Decision Support System for Crop Planning during Droughts

Decision support for irrigation project planning using a genetic algorithm

Abstract: This work presents a model based on on-farm irrigation scheduling and the simple genetic algorithm optimization (GA) method for decision support in irrigation project planning. The proposed model is applied to an irrigation project located in Delta, Utah of 394.6 ha in area, for optimizing economic profits, simulating the water demand, crop yields, and estimating the related crop area percentages with specified water supply and planted area constraints. The user-interface model generates daily weather data based on long-term monthly average and standard deviation data. The generated daily weather data are then applied to simulate the daily crop water demand and relative crop yield for seven crops within two command areas. Information on relative crop yield and water demand allows the genetic algorithm to optimize the objective function for maximizing the projected benefits. Optimal planning for the 394.6 ha irrigation project can be summarized as follows: (1) projected profit equals US$ 114,000, (2) projected water demand equals 3.0310 6 M 3 , (3) area percentages of crops within UCA#2 command area are 70.1, 19, and 10.9% for alfalfa, barley, and corn, respectively, and (4) area percentages of crops within UCA#4 command area are 41.5, 38.9, 14.4, and 5.2% for alfalfa, barley, corn, and wheat, respectively. Simulation results also demonstrate that the most appropriate parameters of GA for this study are as follows: (1) number of generations equals 800, (2) population sizes equal 50, (3) probability of crossover equals 0.6, and (4) probability of mutation equals 0.02. # 2000 Published by Elsevier Science B.V.

Combining remote sensing-simulation modeling and genetic algorithm optimization to explore water management options in irrigated agriculture

Abstract: We present an innovative approach to explore water management options in irrigated agriculture considering the constraints of water availability and the heterogeneity of irrigation system properties. The method is two-folds: (i) system characterization using a stochastic data assimilation procedure where the irrigation system properties and operational management practices are estimated using remote sensing (RS) data; and (ii) water management optimization where we explored water management options under various levels of water availability. We set up a soil–water–atmosphere–plant model (SWAP) in a deterministic–stochastic mode for regional modeling. The distributed data, e.g. sowing dates, irrigation practices, soil properties, depth to groundwater and water quality, required as inputs for the regional modeling were estimated by minimizing the residuals between the distributions of field-scale evapotranspiration (ET) simulated by the regional application of SWAP, and by surface energy balance algorithm for land (SEBAL) using two Landsat7 ETM+ images. The derived distributed data were used as inputs in exploring water management options. Genetic algorithm was used in data assimilation and water management optimizations. The case study was conducted in Bata minor (lateral canal), Kaithal, Haryana, India during 2000–2001 rabi (dry) season. Our results showed that under limited water condition, regional wheat yield could improve further if water and crop management practices are considered simultaneously and not independently. Adjusting sowing dates and their distribution in the irrigated area could improve the regional yield, which also complements the practice of deficit irrigation when water availability is largely a constraint. This result was also found in agreement with the

Optimal land and water resources allocation policies for sustainable irrigated agriculture

Abstract: Conjunctive use of surface water and groundwater is being practiced in many regions of the world to bring more areas under irrigation, increase agricultural production and productivity, and also maintain overall system balance. Successful agricultural water management policies put the physical, hydro-geological, and socio-economic constraints on these integrated water supplies. To sustain these constraints, a linear programming (LP) model has been developed for optimal land and water resources allocation in various sectors of the Hirakud Canal Command, a multi-purpose irrigation project on the river Mahanadi in eastern India. To enhance the decision-taking ability of the Hirakud command area development authority, a menu-driven and user-friendly software has been developed by Visual-Basic that incorporates model-base, data-base, knowledge-base subsystems along with the user-interface. The model-base subsystem includes LP, groundwater balance, and evapotranspiration models. The data-base subsystem includes the meteorological, crop, and water resources data. The knowledge-base subsystem was developed from the knowledge derived from the results of the aforementioned models. Sensitivity analysis of the LP model parameters was performed by varying the parameters that affect the optimal cropping pattern and groundwater allocation. The result indicates that conjunctive use of 87% surface water and 13% groundwater is the sustainable water allocation policy of the command area. The model results also indicate that a 20% deviation in existing cropping pattern is the best alternative as it considered socio-economic need and also meets the entire food demand of the study area.

Irrigation Planning Under Uncertainty—A Multi Objective Fuzzy Linear Programming Approach

Abstract: The problem of irrigation planning becomes more complex by considering an uncertainty. The uncertainties can be tackled by formulating the problem of irrigation planning as Fuzzy Linear Programming (FLP). FLP models can incorporate the scenario of real world problem. In the present study, Multi Objective Fuzzy Linear Programming (MOFLP) irrigation planning model is formulated for deriving the optimal cropping pattern plan for the case study of Jayakwadi project in the Godavari river sub basin in Maharashtra State, India. Four conflicting objectives are considered such as Net Benefits (NB), Crop/Yield Production (CP), Employment Generation/Labour Requirement (EG) and Manure Utilization (MU). Four different cases are considered to incorporate the uncertainty in MOFLP model. To include the uncertainty in irrigation planning problem only objectives are taken as fuzzy and constraints are crisp in nature in Case-I. To consider the uncertainty involved in availability of resources, in Case-II the stipulations are fuzzy. The technological coefficients are fuzzy in Case-III. The Case-IV includes both technological coefficients and stipulations fuzzy. The level of satisfaction (λ) works out to be 0.58, 0.50, 0.50 and 0.28 respectively for Case-I to IV. The results obtained in Case-IV are more realistic and promising as it involves the uncertainty in technological coefficients and stipulations simultaneously.

Integration of heuristic knowledge with analytical tools for the selection of flood damage reduction measures

Abstract: Heuristic knowledge has been integrated with analytical tools to support decision making for flood management. Development of an expert system called Intelligent Flood Management System for the selection of appropriate flood damage reduction measures for a given area is described. The selection of flood damage reduction measures is based on hydraulic, hydrological, geotechnical, environmental, and economic factors related to the river system and the area to be protected from floods. The knowledge base of the Intelligent Flood Management System is generic and can be used to identify a suitable flood management option for any area. The model base of the Intelligent Flood Management System consists of the hydraulic analysis package HEC-RAS, the flood damage analysis program HEC-FDA, and a model for economic analysis. The graphical user interface is developed for effective communication with the system. The developed system has been implemented to identify appropriate flood damage reduction options for the to...

Operational Guidance During Droughts: Expert System Approach

Abstract: Seattle, Washington, suffered its most extreme drought on record during the summer and fall of 1987. Severe and continuing water use restrictions were required to limit the drought's impact on municipal water supplies, fish populations, and navigation. This paper describes a decision support system used to aid in drought decisions. Its components include an expert system, a linear programming model, database management tools, and computer graphics. The expert system incorporates operator experience and intuition using a rule base developed through interviews with management personnel from the Seattle Water Department. The expert system also integrates the other programming techniques into a single system. A linear programming model determines system yield and optimal operating policies for past hydrologic regimes. Database management and graphics software store and allow the display of over two thousand operating policies to decision‐makers. The system provides user‐friendly support to help decision‐maker...

Intelligent Decision Support and Reservoir Management and Operations

Abstract: Reservoir management and operations are very complex activities in the field of water resources engineering. During the last two decades, a number of systems analysis techniques, particularly optimization and simulation, have been adopted for reservoir planning, design, and operations. Recently, the area of knowledge-based engineering (expert systems) has emerged as a potential technique for incorporationg human expertise and some degree of intelligent judgment into decision-supporting software. This paper presents the potential benefits of this technology in the area of reservoir management and operations. The presentation is illustrated with an example of an engineering expert system for reservoir analysis that is currently in development.

Expert System for Calibrating Swmm

Abstract: EPA'S Storm Water Management Model (SWMM) simulates all aspects of the hydrologic and quality cycles. Using expert system technology, an interactive user‐support framework has been developed to automate the calibration of the runoff block. It acts as a front end to assist the user in the initial estimation of the parameter values and in building the SWMM input files. It interprets the simulation results and suggests some useful adjustments in the value of significant parameters thus reducing the user's time and effort. For the interpretation of simulation results, production rules are employed to help the user decide what parameters need to be adjusted. Some heuristics have been developed to evaluate the new parameter values. The combination of simulation techniques and expert system methodologies facilitates the use of sophisticated models such as SWMM.

Expert System for Drought Management Planning

Abstract: Expert systems have been proposed for a wide range of civil engineering applications. They have been applied most successfully in situations governed by simple rules. Many civil engineering decisions, however, must be made in environments in which simple rules are not possible, but rather large amounts of data must be analyzed and evaluated. This paper presents a methodology in which an expert system shell (INSIGHT 2+) is used to evaluate and display information on drought management planning. A linear programming model is used to generate optimal operating policies as a function of numerous past drought experiences. These policies are incorporated into an expert system where the user is required to identify the degree to which the current drought situation is similar to past events. The expert system then aids the user in developing appropriate management policies based on time of year, descriptions of drought intensity, and other system characteristics.

Expert systems in river basin management

Abstract: The basic features of expert systems are outlined and illustrated, and the potential of such an approach as an aid to decision-making in the field of river basin management is explored. Work undertaken towards the development of an expert system for the operational control of a wastewater treatment plant is described. Further areas of potential application are discussed, including data utilization.