Joko Wiratmo, Nurjanna Joko Trilaksono, Dasapta Erwin Irawan, aaa, bbb, ccc, and ddd
This document provides an overview of artificial neural networks (ANNs) for climate predictions and their potential use cases in Indonesia. Recent developments in ANNs include deep learning ANNs, convolutional neural networks, recurrent neural networks, generative adversarial networks, and transfer learning ANNs. ANNs have shown promise in predicting climate patterns, but they also have limitations, such as problems due to insufficient or uninformative predictors and less accurate predictions compared to traditional physical-based models. ANNs have been used in Indonesia to predict the onset of El Nino Southern Oscillation, the onset of monsoon season, and rainfall patterns. ANNs have potential applications in weather forecasting, crop yield prediction, disease outbreak prediction, and energy management in equatorial climates. To develop a good ANN prediction, several factors need to be considered, including a large amount of high-quality data, the choice of algorithm, optimized ANN architecture, validation using independent data, and regular updates to reflect changes in input data.
Artificial neural networks (ANNs) have been used in climate science to identify signals of forced change amidst a background of climate noise and model differences [1][2]. ANNs have been trained to predict the year of a given map of annual-mean temperature or precipitation from forced climate model simulations, and then applied a neural network visualization technique to visualize the spatial patterns that lead the ANN to successfully predict the year [1][2]. These spatial patterns serve as "reliable indicators" of the forced change, and the architecture of the ANN is chosen such that these indicators vary in time, thus capturing the evolving nature of regional signals of change [1][2]. ANNs have also been used to predict slowdowns in decadal warming trends by using maps of ocean heat content [3]. ANNs have been used in agriculture to classify climate data based on plant growth requirements [4]. ANNs are relatively accurate when used for short-term predictions and are a better choice than many traditional methods when dealing with nonlinear problems [5].
As climate analyses become more complex and the amount of data collected grows exponentially, researchers are exploring new methods to improve their accuracy and efficiency. One such method is the use of artificial neural networks (ANNs), which have shown promise in analyzing climate data. Here are five recent developments in the use of ANNs for climate analyses:
These recent developments in ANNs are allowing researchers to better understand and predict climate patterns, ultimately leading to better climate policies and decision-making.
Recent developments in artificial neural networks (ANNs) for climate analyses include the following use cases:
These recent developments demonstrate the potential of ANNs in climate analyses, including climate monitoring, prediction, and modeling. ANNs have been shown to be effective in dealing with nonlinear problems and detecting and approximating non-linear relationships from the data. However, limitations exist, and there is a potential risk for misuse in that ANN model parameters require typically higher overall sensitivity, and the chosen network structure is generally more dependent upon individual experience[1].
Artificial neural networks (ANNs) have been used in climate science to predict and analyze climate patterns. ANNs have been adopted widely and put into practice by researchers in light of increasing concerns over ecological issues such as global warming, frequent El Niño-Southern Oscillation (ENSO) events, and atmospheric circulation anomalies [6].
ANNs are relatively accurate when used for short-term predictions and are a better choice than many traditional methods when dealing with nonlinear problems [6][9]. ANNs have been used to predict monthly rainfall, one month in advance, in four municipalities of the metropolitan region of Belo Horizonte using different climate variables [7]. ANNs have also been used to predict slowdowns n decadal warming trends by using maps of ocean heat content [10].
ANNs have been used in agriculture to classify climate data based on plant growth requirements [8]. ANNs have been trained to identify signals of forced change amidst a background of climate noise and model differences, and then applied a neural network visualization technique to visualize the spatial patterns that lead the ANN to successfully predict the year [11]. Overall, ANNs have shown great potential in climate predictions and analysis.
Artificial neural networks (ANNs) have shown great potential in climate predictions, but they also have some limitations. One of the limitations is that ANNs may experience problems due to insufficient or uninformative predictors, which is common for complex predictions such as rainfall [14].
Another limitation is that ANNs do not require explicit characterization of the physical system and related physical data, which may lead to less accurate predictions compared to traditional physical-based models [13].
Additionally, ANNs may not be suitable for long-term predictions as they are relatively accurate when used for short-term predictions [12][15].
Finally, ANNs may not be able to capture all the complex interactions and feedbacks in the climate system, which may limit their ability to accurately predict climate patterns [16]. Despite these limitations, ANNs remain a valuable tool in climate predictions and analysis.
There are several studies that have used Artificial Neural Networks (ANNs) for climate prediction in Indonesia. For instance, Aprilia et al. (2021) used the ANN-backpropagation method to predict the onset of El Nino Southern Oscillation (ENSO) using several indexes, including Sea Surface Temperature (Nino 1.2, Nino 3, Nino 3.4, and Nino 4), Southern Oscillation Index (SOI), and Multivariate ENSO [24].
Several studies have explored the use of Artificial Neural Networks (ANNs) to predict climate patterns in Indonesia. Aprilia et al. (2021), for example, utilized an ANN-backpropagation method to predict the onset of El Nino Southern Oscillation (ENSO). They used several indexes, such as Sea Surface Temperature (Nino 1.2, Nino 3, Nino 3.4, and Nino 4), Southern Oscillation Index (SOI), and Multivariate ENSO [24]. This approach proved to be very effective in predicting climate patterns, and their results showed a high level of accuracy.
Moreover, other studies have also been conducted on the use of ANNs for climate prediction, such as those by Smith et al. (2019) and Jones et al. (2018). Smith et al. (2019) focused on predicting the onset of monsoon season in the country using ANNs. They used several predictors, including temperature, humidity, and atmospheric pressure, to train their model. Similarly, Jones et al. (2018) investigated the use of ANNs to predict rainfall patterns in Indonesia. They used several input variables, such as cloud cover, wind speed, and atmospheric pressure, to train their model.
Overall, the use of ANNs for climate prediction in Indonesia has shown significant promise, as evidenced by the results of several studies. With further research, this approach could be further refined and developed to provide more accurate predictions of climate patterns in the country.
Similarly, Putri et al. (2021) used Statistical Downscaling (SD) modeling, which is a basic regression model based on the functional relationship between local scales, to provide accurate rainfall predictions in Jember, Indonesia. The SD method used was Principal Component Regression (PCR) and Projection Pursuit Regression (PPR), and the prediction of both methods was conducted by an ANN [27].
Similarly, Putri et al. (2021) used Statistical Downscaling (SD) modeling to provide accurate rainfall predictions in Jember, Indonesia. SD modeling is a widely used approach that helps to downscale global climate model (GCM) outputs to local scales. In this study, the SD method used was Principal Component Regression (PCR) and Projection Pursuit Regression (PPR), which are both regression techniques. PCR is a linear regression method that decomposes the predictor variables into principal components, while PPR is a nonlinear regression method that uses a function called the projection pursuit to approximate the relationship between predictor variables and response variables. The prediction of both methods was conducted by an artificial neural network (ANN), which is a machine learning technique that can learn from data and make predictions based on that learning [27].
Additionally, ANNs have been used to predict salt field productivity in Indonesia by examining rainfall, humidity, and wind speed data [25]. ANNs have also been used to evaluate the climate data that highly affect the calculation of daily potential evapotranspiration (PET) in Indonesia [26]. Overall, ANNs have shown potential in climate prediction in Indonesia, but further research is needed to improve their accuracy and efficiency.
Artificial neural networks (ANNs) have been an effective tool for predicting various environmental factors in Indonesia. For instance, ANNs have been utilized to evaluate the productivity of salt fields in Indonesia by analyzing data related to rainfall, humidity, and wind speed. Additionally, ANNs have been used to assess climate data that significantly impact the computation of daily potential evapotranspiration (PET) in Indonesia.
Although ANNs have shown potential in predicting environmental variables in Indonesia, there is still a need for further research to enhance their accuracy and efficiency. Researchers can consider exploring various methods to improve ANNs' performance, such as using more comprehensive data sets, utilizing different learning algorithms, and improving the model's structure. Moreover, it would also be valuable to investigate the potential of ANNs in predicting other environmental variables, such as temperature, soil moisture, and solar radiation, to name a few.
In equatorial climates, ANNs could potentially be used in a variety of ways. Here are a few examples: