Summer Precipitation Forecast Using an Optimized Artificial Neural Network with a Genetic Algorithm for Yangtze-Huaihe River Basin, China
<p>Study area of the YHRB denoted by the land portion in the red box.</p> "> Figure 2
<p>Schematic diagram of the three-layer BPNN structure, where X and Y are the input and output layer variables, respectively. The input and output layers are connected, separately, by the neuron with the hidden layer as the node.</p> "> Figure 3
<p>Correlation coefficient between a factor of an antecedent month in the previous winter/spring and the precipitation of a month in summer (e.g., “12-6” means the correlation between a December factor and the following June precipitation).</p> "> Figure 4
<p>Sigmoid activation function used in the BPNN, where <span class="html-italic">a</span> is taken as 1 for practice in this study (i.e., shown in the red line).</p> "> Figure 5
<p>Schematic diagram of the calculation process, in which the blue part represents the standard BPNN, while the yellow part illustrates the optimization process based on the standard BPNN using the genetic algorithm.</p> "> Figure 6
<p>Box diagrams of the maximum, minimum and average errors of predicted summer precipitation using the four methods for the YHRB, where (<b>a</b>–<b>c</b>) are for MAPE, MAE and RMSE, respectively.</p> "> Figure 7
<p>Spatial distributions of MAPE by GABP, where the six rows are for models using the factors of December, January, February, March, April and May (from top to bottom; e.g., (<b>a</b>–<b>d</b>) are for December factors), with four columns for the forecasted precipitations of June, July, August and summer (from left to right; e.g., (<b>b</b>,<b>f</b>,<b>j</b>,<b>n</b>,<b>r</b>,<b>v</b>) are for July precipitations), respectively.</p> "> Figure 8
<p>Spatial distribution of identical signs of GABP forecast precipitation anomalies, where rows and columns are the same as those in <a href="#atmosphere-13-00929-f007" class="html-fig">Figure 7</a> for various months of factors and months/season of precipitation forecast (e.g., (<b>e</b>–<b>h</b>) are for January factors, and (<b>m</b>–<b>p</b>) are for identical signs of precipitations of June, July, August and summer, respectively), respectively, and the grids with identical signs of forecast and observed anomalies are marked with crosses (i.e., “+”), through which the AR is calculated.</p> "> Figure 9
<p>Spatial distributions of the “best” summer precipitation forecasts using January factors by GABP over the test periods [i.e., eight-month (summer) means over 2012–2019], where the first and second rows indicate forecasted precipitations with absolute values and corresponding observations, respectively (e.g., (<b>a</b>–<b>d</b>) are for forecasted June, July, August and summer precipitations, respectively, with (<b>e</b>–<b>h</b>) corresponding to the observations, respectively); the third and fourth rows are the same as the first and second rows, respectively, but for anomalies; the fifth row represents MAPE distributions; the columns from left to right show results for June, July, August and summer, respectively.</p> ">
Abstract
:1. Introduction
2. Data and Model Construction
2.1. Data
2.2. Model Introduction
2.2.1. Backpropagation Neural Network
2.2.2. BPNN Optimized by Genetic Algorithm
2.2.3. Multiple Linear Regression
2.2.4. Support Vector Machine
2.3. Modeling Process
2.3.1. Factor Selection
2.3.2. Procedure of BPNN Forecasts
- (a)
- Standard BPNN modeling process
- (b)
- Activation function selection and parameter tuning
2.3.3. GABP Calculation Process
2.3.4. Multiple Linear Regression Calculation Process
2.4. Model Evaluation Measures
3. Predicted Results
3.1. Comparison of Basin-Averaged Measures among the Four Methods
3.2. GABP-Produced Spatial Distributions of the Measures
3.3. Spatial Distributions of Forecasted Summer Precipitation by the Best GABP Model
4. Concluding Remarks
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Appendix A. Predictors/Indices Used for Precipitation Forecast with Available Resources Listed
SOI | Southern Oscillation Index; NOAA Climate Prediction Center (CPC) |
PNA | Pacific North America Index; NOAA Climate Prediction Center (CPC) |
NAO | North Atlantic Oscillation Index; NOAA Climate Prediction Center (CPC) |
ONI | Ocean Nino Index; NOAA Climate Prediction Center (CPC) |
NTA | Tropical North Atlantic Sea Temperature Index; ERSST V3b data set |
CAR | Caribbean Sea Temperature Index; NOAA ERSST V3b data set |
ENSO precipitation index | ENSO precipitation index; http://precip.gsfc.nasa.gov/ESPItable.html, accessed on 6 June 2021 |
BEST | Bivariate ENSO time series; NOAA OI V2 SST data set |
Nino3 | Tropical East Pacific Sea Temperature; NOAA ERSST V5 data set |
Nino4 | Tropical Central Pacific Sea Temperature; NOAA ERSST V5 data set |
Nino1+2 | Extreme eastern tropical Pacific sea temperature; NOAA ERSST V5 data set |
Nino3+4 | The sea temperature of the tropical central and eastern Pacific Ocean; NOAA ERSST V5 |
TNA | Tropical North Atlantic Index; HadISST and NOAA OI 1° × 1° data set |
TSA | Tropical South Atlantic Index, from HadISST and NOAA OI 1° × 1° data set |
Atlantic Tripole SST EOF | The first EOF mode of the tropical Atlantic SST |
WP | Western Pacific Index; NOAA Climate Prediction Center (CPC) |
QBO | Quasi-Biennial oscillation; zonal average of the equatorial 30mb zonal wind calculated by NCEP/NCAR reanalysis |
WHWP | Monthly anomaly of the western hemisphere warm pool area above 28.5 degrees; HadISST and NOAA OI datasets |
PDO | Pacific Interdecadal Oscillation; NOAA Climate Prediction Center (CPC) |
NOI | Arctic Oscillation Index; NOAA Climate Prediction Center (CPC) |
NP | North Pacific Oscillation; NOAA Climate Prediction Center (CPC) |
EP | East Pacific Oscillation; NOAA Climate Prediction Center (CPC) |
AAO | Antarctic Oscillation; NOAA Climate Prediction Center (CPC) |
Pacific Warmpool SST EOF | first mode of Pacific Warmpool; NOAA OI 1° × 1° data set |
Tropical Pacific SST EOF | Tropical Pacific SST EOF first mode; NOAA OI 1° × 1° data set |
TNI | El-Niño Evolution Index; http://psl.noaa.gov/Pressure/Timeseries/TNI/, accessed on 6 June 2021 |
AMO | Atlantic Multidecadal Oscillation long version; Kalplan sea surface temperature |
AMM | Atlantic meridian model; NOAA Climate Prediction Center (CPC) |
Indian | Rainfall Index in Central India; http://www.tropmet.res.in/, accessed on 6 June 2021 |
Sahel | Sahel regional precipitation index; http://jisao.washington.edu/data_sets/sahel/Mitchell, accessed on 6 June 2021 |
NAO | North Atlantic Oscillation; University of East Anglia Climatic Research Unit (CRU) |
MEI | Multivariate ENSO Index; NOAA PSL data |
AO | Arctic Oscillation; NOAA Climate Prediction Center (CPC) |
Brazil | Precipitation anomalies in northeastern Brazil; http://jisao.washington.edu/data_sets/brazil/, accessed on 6 June 2021 |
Solar Flux | from ftp://ftp.ngdc.noaa.gov/STP/space-weather/solar-data/, accessed on 6 June 2021 |
Hurricane activity | Monthly Atlantic hurricanes and tropical storms; Colorado State University |
Global Mean Land/Ocean Temperature | NASA Goddard Institute for Space Studies (GISS) |
SW Monsoon Region rainfall | Average rainfall in Arizona and New Mexico; the climate department of NCDC |
MDRSST | MDR minus tropical sea temperature observation anomalies, PSL from NOAA |
AEEP | Accumulated Energy Eastern Pacific; NOAA Climate Prediction Center (CPC) |
AEAO | Accumulated Energy Atlantic Ocean; NOAA Climate Prediction Center (CPC) |
Atlantic Tripole EOF | The first EOF mode of tropical Pacific SST; NOAA Climate Prediction Center (CPC) |
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Measure | Method | M12-6 | M12-7 | M12-8 | M1-6 | M1-7 | M1-8 | M2-6 | M2-7 | M2-8 |
---|---|---|---|---|---|---|---|---|---|---|
(M3-6) | (M3-7) | (M3-8) | (M4-6) | (M4-7) | (M4-8) | (M5-6) | (M5-7) | (M5-8) | ||
MAPE/% | BPNN | 108.3 | 94.1 | 73.4 | 84.6 | 57.7 | 94.3 | 79.7 | 94 | 89 |
(98.1) | (86.6) | (92.7) | (80.7) | (62.7) | (88.8) | (71.7) | (78.6) | (109.5) | ||
GABP | 19.7 | 27.4 | 15.9 | 29.6 | 13.9 | 18.8 | 23.8 | 21.3 | 16 | |
(31.5) | (19.9) | (17.1) | (27.6) | (12.9) | (20.4) | (31.3) | (18.3) | (20.6) | ||
SVM | 43.8 | 58.6 | 32.3 | 51.3 | 61.9 | 39 | 51.5 | 45.4 | 41.5 | |
(52.8) | (45.6) | (43.1) | (52) | (33.5) | (44.3) | (46.5) | (61.5) | (44.9) | ||
MLR | 51.9 | 76.6 | 69.5 | 71.2 | 45.7 | 57.5 | 63.5 | 72 | 121 | |
(98.6) | (160.1) | (87.7) | (60) | (171.8) | (63.9) | (81.3) | (79.9) | (214.3) | ||
MAE/mm | BPNN | 130.3 | 115.7 | 100.4 | 122.4 | 112.7 | 108.2 | 107.7 | 132.2 | 109.7 |
(136.3) | (127.9) | (106.2) | (123.9) | (112.4) | (107.4) | (104.4) | (116.8) | (121.7) | ||
GABP | 25 | 31.6 | 20.2 | 38.6 | 23.3 | 21.4 | 31.8 | 29.4 | 18.8 | |
(42.5) | (29.7) | (18.8) | (41.2) | (21.8) | (24.1) | (32.9) | (26.8) | (23.8) | ||
SVM | 50.4 | 65.4 | 46.2 | 71.7 | 61.2 | 43.5 | 65.4 | 68.1 | 49.2 | |
(80.4) | (66.3) | (47.4) | (78.3) | (60) | (52.8) | (69) | (62.1) | (53.4) | ||
MLR | 66 | 100.8 | 88.4 | 134.9 | 80.6 | 79.9 | 83.3 | 123.3 | 143.8 | |
(167.6) | (253.1) | (102.2) | (109.3) | (458.6) | (91.2) | (153.6) | (144.5) | (264.8) | ||
RMSE/mm | BPNN | 169.4 | 140 | 120.5 | 154.9 | 137 | 126.8 | 131.6 | 152.6 | 130.7 |
(169.5) | (150.8) | (128.9) | (148.4) | (136) | (126.3) | (130.6) | (138.3) | (142.1) | ||
GABP | 30.9 | 38.8 | 24.6 | 47.2 | 28.7 | 26.1 | 38.5 | 35.5 | 23.1 | |
(51.8) | (36.3) | (22.8) | (50.6) | (26.8) | (29.5) | (40.5) | (32.8) | (28.9) | ||
SVM | 59.4 | 79.8 | 60.6 | 87 | 76.8 | 52.8 | 80.4 | 81 | 60.8 | |
(97.5) | (81.0) | (58.2) | (96.3) | (73.8) | (65.4) | (85.5) | (77.4) | (66.0) | ||
MLR | 81.9 | 132.8 | 113.8 | 202.7 | 99.2 | 99.0 | 103.6 | 156.2 | 202.1 | |
(250.9) | (426.6) | (128.2) | (146.9) | (961.8) | (110.4) | (258.9) | (214.5) | (74.6) | ||
AR/% | BPNN | 49.4 | 82.4 | 78.6 | 65.8 | 98.2 | 78.6 | 61.4 | 34.7 | 35.0 |
(75.1) | (37.4) | (40.9) | (75.1) | (38.6) | (35.1) | (72.5) | (98.2) | (35.3) | ||
GABP | 27.8 | 78.6 | 74.6 | 77.5 | 93.8 | 77.2 | 68.1 | 21.6 | 41.2 | |
(81.6) | (21.7) | (48.5) | (81.6) | (34.5) | (41.5) | (81.6) | (92.1) | (41.8) | ||
SVM | 35.3 | 74.5 | 49.7 | 61 | 82.9 | 62.6 | 54.6 | 55.3 | 44.2 | |
(66.0) | (43.6) | (40.9) | (70.5) | (48.1) | (33.5) | (74.7) | (82.3) | (42.9) | ||
MLR | 38.6 | 70.7 | 54.6 | 63.7 | 86.2 | 58.7 | 53.5 | 51.1 | 41.5 | |
(62.0) | (41.8) | (39.2) | (67.1) | (52.0) | (36.8) | (70.5) | (86.8) | (45.3) |
Measure | M12-S | M1-S | M2-S | M3-S | M4-S | M5-S |
---|---|---|---|---|---|---|
MAPE/% | 9.1 | 4.7 | 21.5 | 18.5 | 18.0 | 7.4 |
AR/% | 74.0 | 88.3 | 37.7 | 51.5 | 57.9 | 78.4 |
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Zhang, Z.-C.; Zeng, X.-M.; Li, G.; Lu, B.; Xiao, M.-Z.; Wang, B.-Z. Summer Precipitation Forecast Using an Optimized Artificial Neural Network with a Genetic Algorithm for Yangtze-Huaihe River Basin, China. Atmosphere 2022, 13, 929. https://doi.org/10.3390/atmos13060929
Zhang Z-C, Zeng X-M, Li G, Lu B, Xiao M-Z, Wang B-Z. Summer Precipitation Forecast Using an Optimized Artificial Neural Network with a Genetic Algorithm for Yangtze-Huaihe River Basin, China. Atmosphere. 2022; 13(6):929. https://doi.org/10.3390/atmos13060929
Chicago/Turabian StyleZhang, Zhi-Cheng, Xin-Min Zeng, Gen Li, Bo Lu, Ming-Zhong Xiao, and Bing-Zeng Wang. 2022. "Summer Precipitation Forecast Using an Optimized Artificial Neural Network with a Genetic Algorithm for Yangtze-Huaihe River Basin, China" Atmosphere 13, no. 6: 929. https://doi.org/10.3390/atmos13060929