program. In Mississippi, numbers and times of the irrigation events triggered by the SBISM were compared with those that would have been scheduled by the AIS. Results showed the number and time of irrigation events scheduled using the SBISM were often different from those predicted by the AIS, especially during the 2018 growing season. The highly variable soils at the Missouri site complicated the interpretation of the sensor values. While all of the sites were within the Tiptonville silt loam map unit, some of the measurements appeared to come from sandier soils. The AIS assumed more water entered the soil than the sensors indicated from both irrigations and rainfalls less than 25 mm. While the irrigation amounts were based on the pivot sprinkler chart, previous testing had confirmed the accuracy of the charts. Furthermore, the difference varied among sites, especially for rainfall large enough to cause runoff. The recommendations based on the Watermark sensors agreed fairly well with the AIS in July after the data from the sandiest site was omitted; however, the later irrigations called for by the AIS were not indicated by the sensors. Both the sensor-based irrigation scheduling method and the AIS could be used as tools for irrigation management in the Mid-South region, but with careful attention to soil texture and the effective portion of rainfall or irrigation. Keywords: Irrigation scheduling, Soil moisture sensor, Soil water content, Water management.

Forty Years of Increasing Cotton’s Water Productivity and Why the Trend Will Continue

Highlights Over the last 40 years the amount of irrigation water used by cotton in the United States has decreased while yields have increased leading to a large increase in crop water productivity (CWP). Many factors have contributed to improved CWP, such as improvements in water delivery systems. Irrigation scheduling technologies have also contributed to improved CWP; however, farmer adoption of advanced scheduling technologies is still limited and there is significant room for improvement. Increased yields from improved cultivars without an increase in water requirements has also been important for CWP. Continued developments in sensor technologies and improved crop simulation models are two examples of future strategies that should allow the U.S. cotton industry to continue an upward trend in CWP. Abstract. Over the last 40 years the amount of irrigation water used by cotton in the United States has decreased while yields have increased. Factors contributing to higher water productivity and decreased irrigation water use include migration of cotton out of the far western U.S. states to the east where more water requirements are met by rainfall; improved irrigation delivery systems with considerable variation in types and adoption rates across the U.S.; improved irrigation scheduling tools; improved genetics and knowledge of cotton physiology, and improved crop models that can help evaluate new irrigation strategies rapidly and inexpensively. The considerable progress over the last 40 years along with the promise of emerging technologies suggest that this progress will continue. Keywords: Cotton, Crop water productivity, Irrigation, Sustainability, Water use efficiency.

An integrated tool for optimal energy scheduling and power quality improvement of a microgrid under multiple demand response schemes

This paper presents an integrated tool to mitigate power quality issues in a microgrid through coordinating the operating schedule of its generating resources and loads. Such a microgrid includes renewable and conventional distributed energy resources, electric vehicles, energy storage, linear and nonlinear loads, while it serves as an example small-to-medium scale residential and commercial buildings. The proposed tool operates on a sequential, two-stage basis: at the first stage the energy management system (EMS) ensures that the microgrid’s generation resources and loads are dispatched at the minimum total system cost. In addition, it assesses the potential provision of flexibility services towards the system operator, relying on financially incentivized power signal requests. At the second stage, the power quality (PQ) framework evaluates whether the proposed optimal solution complies or not with several PQ standards applicable to the distribution level. The unique characteristic of the proposed tool is the self-triggered interaction between the EMS and the PQ framework, which identifies potential PQ violations, and restores the PQ indices to acceptable levels through an iterative process. Case studies have been performed with realistic model parameters to verify the performance of the proposed integrated tool. The obtained results demonstrate the effectiveness of the algorithm in managing voltage deviations, voltage unbalance, as well as harmonic distortions with a small additional cost for the total system.

Assessment of the Burden on Caregivers of Patients with Mental Disorders- A cross-sectional study

Background: Mental illness is the condition that disrupts a person’s thinking, feeling, mood, ability to relate to others and daily functioning. Caregivers play an important role in the management of all the chronic mental illnesses. Thus the aim of this study was to assess the burden and to find out association between level of burden among caregivers of mentally ill patients and related variables.&#x0D; Material and Method: An analytical cross-sectional, study was conducted among the caregivers of mentally ill patients visiting at selected Hospital of Bharatpur. Burden Assessment Scheduled (BAS) tool was used to collect the data. Chi-square test was used to find the association between levels of burden with related variables. P-value less than 0.05 were considered as statistically significant.&#x0D; Results: Research showed that among 204 caregivers 61.8% had moderate level of burden and none of the caregivers had mild level of burden however 38.2% had severe level of burden. The statistically significant factors that affect the level of burden in the caregivers are: place of residence, relation with the patient, duration of mental illness, duration of care giving and monthly expenditure for treatment are the statistically significant variables to the level of burden.&#x0D; Conclusion: More than one third of the caregivers have severe level of burden. The level of burden is high among the female as among the male. Caregivers who are married and education status less than primary level have high level of burden. Also, the caregivers who are from nuclear family and whose patient’s duration of mental illness more than one year had severe level of burden. The statistically significant factors that affect to the level of burden are: place of residence, relation with the patients, and duration of mental illness, duration of care giving and monthly expenditure for treatment.

Research Scope and Tools for Workflow Scheduling in Cloud Environment

Workflow is a prototype that executes the behavior of scientific and engineering applications for which the sequence of tasks needs to be automated based on the input parameters specified. Difficulties arise for CSPs primarily during the execution environment due to its direct impact on various QoS parameters. Existing workflow scheduling techniques has research focus with dimensions that include undetermined demands, task failure,l delay cost, ambiguous deadlines, bandwidth, cache inclusion, scheduler policies, VM cycles, QoS impact, OS support, fault-tolerant and virtualization level. The comparative analysis made in this paper for workflow scheduling strategies and tools used in the cloud environment towards QoS parameters would have a greater impact on industry task automation which in turn provides the researchers to set their objectives and tools to be used in-order to bring forth new approaches and solutions. This paper can be extended considering the research scope given in the comparative analysis and the data size pertaining to each task in workflow and data migration issues which cannot be done without cost, legal policies and technical issues.

Use of Predictive Weather Uncertainties in an Irrigation Scheduling Tool Part I: A Review of Metrics and Adjoint Methods

AbstractIrrigation management consists of many components. In this work we review and recommend rainfall forecast performance metrics and adjoint methodologies for the use of predictive weather data within the Colorado State University Water Irrigation Scheduler for Efficient Application (WISE). WISE estimates crop water uses to optimize irrigation scheduling. WISE and its components, input requirements, and related software design issues are discussed. The use of predictive weather allows WISE to consider economic opportunity‐costs of decisions to defer water application if rainfall is forecast. These capabilities require an assessment of the system uncertainties and use of weather prediction performance probabilities. Rainfall forecasts and verification performance metrics are reviewed. In addition, model data assimilation methods and adjoint sensitivity concepts are introduced. These assimilation methods make use of observational uncertainties and can link performance metrics to space and time considerations. We conclude with implementation guidance, summaries of available data sources, and recommend a novel adjoint method to address the complex physical linkages and model sensitivities between space and time within the irrigation scheduling physics as a function of soil depth. Such tool improvements can then be used to improve water management decision performance to better conserve and utilize limited water resources for productive use. Editor’s note: This paper is part of the featured series on Optimizing Ogallala Aquifer Water Use to Sustain Food Systems. See the February 2019 issue for the introduction and background to the series.

Use of Predictive Weather Uncertainties in an Irrigation Scheduling Tool Part II: An Application of Metrics and Adjoints

AbstractWe apply predictive weather metrics and land model sensitivities to improve the Colorado State University Water Irrigation Scheduler for Efficient Application (WISE). WISE is an irrigation decision aid that integrates environmental and user information for optimizing water use. Rainfall forecasts and verification performance metrics are used to estimate predictive rainfall probabilities that are used as input data within the irrigation decision aid. These input data errors are also used within a land model sensitivity study to diagnose important prognostic water movement behaviors for irrigation tool development purposes simultaneously performing the analysis in space and time. Thus, important questions such as “how long can a crop water application be delayed while maintaining crop yield production?” are addressed by evaluating crop growth stage interactions as a function of soil depth (i.e., space), rainfall events (i.e., time), and their probabilistic uncertainties. Editor’s note: This paper is part of the featured series on Optimizing Ogallala Aquifer Water Use to Sustain Food Systems. See the February 2019 issue for the introduction and background to the series.

Generation, visualization, and evaluation schedule of repetitive construction projects using GIS

Surroundings is a major factor that affects scheduling of a repetitive construction project. But, both the past experience of planner and the commercially available tools are limited to take the effects of surroundings on the developed schedule into consideration. Therefore, geographic information systems (GIS) are used in the current study to consider the effects of surroundings on the schedule of a repetitive construction project. The interactive tool for project scheduling is developed in GIS environment by writing in-house scripts for both non-repetitive and repetitive units. 4D execution sequences along with its surroundings have been developed in GIS by linking schedule of a repetitive project with related 3D components. Hence, the effects of surroundings on the schedule of a repetitive construction project can be considered through using GIS in the current case study. Also, the 4D execution sequence along with its surroundings is used in checking the constructability of the developed schedule. Moreover, it helps in identifying logical errors, missing activities in the schedule, spatial feasibility, and constructability analysis. The developed interactive tool is used for the scheduling of Udaigiri boys hostel (UBH) building, as a case study, and helps the practitioners and planners in scheduling of their repetitive construction projects.

A Model-Based Real-Time Decision Support System for Irrigation Scheduling to Improve Water Productivity

A precisely timed irrigation schedule to match crop water demand is vital to improving water use efficiency in arid farmland. In this study, a real-time irrigation-scheduling infrastructure, Decision Support System for Irrigation Scheduling (DSSIS), based on water stresses predicted by an agro-hydrological model, was constructed and evaluated. The DSSIS employed the Root Zone Water Quality Model (RZWQM2) to predict crop water stresses and soil water content, which were used to trigger irrigation and calculate irrigation amount, respectively, along with forecasted rainfall. The new DSSIS was evaluated through a cotton field experiment in Xinjiang, China in 2016 and 2017. Three irrigation scheduling methods (DSSIS-based (D), soil moisture sensor-based (S), and conventional experience-based (E)), factorially combined with two irrigation rates (full irrigation (FI), and deficit irrigation (DI, 75% of FI)) were compared. The DSSIS significantly increased water productivity (WP) by 26% and 65.7%, compared to sensor-based and experience-based irrigation scheduling methods (p &lt; 0.05), respectively. No significant difference was observed in WP between full and deficit irrigation treatments. In addition, the DSSIS showed economic advantage over sensor- and experience-based methods. Our results suggested that DSSIS is a promising tool for irrigation scheduling.