Application of Artificial Neural Network and Information Entropy Theory to Assess Rainfall Station Distribution: A Case Study from Colombia
Abstract
:1. Introduction
2. Materials and Methods
2.1. Characteristics of the Studied Region
2.2. Methods
2.2.1. Meteorological Network Data
2.2.2. Data Processing
2.2.3. Development of the Artificial Neural Network Model
2.2.4. Performance Evaluation of the Rainfall Network in the Cundinamarca Region
3. Results and Discussion
3.1. Classification of Rainfall Stations
3.2. Scenario Configurations
3.3. Mutual Information Classification Analysis
4. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Watershed | Area (km2) |
---|---|
Sumapaz River | 2527 |
Bogota River | 5671 |
Magdalena River | 2191 |
Negro River | 4239 |
Minero River | 990.8 |
Ubate-Suarez River | 1965 |
Blanco River | 471.0 |
Gachetá River | 97.30 |
Machetá River | 508.7 |
Input Variable | Transformation |
---|---|
Latitude (m) | y = (x − xmin)/(xmax − xmin) |
Longitude (m) | y = (x − xmin)/(xmax − xmin) |
Elevation (m) | y = x/xmax |
Annual rainfall (mm) | y = x/xmax |
Standard deviation of annual rainfall (mm) | y = x/xmax |
Monthly rainfall (mm) | y = x/xmax |
Mutual Information Range | Index |
---|---|
0–0.5 | High deficit |
0.5–1.0 | Deficit |
1.0–1.5 | Acceptable |
1.5–2.0 | Above average |
>2.0 | Excess |
Parameter | Rainfall Station Number | ||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|
S1 | S4 | S8 | S9 | S10 | S13 | S15 | S16 | S18 | S22 | ||
Longitude | 0.6582 | 0.1046 | 0.1765 | 0.6240 | 0.3406 | 0.4603 | 0.9768 | 0.7852 | 0.6414 | 0.4045 | |
Latitude | 0.4806 | 0.0256 | 0.1008 | 0.5132 | 0.3561 | 0.3755 | 0.6668 | 0.7215 | 0.3214 | 0.3260 | |
Elevation | 0.7636 | 0.1150 | 0.1093 | 0.8806 | 0.7688 | 0.7837 | 0.8053 | 0.9002 | 0.7417 | 0.7506 | |
Average monthly rainfall | January | 0.3197 | 0.4796 | 0.5404 | 0.2984 | 0.4240 | 0.3530 | 0.3445 | 0.2358 | 0.5877 | 0.3984 |
February | 0.3681 | 0.5415 | 0.5799 | 0.3155 | 0.4837 | 0.4429 | 0.3097 | 0.2851 | 0.5078 | 0.5936 | |
March | 0.3438 | 0.5556 | 0.5726 | 0.4048 | 0.4610 | 0.4513 | 0.3644 | 0.3543 | 0.4520 | 0.5175 | |
April | 0.2817 | 0.4985 | 0.4060 | 0.2883 | 0.2691 | 0.3447 | 0.2279 | 0.2684 | 0.3012 | 0.3315 | |
May | 0.3257 | 0.5299 | 0.4270 | 0.3777 | 0.2608 | 0.3329 | 0.4092 | 0.2680 | 0.2975 | 0.3670 | |
June | 0.3446 | 0.2578 | 0.2759 | 0.4483 | 0.2356 | 0.3174 | 0.5403 | 0.2651 | 0.3430 | 0.4590 | |
July | 0.4423 | 0.1983 | 0.2441 | 0.5927 | 0.2765 | 0.3589 | 0.6043 | 0.3653 | 0.4742 | 0.5490 | |
August | 0.2833 | 0.1837 | 0.2197 | 0.5957 | 0.2626 | 0.3128 | 0.4148 | 0.2476 | 0.3009 | 0.4912 | |
September | 0.3041 | 0.4688 | 0.4408 | 0.3927 | 0.3009 | 0.3613 | 0.3561 | 0.2203 | 0.3655 | 0.4860 | |
October | 0.4040 | 0.3501 | 0.3811 | 0.3445 | 0.3192 | 0.3827 | 0.3469 | 0.3007 | 0.3461 | 0.4283 | |
November | 0.2871 | 0.3532 | 0.4962 | 0.3086 | 0.3615 | 0.3209 | 0.2984 | 0.2276 | 0.3648 | 0.3564 | |
December | 0.2593 | 0.3984 | 0.4038 | 0.2600 | 0.3633 | 0.4133 | 0.3635 | 0.3224 | 0.3810 | 0.4007 | |
Annual rainfall | 0.3286 | 0.4104 | 0.4169 | 0.3749 | 0.3246 | 0.3627 | 0.3683 | 0.2782 | 0.3720 | 0.4310 | |
Standard deviation of annual rainfall | 0.3377 | 0.5635 | 0.4935 | 0.3289 | 0.3004 | 0.3365 | 0.3209 | 0.2699 | 0.2519 | 0.3057 |
Group Number | Station List | Number of Grouped Stations |
---|---|---|
1 | S16, S25, S27, S36, S37, S52, S60, S61, S65, S71, S86, S89, S101, S102, S126, S138, S144, S149, S150, S156, S166 | 21 |
2 | S29, S31, S40, S76, S96, S122, S158 | 7 |
3 | S140 | 1 |
4 | S15, S91, S105, S118, S131 | 5 |
5 | S85, S97, S109 | 3 |
6 | S80 | 1 |
7 | S63, S88, S123 | 3 |
8 | S9, S18, S28, S41, S45, S108, S142, S151, S174 | 9 |
9 | S44, S64, S77, S78, S124, S130, S159, S163 | 8 |
10 | S32, S42, S62, S67, S116, S129, S147, S154, S162 | 9 |
11 | S10, S13, S22, S81 | 4 |
12 | S4, S8, S55 | 3 |
13 | S1, S33, S49, S53, S54, S161 | 6 |
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Garrido-Arévalo, A.R.; Agudelo-Otálora, L.M.; Obregón-Neira, N.; Garrido-Arévalo, V.; Quiñones-Bolaños, E.E.; Naraei, P.; Mehrvar, M.; Bustillo-Lecompte, C.F. Application of Artificial Neural Network and Information Entropy Theory to Assess Rainfall Station Distribution: A Case Study from Colombia. Water 2020, 12, 1973. https://doi.org/10.3390/w12071973
Garrido-Arévalo AR, Agudelo-Otálora LM, Obregón-Neira N, Garrido-Arévalo V, Quiñones-Bolaños EE, Naraei P, Mehrvar M, Bustillo-Lecompte CF. Application of Artificial Neural Network and Information Entropy Theory to Assess Rainfall Station Distribution: A Case Study from Colombia. Water. 2020; 12(7):1973. https://doi.org/10.3390/w12071973
Chicago/Turabian StyleGarrido-Arévalo, Augusto Rafael, Luis Mauricio Agudelo-Otálora, Nelson Obregón-Neira, Victor Garrido-Arévalo, Edgar Eduardo Quiñones-Bolaños, Parisa Naraei, Mehrab Mehrvar, and Ciro Fernando Bustillo-Lecompte. 2020. "Application of Artificial Neural Network and Information Entropy Theory to Assess Rainfall Station Distribution: A Case Study from Colombia" Water 12, no. 7: 1973. https://doi.org/10.3390/w12071973
APA StyleGarrido-Arévalo, A. R., Agudelo-Otálora, L. M., Obregón-Neira, N., Garrido-Arévalo, V., Quiñones-Bolaños, E. E., Naraei, P., Mehrvar, M., & Bustillo-Lecompte, C. F. (2020). Application of Artificial Neural Network and Information Entropy Theory to Assess Rainfall Station Distribution: A Case Study from Colombia. Water, 12(7), 1973. https://doi.org/10.3390/w12071973