ArcGIS Developer

ArcGIS API for Python

Forecasting Air Temperature in California using ResCNN model


A rise in air temperature is directly correlated with Global warming and change in climatic conditions and is one of the main factors in predicting other meteorological variables, like streamflow, evapotranspiration, and solar radiation. As such, accurate forecasting of this variable is vital in pursuing the mitigation of environmental and economic destruction. Including the dependency of air temperature in other variables, like wind speed or precipitation, helps in deriving more precise predictions. In this study, the deep learning TimeSeriesModel from arcgis.learn is used to predict monthly air temperature for two years at a ground station at the Fresno Yosemite International Airport in California, USA. The dataset ranges from 1948-2015. Data from January 2014 to November 2015 is used to validate the quality of the forecast.

Univariate time series modeling is one of the more popular applications of time series analysis. This study includes multivariate time series analysis, which is a bit more convoluted, as the dataset contains more than one time-dependent variable. The TimeSeriesModel from arcgis.learn includes backbones, such as InceptionTime, ResCNN, ResNet and FCN, which do not need fine-tuning of multiple hyperparameters before fitting the model. Here is the schematic flow chart of the methodology:

Importing libraries

In [1]:
%matplotlib inline
import matplotlib.pyplot as plt

import numpy as np
import pandas as pd
from pandas.plotting import autocorrelation_plot as aplot

from sklearn.preprocessing import MinMaxScaler
from sklearn.model_selection import train_test_split
from sklearn.metrics import r2_score
import sklearn.metrics as metrics

from arcgis.gis import GIS
from arcgis.learn import TimeSeriesModel, prepare_tabulardata
from arcgis.features import FeatureLayer, FeatureLayerCollection

Connecting to your GIS

In [2]:
gis = GIS('home')

Accessing & visualizing the dataset

The data used in this sample study is a multivariate monthly time series dataset recorded at a ground station in the Fresno Yosemite International Airport, California, USA. It ranges from January 1948 to November 2015.

In [3]:
# Location of the ground station
location ="Fresno Yosemite International California", zoomlevel=12)