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Application of ANN and traditional ML algorithms in modelling compost production under different climatic conditions

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Abstract

Plentiful and diverse Organic wastes such as green, food, pruning and landscaping waste necessitate upon effective and efficient recycling strategies such as composting. In this article, for the first time, machine learning (ML) models have been applied to model compost generation rate as a function of climatic parameters and organic waste content. The data size contained approximately 864 sample points records of the meteorological parameters (Modern-Era Retrospective Analysis), organic waste (Central Pollution Control Board) and compost yields (Open Government Data) data for 2010–2021. The modelling efforts involved the consideration of MLP and traditional ML algorithms namely k-nearest neighbour (kNN), gradient boosting (GB) and random forest (RF) for prediction and autoregressive integrated moving average (ARIMA) model supplemented ML models for long-term forecasting of the compost generation rate. Model validation resulted in an RMSE of 0.757 and R2 of 0.99 for GB model, and a correlation index of 0.68 between observed and predicted values to thereby outperform all other models. However, forecasted data for ten years after the predicted outcomes resulted in the best performance of ARIMA–MLP model with a standard error of 21.1152 and a CP yield of 74,958 kg. Thereby, the findings affirm upon the evidence for the limitations of the broader application of the empirical approaches and the feasibility of ML algorithms as a potential reconstruction technique for developing robust and accurate region-specific compost prediction and forecasting models to assist integrated circular agricultural system development for a sustainable global future.

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Data availability

The datasets used and analysed in this study are mentioned as below, and the rest of the predicted datasets will be made available on reasonable request.

[dataset] https://www.soda-pro.com/web-services/meteo-data/merra.

[dataset] https://cpcb.nic.in/uploads/MSW/Waste_generation_Composition.pdf.

[dataset] https://data.gov.in/search?title=compost.

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Acknowledgements

The authors thankfully acknowledge the Centre for the Environment, Indian Institute of Technology Guwahati, India, Guwahati Municipal Corporation (GMC), Assam, India, and Central Pollution Control Board (CPCB).

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The authors declare the following financial interests/personal relationships which may be considered as potential competing interests.

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Authors and Affiliations

  1. Centre for the Environment, Indian Institute of Technology Guwahati, Assam, India

    Tinka Singh

  2. Department of Chemical Engineering, Indian Institute of Technology Guwahati, Assam, India

    Ramagopal V. S. Uppaluri

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  1. Tinka Singh
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Contributions

TS contributed to data extraction, data pre-processing, investigation, modelling, writing—original draft preparation, writing—review and editing, and validation. RVSU contributed to conceptualization, supervision, validation, visualization, and writing—review and editing.

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Correspondence to Ramagopal V. S. Uppaluri.

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Glossary

ANN

An approach based on artificial neural networks (ANN) is used to detect patterns in data or to simulate dynamic interactions between inputs and outputs

ARIMA

A technique called ARIMA based on statistical concept of correlation, where past data points influence subsequent data points, is used to anticipate or predict future events using a previous time series

ARIMA–MLP

In order to build series linear/nonlinear combination models, the hybrid ARIMA–MLP models are frequently employed linear and nonlinear models

CNN

A deep learning neural network designed for processing organised arrays of data is known as a convolutional neural network

GB

A tree-based machine learning method called gradient boosting is employed for classification and regression applications

kNN

One of the most basic machine learning algorithms, kNN, is mostly employed for classification and regression

ML

Machine learning algorithms construct a model from sample data, referred to as training data, in order to make predictions or decisions without being explicitly programmed to do so

MLP

MLP is a term that can be used ambiguously to refer to any feedforward ANN or specifically to networks made up of many layers of perceptrons

RF

Bagging is an ensemble approach that combines the predictions from all models after fitting them to various subsets of a training dataset

CPCB

Municipal solid waste data collected from Central Pollution Control Board of India (a statutory organization under the Ministry of Environment, Forest and Climate Change)

MERRA-2

Modern-Era Retrospective analysis for Research and Applications provided the relevant meteorological data

OGD

Open Government Data Platform India or data.gov.in is a platform for supporting Open data initiative of Government of India

CP

The dependent variable to predict the Compost Production

T

Meteorological parameter: Temperature

RH

Meteorological parameter: Relative Humidity

P

Meteorological parameter: Precipitation

WS

Meteorological parameter: Wind speed

OW

Organic waste found in municipal solid waste include food, paper, wood, sewage sludge, and yard waste

CL

Confidence Levels is the mean of an estimate +/− the variation in the estimate

MAE

Mean absolute error represents the average of the absolute difference between the actual and predicted values in the dataset

MSE

A measurement of how close a fitted line is to plotted data points

RMSE

The square root of the mean square, or the quadratic mean

R 2

The coefficient of determination or R-squared represents the proportion of the variance in the dependent variable which is explained by the linear regression model

SSE

Sum of squared error is the difference between the observed value and the predicted value

IOA

Index of agreement for the models have been calculated as the ratio between the MSE and the potential error

PO

Parameter optimization in ML is the values when the learning algorithm can change independently as it learns and these values are affected by the choice of hyperparameters we provide

C/N

The ratio of the weight of organic carbon to the weight of total nitrogen in a soil or in organic material

MC

Moisture content (or water content) refers to the weight of the water contained in a compost pile

MSW

Municipal solid waste are made up of waste, organics, and recyclable materials, with the municipality overseeing its disposal.

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Singh, T., Uppaluri, R.V.S. Application of ANN and traditional ML algorithms in modelling compost production under different climatic conditions. Neural Comput & Applic 35, 13465–13484 (2023). https://doi.org/10.1007/s00521-023-08404-4

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