expert system for irrigation management (esim)

Agricultural Systems 36 (1991) 297-314

Expert System for Irrigation Management (ESIM)

R. Srinivasan, Bernard A. Engel

Department of Agricultural Engineering, Purdue University, West Lafayette, Indiana 47907, USA

&

G. N. Paudyal

Agricultural Land and Water Development Program, AIT, Bangkok-10501, Thailand

(Received 1 June 1990; revised version received 28 November 1990; accepted 28 November 1990)

ABSTRACT

In an irrigation management problem, expert decisions are made at various levels for assessment of water availability and requirements, proposing the type of water scheduling, and deriving an actual operational poliey for various crop scenarios. In this study, an expert system 'ESIM' (Expert System For Irrigation Management) was developed for making decisions on water management in an irrigation project. A general strategy for development and application of expert systems was proposed. Since a development tool was used, the study focuses on knowledge engineering which involves knowledge acquisition, system design, implementation and evaluation. Effective use of graphical displays made interpreting and analyzing results easier. Based on the statistical analysis of flowrate in stream, a new type of index called 'Probabilistic Scheduling Index' was proposed in this work. The expert decision on a particular type of scheduling (Fixed/Arranged/Demand) was based on this index together with other site specific conditions.

The expert system ESIM was applied to an irrigation management problem of the Mac- Taeng Irrigation Project located in northern Thailand. Output of the expert system was found to be reasonable and demonstrated results could be used to assist in improving water management decisions.

297 Agricultural Systems 0308-521X/91/$03.50 © 1991 Elsevier Science Publishers Ltd, England Printed in Great Britain

298 R. Srinivasan, Bernard A. Engel, G. N. Paudyal

INTRODUCTION

Present irrigation systems and their problems

Irrigation development is accepted as a major vehicle for increasing agricultural food production in many countries in South and Southeast Asia, particularly those with large, expanding populations. Irrigation enables the farmers to use modern high-yielding varieties of various crops and makes a second, and sometimes even a third, crop possible in one year. Irrigation creates jobs in rural areas and can promote stability in society by creating a solid agrarian economy.

Concurrent with new irrigation facility development, substantial investments to upgrade existing systems and improve their performances are being made in several South and Southeast Asian countries. Two reasons for large outlays to upgrade have been identified.

• Performance of many existing irrigation systems has been unsatisfactory. • New irrigation development costs are escalating rapidly, because the

most favorable lands for development are being exhausted (Small, 1981). Investments to modernize existing irrigation systems are also being made because it is believed substantial cost effective performance improvements can be achieved.

When water management became a key issue for improving irrigated crop environments in the late 1960s and early 1970s, many advocated the improvement of on-farm management as the primary means of increasing water use efficiency in irrigated systems. However, Wickham and Takase (1976) argued that the farm-level problems most often result from water allocation and distribution problems at the main system level. Unless main system management is organized and improved, on-farm water problems cannot be resolved. Recent experiences of the Command AreaDevelopment Program in Andhra Pradesh State, India, indicate 'precision developed lands and lined channels remained dry for the simple reason that no-one is (carefully) operating the canal system' (Ali, 1981). Other studies (Valera & Wickham, 1976; Levine & Wickham, 1977; Chambers, 1980) suggested that the farm-level focus should not get priority over main system operation problems if major gains in irrigation system performance are to be achieved. Chambers and Wade (1980) analyzed the reasons why main system water management problems have not received adequate attention in the past.

Arguments favoring consideration of main system problems over on- farm problems when trying to improve irrigation system performance are

Expert system for irrigation management (ESIM) 299

convincing. However, on-farm issues should not be completely separated from main system issues because they are interrelated. Farms and farmers are as much a part of an irrigation system as are canals and canal tenders. Irrigation-system management efforts should take a global view of problems existing at all levels of the system and attempts should be made to analyze the problems.

Why an expert system (ES) approach?

The aforementioned problems clearly indicate the need for more research in the field of irrigation management, which consists of various fields of study including human behavior, environmental impact, socioeconomic views, hydraulics, and agronomy. In the past, research has been undertaken to identify the problems, but definite solutions have not yet been achieved. All these problems are location-specific. More research is needed to combine both on-farm and main system management including water requirement at the on-farm level, and allocation and scheduling of irrigation water in the main system.

One way of simplifying the solution to the above problem may be by the introduction of AI (Artifical Intelligence) techniques in irrigation management. Books and journals store a large volume of knowledge, but, before that knowledge can be applied one must read it, understand it and then decide how to use that knowledge for solving a specific problem. This limits the knowledge to a small group. Since a book rarely defines 'how' to perform a given task, the knowledge could be misinterpreted and wrongly applied. Expert systems technology, a subfield of AI, can capture and use this knowledge in a computer.

Objectives

The objectives of this study were as follows:

• Interrelate on-farm and main system irrigation management. • Develop an ES for irrigation management. • Demonstrate the use of an ES as an operational tool in an irrigation

system to improve irrigation management. • Evaluate ESs as irrigation management tools for analyzing three

proposed scheduling alternatives by simulating the hypothetical data under expected canal operational conditions.

• Application of the ES to a real-world situation (the Mae-Taeng irrigation project).


ESIM (EXPERT SYSTEM FOR IRRIGATION MANAGEMENT)

After establishment of an irrigation project, it is important to get maximum benefit with the existing system, or with slight modifications to the existing system, instead of developing a new project. A major problem has been identified as the selection of the water allocation method in the main canal, and subsequently in the laterals.

of method Estimate ETo

1- I

F~lit crop or I[_ crop calendar

databases r

1 Estimation of L reference ETo F

Estimation of ] El'crop for ~_

each branch canal [ !

Estimation of ~ater requinmaent for

each brar~h canal and for the

total project

Decision on type of [ scheduling in the main I canal based on water availability and water

requirement for.thc whole l- season using [

LN3 distribution [

A graphical display between

WA & WR Vs Time

Do you wish to I yes 1

change the type

I of schexluling

4? Fig. 1.

Daily rainfall L ] Historical generation 1- [ rainfail data

1 or2

J Crop or crop calendar

-I databases

Temperature, radiation, humidity

and windspeed

Rainfall, crop, crop calendar

databases

f Inflow data ] in main canal I

1. Area I _ 2. Crop Data - 3. CroPdatabasesCalendar

1,2.3

Flowchar t for decision level 1.


A recent study of irrigation delivery system management in Egypt (Richardson et al., 1984) confirmed that operation of the delivery system was limiting improved farm water management. Constraints caused by operation of the system as a static distribution entity, and other techniques and policies, are associated with on-farm water management problems throughout the world. This indicates a need to improve project delivery systems and methods to allow better farm water management. It is becoming apparent that both the physical system canals and gates (hardware) and the management, social, economic, and psychological aspects (software) need careful attention. Irrigation delivery to the field crop is a function of three components: (1) the delivery flow rate onto the field, which may be by surface spreading, sprinkler, or drip systems; (2) the delivery frequency to the field, or simply the times of the deliveries; and (3) the duration of the delivery. The major classifications of this schedule are fixed, arranged or demand and are defined as:

(1) Fixed: as the term implies a rigid schedule with fixed flow rate, fixed frequency and fixed duration. The overall control of scheduling is in the hands of water authority.

(2) Arranged: the schedule here can be arranged between the farmers and the water authorities in regulating the rate and time.

(3) Demand: this implies that no limits exist on rate, frequency or duration, and there is no external control by the water authority.

A detailed description of individual classifications is provided in Replogle, 1984.

The ES ESIM was developed by formulating the rules related to each step shown in Figs 1 and 2. Based on these figures, major decisions are made at three levels. Each level is discussed in the following sections. Development of a suitable data base was one of the first requirements of the ES. Components of the data base are described in the following section.

Data bases

Data needed for developing and running the expert system (ES) are classified into six groups.

(a) Meteorological data base (b) Crop calendar data base (c) Soil and crop characteristics (d) Canal hydraulics data base (e) Hydrologic data base (f) General information


Decision level2

Selection of the type of scheduling in the main canal based on general

information

l Decision level3

Selection of the type of scheduling in the branch canal

based on the information

Estimation of amount and time of irrigation

each branch canal for each decade

Graphical representation of WR and WS for each branch canal

for the season and for each decade

+ Fig. 2. Flowchart for decision levels 2 and 3.

( a) Meteorological data base Temperature, humidity, radiation, wind speed and rainfall are the main data needed to compute the Reference Evopotranspiration (Reference ETo) and for the subsequent calculation of actual crop water requirements. These data are required on a daily basis.


(b) Crop calendar data base The data needed are number of crops grown in the branch canal, names of the crops, planting date and area factor for each crop. The area factor is the ratio of area of that crop planted in that branch canal to the total cropped area of the project. The user has the choice of modifying this information later, after determining the type of scheduling, in case results at decision level 1 (Fig. 1) are unsatisfactory (scheduling method is unacceptable). The data can be edited on the screen itself and are stored in the respective canal files.

( c ) Soil and crop characteristics The characteristics of the soils and the crops at the project site are needed and may be obtained from project publications. The data needed are: maximum soil moisture holding capacity (mm/m depth of soil), total growing season of the crop, duration (d) and crop coefficient (Kc) values of the crop at each stage (planting, vegetative, ripening and maturity), and root length. The data can be edited whenever one faces unsatisfactory scheduling results in decision level 1 (Fig. 1).

(d) Canal hydraulics data base The main canal capacity, total cropped area of the project, capacity of each branch canal and total project efficiency are included. These data are normally obtained from project reports. The total cropped area of the project and the project efficiency can be updated periodically.

(e ) Hydrologic data base In the dry season, water availability in the river varies drastically from year to year. It is essential to forecast flowrate in stream carefully, since it determines the amount of water to be scheduled in each branch canal under water shortage. Here, a 3-parameter lognormal distribution (LN3) is used to fit the available stream flow data. From the user's choice of probability level, availability of water at the main canal can be generated for a period of one calendar year. The estimated average value of water availability is for a decade.

(f) General information Apart from the quantitative data, it is desirable to know general information about the project site that is important in the scheduling method. This information includes the type of structure in the main and branch canals, types of crops grown, availability of main canal operators, type of communicat ion between the farmers and the water authorities, degree of irrigation awareness among farmers, knowledge of canal operators about irrigation management, and organizational setup such as a water user's


organization to represent any decisions jointly made by the user's group to authorities.

Knowledge base description

The quality and ability of an expert system will be determined by how much knowledge has been included, and how well the knowledge has been structured and systemized. This is dependent upon the knowledge engineering process which includes selection of an appropriate problem and development tool, knowledge acquisition, system design, implementation and testing, and evaluation.

( i ) Decision level 1 The rules related to each step were formulated carefully to determine the type of scheduling in the main canal (demand, arranged, or fixed) as shown in Fig. 1. When a type of scheduling is suggested at the end of the consultation, a graphical display in the form of a bar chart provides a clear idea about water availability and water requirement for the whole project on a 10-d basis. After asserting the decision, the user has the option to modify the type of scheduling that has been recommended by choosing one of the alternatives (the choices are discussed in the following sections).

Rules to determine the method for calculating the Reference Evopotran- spiration (Reference ETo) are invoked first. The FAO procedure (Doorenbos & Pruitt, 1977) was used to formulate these rules. In the current implementation, only the Penman method is incorporated. This method requires the following meteorological data: temperature, humidity, radiation, and wind speed on a daily basis.

After determining the ETo method, ESIM calls an external procedure to find the reference ETo. Before it calculates the reference ETo, the procedure assists in generating the daily rainfall data based on historical records which are one of the inputs required to find the ETcrop (Crop Evopotranspiration). The statistical procedures involved in this are the 3-parameter lognormal distribution, uniformly distributed random number generation and normally distributed random number generation.

The reference ETo is estimated on a daily basis using the modified Penman method (Doorenbos & Pruitt, 1977). The crop calendar data bases, soil and crop characteristics, output of the climatic data from the reference ETo and the generated rainfall data are used to etimate ETcrop in each branch canal on a 10-day basis. The estimated ETcrop is used to schedule the irrigation and estimate the water requirements. Water requirements for each branch canal over the 10 d are computed. This output is stored in an external file for further computation. The total irrigation requirement for the whole


project is estimated to make a preliminary decision on the type of scheduling in the main canal (demand, arranged or fixed).

Probabilistic scheduling index (PSI). A statistical analysis was done to compare the water availability and water requirement of the project for the whole season. It was found that the 3-parameter lognormal distribution (LN3) is satisfactory most of the time. The average 10-d values of streamflow in the main canal were fitted with the LN3 distribution and the value of parameters were estimated. The distribution was then normalized to find the probability of water requirement exceeding water availability for each 10-d period where water requirement was positive.

A simple index was computed to determine the method of overall scheduling in the main canal based on water availability and water requirement for the entire season. The equation of the index is as follows:

S'Pi * WAi erobabilistic Scheduling lndex (PSI)=

where Pi = probability that water requirement is less than or equal to water availability for ith decade, and WAi = average water availability of ith decade. If the probabilistic scheduling index is 0"85, then 85% of the time the water availability can meet the water requirement of the project. The index is used to select the type of scheduling based on the experts' decisions and is defined as:

If PSI> 0.90 then demand type scheduling is chosen. If PSI< 0.90 and > 0.75 then arranged type scheduling is chosen. Otherwise fixed type scheduling is chosen.

During estimation of the probabilistic scheduling index, the procedure estimates the probability of water availability in the main canal on a 10-d basis from historical data. The statistical procedure involved is also the LN3 distribution. Once the type of scheduling in the main canal is determined, an external procedure transfers control to the knowledge base and further questions are asked to make a decision at level 2 (Fig. 2). Before transferring control, the procedure allows the user to modify the decision by choosing one of the options:

(1) Total cropped area of the project. (2) Date of planting (crop calendar data base). (3) Types of crops grown (crop calendar data base).

The system shows a bar chart comparing water availability and water requirement for the entire season. The graphical display eases decision making concerning adjustments to match water availability and requirements.


( ii ) Decision level 2 More questions about the general and hydraulic information of the project site are asked about the physical structures in the main and branch canals (level top, downstream control, or check gates), types of crops grown, irrigation awareness among farmers (high, medium, or low), onsite facilities such as type of communication between the farmers and the water authorities (person to person, telephone, radio, or none), canal operator availability (adequate, moderate, or inadequate) and their knowledge about irrigation management (high, medium, or low). The system assesses the answers and assigns certainty factors (CF) on a 0-100 scale for different types of scheduling on the main canal. A CF of 0 represents rejection, while 100 represents the most certain decision. If the probability for a particular alternative is generated under more than one rule, then the average value is estimated, except when the probability level is locked at either 0 or 100. Backward chaining techniques are used to eliminate the unnecessary questions once a decision is reached at this level. Once the principal type of scheduling is determined, control is transferred to decision level 3 (Fig. 2).

( iii) Decision level 3 Here the system determines the type of scheduling for the branch canals. The decisions to be made are sub-classes of the main scheduling (e.g. varied rate rotation, varied duration rotation, varied frequency rotation, varied frequency and rate rotation, varied frequency and duration rotation, varied rate and duration rotation). To determine one of the sub-classes, the system requires more information about the branch canal command area, its soil uniformity, uniformity of crops grown, branch canal capacity, adjustments in the farm gate, possibilities of irrigation at all time, water rights policy, storage facilities at the head works, and types of crops grown.

The rule that selects the 'fixed-frequency/arranged' type of scheduling in branch canals is shown below:

(defrule fixed-frequency-arranged (type of main canal scheduling is arranged in decision level 2) (adjustments-farm-gate yes) (irrigation-method level-basin[ flooding[ furrow) (negotiation possible) (irrigation-all-time yes) (soil-type uniform) (crop-type perennial[seasonal) = ~ >

(assert (the type of scheduling is fixed-frequency/arranged cf 70-- (gensym))))


The above rule is interpreted as: if the main canal scheduling is arranged at decision level 2, the branch canal conditions allow flow adjustment in farm gates or turn-outs, adopted irrigation method is level-basin or flooding or furrow, negotiation between the farmers and water authorities is possible, irrigation can be regulated on a 24 h basis, type of soil is uniform, and types of crops grown are either perennial or seasonal, then conclude that the scheduling method for the branch canal is fixed-frequency/arranged with a certainty factor of 70.

After the system suggests a particular method of scheduling, a procedure allocates the water to each branch canal. The output of this procedure gives the time and amount of water to be delivered to each branch canal for each decade. Finally, a project evaluation index is formulated which indicates how well the water requirement of the project is met by the water supply as a whole. To improve the P S I index, one must change the input parameters by choosing one of the options discussed earlier.

Graphical output in the form of a bar chart from the scheduling program is provided. The following displays are possible:

(i) Graphical display comparing Water Supply (WS) and Water Requirement (WR) for any branch canal.

(ii) Graphical display comparing WS and WR at any particular time (decade).

(iii) Graphical display comparing WS and WR for the whole project.

Recommendations of the ES are shown at the end explaining why a particular type of scheduling was selected. One can change the type of scheduling by rerunning the ES with different options and comparing results and the expected benefits versus costs for many option trials.

Integration

In this study, the expert system was initially developed with the help of a commercially available shell called EXSYS (EXSYS, 1985). Because this shell has limitations (Engel et al., 1988), another tool was later selected (CLIPS). The recent addition of a user interface (Srinivasan et al., 1990) to CLIPS (NASA, 1989) enhances its capabilities for integrating symbolic and numerical simulations such as estimation of water requirements and probabilistic scheduling. The knowledge base was developed using a shell (CLIPS) and the main external program was written in QUICK BASIC, which provides help menus and assistance in integrating the external procedures. In total, 66 rules were incorporated in ESIM to decide on the type of scheduling in main and branch canals. Estimating water requirements of an irrigation project involve simulation of the following: ETo, ETcrop, and


water requirement for each canal (written in FORTRAN). Each program requires significant memory to execute over and above the memory required by the CLIPS shell. Due to the limitations of MS-DOS, the programs were split into individual components and linked through a common program written in QUICK BASIC. A procedure in TURBO PASCAL was developed to represent simulated results in bar charts. Graphically displaying the results has proven to be more effective for viewing and making decisions than numerical output.

RESULTS: MAE-TAENG APPLICATION OF ESIM

ESIM (Expert System for Irrigation Management) was applied to the dry season water management problem in the Mae-Taeng project in northern Thailand. The data were used to evaluate the expert system (Srinivasan, 1989).

Decision level I

Since temperature, humidity, radiation and wind speed data were readily available, a decision was made to use the Penman method to estimate reference ETo. The external procedure was called (Fig. 1) and ETo, ETcrop and water requirements for each branch canal and for the total project were estimated.

For the given cropping pattern and calendar of activities for the dry season in the Mac-Tang project, the probabilistic scheduling index for a total cropped area of 14 070 ha with a project efficiency of 30% was found to be 0.40. The system suggested fixed scheduling for the whole season in the main canal. The graphic output showed how the water requirement and water availability was distributed for different decades. From the fixed scheduling suggestion and PSI of 0.40, a decision was made to reduce total cropped area. The probabilistic scheduling index improves when this is done, but whether it will satisfy the farmers is questionable. Past records and experience indicate that only about 6000 ha can be cultivated without much risk of not meeting the demand during the dry season.

By selecting an area of 7000 ha, the probabilistic scheduling index was found to be 0-57 and the system suggested fixed type scheduling. The outputs of water availability (WA) and water requirement (WR) for 14070 and 7000 ha of cropped area are presented in Fig. 3. This figure indicates that during the peak water requirement decades (8-11 for 14070ha) the difference between the water availability and water requirement is high when compared to an area of 7000ha. The maximum demand for 14070ha of


12

10

8

6

4

2

,,( 0

Q In Million nY'3

t i J z ~ i = r i

2 3 4 5 6 7 8 9 10 11

Time(d~Alde) 12 13 14 15

Fig. 3. Comparison of WA and W R versus time. Case 1: no change in the planting date. WA = water availability; W R = water requirement.

cropped area is 11-9 × 10 6 m a, whereas for 7000 ha the maximum demand is only 5"9 x 10 6 m 3.

The results of fixed scheduling were not satisfactory even after reducing the cropped area. One of the reasons is unavailability of storage facilities. During the second run, changes in the crop calendar data base of branch canals were made by advancing the planting date. The peak water requirements can then be distributed over the decades, thereby reducing the difference between water availability and water requirement. It was found that, in the total cropped area, lateral number 23 constitutes 42% of total area. The planting dates were advanced in that canal by 10-15 d and ESIM was allowed to run to find the reference ETo, ETcrop and irrigation water requirements for each branch canal. The probabilistic scheduling index was then estimated with a total cropped area of 14 070 ha and found to be 0-52. ESIM suggested fixed type scheduling. For the same crop calendar data, the total cropped area was changed to 7000 ha and the index was found to be 0-63. ESIM again suggested fixed type of scheduling. This forms a check on how the probabilistic scheduling index varies with the change of parameters, and it behaves as one expects. The comparison of water availability and water requirement for both areas is shown in Fig. 4. The cropping season was extended from 15 to 17 decades and the peak water requirement is greatly reduced by distributing the water requirements over six decades (7-12). One can conclude that changing the planting dates for branch canal number 23 significantly improved the system's ability to meet the demand. Using several trial planting dates, ESIM estimates the probabilistic scheduling index until satisfactory results are obtained. Figures 5 and 6 show how the water requirement is distributed for the same area with different


O in MHlion m^3

6

Fig. 4.

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17

Time (decade)

Comparison of WA and WE versus time. Case l : no change in planting date; Case 2: planting date altered in BC 23.

planting dates. For total cropped areas of 14 070 and 7000 ha, the percentage of deficit between availability and water requirement is as high as 93 % and 86%, respectively. Similarly, the percentages of maximum deficits for the same area of 7000 ha with different cropping calendars are 86% and 83%, respectively. A considerable reduction in the water requirement deficit was realized by changing the crop calendar for branch canal number 23. This is not the final decision since the decision at this level may be altered in subsequent levels.

Fig. 5.

Q in Million m*3

14

12

10

8

6

4

2

0 2 3 4 5 6 7 9 10 11 t2 13 14 15 16 17

Time (decade)

Comparison of WA and WR versus time. Case 1: no change in planting date; Case 2: planting date altered in BC 23.

Fig. 6.

Expert system for irrigation management (ESIM)

O i n M i l l i o n m " 3

311

12

10

8

6

4

2

0 = = = = ~ t = ~ t i i i i i i i

2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 Time (decade)

Comparison of WA and WR versus time. Case 2: planting date altered in BC 23. WA = water availability; WR--water requirement.

Decision level 2

At this level, the type of scheduling in the main canal selected at level 1 was further refined for various site conditions to test its feasibility. For the Mae- Taeng project conditions, if one arrives at the demand or arranged type of scheduling, these cannot be recommended due to lack of suitable structures. After acquiring additional information as discussed earlier, ESIM suggested the fixed type of scheduling in the main canal for the dry season.

Decision level 3

At this level, the decision on type of scheduling in the branch canal was determined. Based on the decision at level 2, questions are asked to decide on the type of scheduling in branch canals. The system suggested a continuous supply in the main canal and rotational supply in the branch canals. Since there are no storage facilities at the head works, intermittent rotation scheduling was not possible. Once continuous supply was selected by ESIM, the system called an external procedure to find the allocation of available water to different branch canals for different decades. This procedure displayed the ratio between water supplied and water requirement for each branch canal during the crop season. Only the maximum, minimum and average values were shown for each branch canal for the whole season. A project evaluation index was shown based on this estimated ratio, which gives an idea of how well the irrigation system meets the requirements. Low


evaluation indices indicate poor distribution of water and that for Mae- Taeng was 0-68. Depending on the user's choice, the graphical display can be for any one of the following:

(i) Graphical display comparing WS and WR for any branch canal. (ii) Graphical display comparing WS and WR at any particular time

(decade). (iii) Graphical display comparing WS and WR for the whole project.

Once the decision is made, the system displays the final decision. If one is not satisfied with the decisions, the program can be rerun modifying parameters. The final decision can be compared with the expected benefits versus costs for many option trials.

The biggest limitation of ESIM is that it does not support all methods of estimating the ETo and it does not support all types of scheduling. When estimating rainfall, the preferred method is chosen and may not agree at all times. In estimating the water availability, the LN3 distribution is used. It would be better to have a statistical test determine the best method of distribution for the available data before proceeding with these statistical calculations. The irrigation management problem is location-specific, and it is difficult to generalize the solution. The same knowledge base may not be applicable to all systems. Some modification may be necessary before it can be applied to different locations. Even in the same project, the knowledge base has to be updated from time to time since the old one may not be relevant after some period.

CONCLUSIONS

In general, an expert system for irrigation management should be able to support all aspects of irrigation management including decisions on selection of the most appropriate type of scheduling. The distribution of water to different branch canals on a real-time basis, especially during shortages, poses several problems. The ES ESIM (Expert System for Irrigation Management), developed in this study, is primarily intended to determine the type of scheduling based on the computed water requirement, water availability data and general information about the site conditions. In an irrigation management problem, expert decisions are made at various levels in proposing a water scheduling method and in determining actual operational policy for various crop scenarios. The work also involved developing data bases and external computer programs for numerical processing. Based on the statistical analysis of streamflow, a new type of index called 'Probabilistic Scheduling Index' was proposed in this research.


The expert decision on the particular type of scheduling (Fixed/Arranged/ Demand) is based on this index together with other site-specific conditions. The software developed is interactive and has been made user friendly.

The developed model, ESIM, was applied to the Mae-Taeng irrigation project located in northern Thailand. Using the data collected from the site, different simulations were performed and recommendat ions were made based on ESIM. ESIM was run for cropped areas of 14 070 ha, 7000 ha and for different planting dates in the branch canal 23 which contains about 42% of the total cropped area. Results obtained from these runs showed an improvement of 93% water deficit to 83% water deficit. The water deficit can further be reduced by altering the options discussed. The results were in agreement with different experts' predictions. Developing alternative scenarios are possible due to the easy interpretation of results using the graphical display.

R E F E R E N C E S

Ali, S. H. (1981). Management of canal irrigation systems and warabandi. In Main System Canal Irrigation: Choices for Investment and Research, ed. R. Chamber. A/D/C Workshop on Investment Decisions to Further Develop and Make Use of Southeast Asia's Irrigation Resources, 17-21 August 1981, Kasersart University, Thailand.

Chambers, R. (1980). In search of water revolution: questions for managing canal irrigation in the 1980s. Report of a planning workshop on irrigation water management. International Rice Research Institute, Los Banos, Laguna, The Philippines.

Chambers, R. & Wade, T. (1980). Managing the main system canal irrigations blind spot. Economic and Political Weekly, India, pp. A 107-A 112, September 1980.

Doorenbos, J. & Pruitt, W. O. (1977). Guidelines for predicting crop water requirements. Irrigation and Drainage Paper 24, FAO, United Nations.

Engel, B. A., Jones, D. D. & Wright, J. R. (1988). Selection of an expert system development tool, ASAE (American Society of Agricultural Engineers), 88- 5017, St Joseph, MI, USA.

EXSYS (1985). Expert system development package. Exsys, Inc., Albuquerque, NM 87194, 1985.

Levine, G. & Wickham, T. (1977). Some critical issues in irrigation planning for Southeast Asia. Paper presented at the Seminar on Water Resources Problems in Developing Countries, July 1977, University of Minnesota, USA.

NASA (1989). Reference Manual, Version 4.3. Artificial Intelligence Section, Johnson Space Center, Houston, TX, USA.

Replogle, J. A. (1984). Some environmental, engineering and social impacts of water delivery schedules. In Proceedings of the 12th Congress of the International Commission on Irrigation and Drainage, Question 36, Report 61, pp. 965-78.

Richardson, E. V., Horsey, H. R. & Andrew, J. W. (1984). On-farm and delivery system water management. In Proceedings of the Specialty Conference of the


Irrigation and Drainage Division, ASAE (American Society of Agricultural Engineers), Flagstaff, Arizona, 24-26 July, pp. 493-501.

Small, L. E. (1981). Evaluating improvements in irrigation performance. Paper presented at the A/D/C Workshop on Investment Decision to Further Development and Make Use of Southeast Asia's Irrigation Resources, 17-21 August, Kasersart University, Thailand.

Srinivasan, R. (1989). Expert system for irrigation management. Master's thesis, Asian Institute of Technology, Bangkok, Thailand.

Srinivasan, R., Rewerts, C. C., Engel, B. A., Rogers, J. B. & Jones, D. D. (1990). User guide for Purdue CLIPS interface. Department of Agricultural Engineering, Purdue University, IN, USA.

Valera, A. & Wickham, T. H. (1976). Management of traditional and improved irrigation systems: some findings from the Philippines. Paper presented at the Choices in Irrigation Management Workshop, University of Kent, England.

Wickham, T. H. & Takase, K. (1976). Some management issues in irrigation development. Paper presented at the CPDS-UPLB-NWRC-NIA Workshop on Water Resources, November, Los Banos, Laguna, The Philippines.

expert system for irrigation management (esim)

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