Smart models to improve agrometeorological estimations and predictions

La población mundial, en continuo crecimiento, alcanzará de forma estimada los 9,7 mil millones de habitantes en el 2050. Este incremento, combinado con el aumento en los estándares de vida y la situación de emergencia climática (aumento de la temperatura, intensificación del ciclo del agua, etc.) nos enfrentan al enorme desafío de gestionar de forma sostenible los cada vez más escasos recursos disponibles. El sector agrícola tiene que afrontar retos tan importantes como la mejora en la gestión de los recursos naturales, la reducción de la degradación medioambiental o la seguridad alimentaria y nutricional. Todo ello condicionado por la escasez de agua y las condiciones de aridez: factores limitantes en la producción de cultivos. Para garantizar una producción agrícola sostenible bajo estas condiciones, es necesario que todas las decisiones que se tomen estén basadas en el conocimiento, la innovación y la digitalización de la agricultura de forma que se garantice la resiliencia de los agroecosistemas, especialmente en entornos áridos, semi-áridos y secos sub-húmedos en los que el déficit de agua es estructural. Por todo esto, el presente trabajo se centra en la mejora de la precisión de los actuales modelos agrometeorológicos, aplicando técnicas de inteligencia artificial. Estos modelos pueden proporcionar estimaciones y predicciones precisas de variables clave como la precipitación, la radiación solar y la evapotranspiración de referencia. A partir de ellas, es posible favorecer estrategias agrícolas más sostenibles, gracias a la posibilidad de reducir el consumo de agua y energía, por ejemplo. Además, se han reducido el número de mediciones requeridas como parámetros de entrada para estos modelos, haciéndolos más accesibles y aplicables en áreas rurales y países en desarrollo que no pueden permitirse el alto costo de la instalación, calibración y mantenimiento de estaciones meteorológicas automáticas completas. Este enfoque puede ayudar a proporcionar información valiosa a los técnicos, agricultores, gestores y responsables políticos de la planificación hídrica y agraria en zonas clave. Esta tesis doctoral ha desarrollado y validado nuevas metodologías basadas en inteligencia artificial que han ser vido para mejorar la precision de variables cruciales en al ámbito agrometeorológico: precipitación, radiación solar y evapotranspiración de referencia. En particular, se han modelado sistemas de predicción y rellenado de huecos de precipitación a diferentes escalas utilizando redes neuronales. También se han desarrollado modelos de estimación de radiación solar utilizando exclusivamente parámetros térmicos y validados en zonas con características climáticas similares a lugar de entrenamiento, sin necesidad de estar geográficamente en la misma región o país. Analógamente, se han desarrollado modelos de estimación y predicción de evapotranspiración de referencia a nivel local y regional utilizando también solamente datos de temperatura para todo el proceso: regionalización, entrenamiento y validación. Y finalmente, se ha creado una librería de Python de código abierto a nivel internacional (AgroML) que facilita el proceso de desarrollo y aplicación de modelos de inteligencia artificial, no solo enfocadas al sector agrometeorológico, sino también a cualquier modelo supervisado que mejore la toma de decisiones en otras áreas de interés.The world population, which is constantly growing, is estimated to reach 9.7 billion people in 2050. This increase, combined with the rise in living standards and the climate emergency situation (increase in temperature, intensification of the water cycle, etc.), presents us with the enormous challenge of managing increasingly scarce resources in a sustainable way. The agricultural sector must face important challenges such as improving natural resource management, reducing environmental degradation, and ensuring food and nutritional security. All of this is conditioned by water scarcity and aridity, limiting factors in crop production. To guarantee sustainable agricultural production under these conditions, it is necessary to based all the decision made on knowledge, innovation, and the digitization of agriculture to ensure the resilience of agroecosystems, especially in arid, semi-arid, and sub-humid dry environments where water deficit is structural. Therefore, this work focuses on improving the precision of current agrometeorological models by applying artificial intelligence techniques. These models can provide accurate estimates and predictions of key variables such as precipitation, solar radiation, and reference evapotranspiration. This way, it is possible to promote more sustainable agricultural strategies by reducing water and energy consumption, for example. In addition, the number of measurements required as input parameters for these models has been reduced, making them more accessible and applicable in rural areas and developing countries that cannot afford the high cost of installing, calibrating, and maintaining complete automatic weather stations. This approach can help provide valuable information to technicians, farmers, managers, and policy makers in key wáter and agricultural planning areas. This doctoral thesis has developed and validated new methodologies based on artificial intelligence that have been used to improve the precision of crucial variables in the agrometeorological field: precipitation, solar radiation, and reference evapotranspiration. Specifically, prediction systems and gap-filling models for precipitation at different scales have been modeled using neural networks. Models for estimating solar radiation using only thermal parameters have also been developed and validated in areas with similar climatic characteristics to the training location, without the need to be geographically in the same region or country. Similarly, models for estimating and predicting reference evapotranspiration at the local and regional level have been developed using only temperature data for the entire process: regionalization, training, and validation. Finally, an internationally open-source Python library (AgroML) has been created to facilitate the development and application of artificial intelligence models, not only focused on the agrometeorological sector but also on any supervised model that improves decision-making in other areas of interest

Smart reference evapotranspiration using Internet of Things and hybrid ensemble machine learning approach

Reference Evapotranspiration (ET) is the cornerstone of efficient water utilization for sustainability in agriculture. The standard Penman–Montieth (PM) approach of Reference Evapotranspiration (ET), is complex due to the involvement of an extensive set of climatic conditions. The existing solutions of simplification of ET predictions are not in accordance with the Penman–Montieth approach. A hybrid ensemble machine learning approach for simplification of ET prediction is proposed using the Internet of Things(IoT) based crop field sensed climatic data. The proposed hybrid ensemble model is implemented with an Artificial Neural Network (ANN) and regression models. The proposed solution is unique for its utilization of flexible climatic conditions and in accordance with the standard Penman–Montieth (PM) approach. The proposed solution is able to predict daily ET from only temperature and also can adjust ET according to wind speed, humidity, and sunshine duration. The assessment of the proposed model exhibits a high coefficient of determination (R2) of 0.94 compared to 0.91 from the basic ANN model. The proposed hybrid ensemble model also exhibits a low RMSE of 0.86, MAE of 0.75 mm day−1, and MAPE of 15.05%, compared to 0.91, 0.75 mm day−1, and 20.40% from the basic ANN model. The ET predictions by the proposed hybrid ensemble model also exhibit a higher Pearson correlation coefficient of 0.917 with the ET by the Penman–Montieth (PM) approach, compared to 0.778 by the basic ANN model. The statistics reveal the accuracy and goodness of fit of the proposed hybrid ensemble machine learning model.© 2023 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).fi=vertaisarvioitu|en=peerReviewed

AgroML: An Open-Source Repository to Forecast Reference Evapotranspiration in Different Geo-Climatic Conditions Using Machine Learning and Transformer-Based Models

Accurately forecasting reference evapotranspiration (ET0) values is crucial to improve crop irrigation scheduling, allowing anticipated planning decisions and optimized water resource management and agricultural production. In this work, a recent state-of-the-art architecture has been adapted and deployed for multivariate input time series forecasting (transformers) using past values of ET0 and temperature-based parameters (28 input configurations) to forecast daily ET0 up to a week (1 to 7 days). Additionally, it has been compared to standard machine learning models such as multilayer perceptron (MLP), random forest (RF), support vector machine (SVM), extreme learning machine (ELM), convolutional neural network (CNN), long short-term memory (LSTM), and two baselines (historical monthly mean value and a moving average of the previous seven days) in five locations with different geo-climatic characteristics in the Andalusian region, Southern Spain. In general, machine learning models significantly outperformed the baselines. Furthermore, the accuracy dramatically dropped when forecasting ET0 for any horizon longer than three days. SVM, ELM, and RF using configurations I, III, IV, and IX outperformed, on average, the rest of the configurations in most cases. The best NSE values ranged from 0.934 in Córdoba to 0.869 in Tabernas, using SVM. The best RMSE, on average, ranged from 0.704 mm/day for Málaga to 0.883 mm/day for Conil using RF. In terms of MBE, most models and cases performed very accurately, with a total average performance of 0.011 mm/day. We found a relationship in performance regarding the aridity index and the distance to the sea. The higher the aridity index at inland locations, the better results were obtained in forecasts. On the other hand, for coastal sites, the higher the aridity index, the higher the error. Due to the good performance and the availability as an open-source repository of these models, they can be used to accurately forecast ET0 in different geo-climatic conditions, helping to increase efficiency in tasks of great agronomic importance, especially in areas with low rainfall or where water resources are limiting for the development of crops

Using Ensembles of Machine Learning Techniques to Predict Reference Evapotranspiration (ET0) Using Limited Meteorological Data

To maximize crop production, reference evapotranspiration (ET0) measurement is crucial for managing water resources and planning crop water needs. The FAO-PM56 method is recommended globally for estimating ET0 and evaluating alternative methods due to its extensive theoretical foundation. Numerous meteorological parameters, needed for ET0 estimation, are difficult to obtain in developing countries. Therefore, alternative ways to estimate ET0 using fewer climatic data are of critical importance. To estimate ET0 with alternative methods, difference climatic parameters of temperatures, relative humidity (maximum and minimum), sunshine hours, and wind speed for a period of 20 years from 1996 to 2015 were used in the study. The data were recorded by 11 meteorological observatories situated in various climatic regions of Pakistan. The significance of the climatic parameters used was evaluated using sensitivity analysis. The machine learning techniques of single decision tree (SDT), tree boost (TB) and decision tree forest (DTF) were used to perform sensitivity analysis. The outcomes indicated that DTF-based models estimated ET0 with higher accuracy and fewer climatic variables as compared to other ML techniques used in the study. The DTF technique, with Model 15 as input, outperformed other techniques for the most part of the performance metrics (i.e., NSE = 0.93, R-2 = 0.96 and RMSE = 0.48 mm/month). The results indicated that the DTF with fewer climatic variables of mean relative humidity, wind speed and minimum temperature could estimate ET0 accurately and outperformed other ML techniques. Additionally, a non-linear ensemble (NLE) of ML techniques was further used to estimate ET0 using the best input combination (i.e., Model 15). It was seen that the applied non-linear ensemble (NLE) approach enhanced modelling accuracy as compared to a stand-alone application of ML techniques (R-2 Multan = 0.97, R2 Skardu = 0.99, R-2 ISB = 0.98, R2 Bahawalpur = 0.98 etc.). The study results affirmed the use of an ensemble model for ET0 estimation and suggest applying it in other parts of the world to validate model performance

Hybrid neural network based models for evapotranspiration prediction over limited weather parameters

Evapotranspiration can be used to estimate the amount of water required by agriculture projects and green spaces, playing a key role in water management policies that combat the hydrological drought, which assumes a structural character in many countries. In this context, this work presents a study on reference evapotranspiration (ETo) estimation models, having as input a limited set of meteorological parameters, namely: temperature, humidity, and wind. Since solar radiation (SR) is an important parameter in the determination of ETo, SR estimation models are also developed. These ETo and SR estimation models compare the use of Artificial Neural Networks (ANN), Long Short Term Memory (LSTM), Gated Recurrent Unit (GRU), Recurrent Neural Network (RNN), and hybrid neural network models such as LSTM-ANN, RNN-ANN, and GRU-ANN. Two main approaches were taken for ET(o )estimation: (i) directly use those algorithms to estimate ETo, and (ii) estimate solar radiation first and then use that estimation together with other meteorological parameters in a method that predicts ETo. For the latter case, two variants were implemented: the use of the estimated solar radiation as (ii.1) a feature of the neural network regressors, and (ii.2) the use of the Penman-Monteith method (a.k.a. FAO-56PM method, adopted by the United Nations Food and Agriculture Organization) to compute ETo, which has solar radiation as one of the input parameters. Using experimental data collected from a weather station (WS) located in Vale do Lobo (Portugal), the later approach achieved the best result with a coefficient of determination (R-2) of 0.977. The developed model was then applied to data from eleven stations located in Colorado (USA), with very distinct climatic conditions, showing similar results to the ones for which the models were initially designed ((R2) > 0.95), proving a good generalization. As a final notice, the reduced-set features were carefully selected so that they are compatible with free online weather forecast services.info:eu-repo/semantics/publishedVersio

Modeling pan evaporation using Gaussian Process Regression, K-Nearest Neighbors, Random Forest, and Support Vector Machines: Comparative analysis

Evaporation is a very important process; it is one of the most critical factors in agricultural, hydrological, and meteorological studies. Due to the interactions of multiple climatic factors, evaporation is considered as a complex and nonlinear phenomenon to model. Thus, machine learning methods have gained popularity in this realm. In the present study, four machine learning methods of Gaussian Process Regression (GPR), K-Nearest Neighbors (KNN), Random Forest (RF) and Support Vector Regression (SVR) were used to predict the pan evaporation (PE). Meteorological data including PE, temperature (T), relative humidity (RH), wind speed (W), and sunny hours (S) collected from 2011 through 2017. The accuracy of the studied methods was determined using the statistical indices of Root Mean Squared Error (RMSE), correlation coefficient (R) and Mean Absolute Error (MAE). Furthermore, the Taylor charts utilized for evaluating the accuracy of the mentioned models. The results of this study showed that at Gonbad-e Kavus, Gorgan and Bandar Torkman stations, GPR with RMSE of 1.521 mm/day, 1.244 mm/day, and 1.254 mm/day, KNN with RMSE of 1.991 mm/day, 1.775 mm/day, and 1.577 mm/day, RF with RMSE of 1.614 mm/day, 1.337 mm/day, and 1.316 mm/day, and SVR with RMSE of 1.55 mm/day, 1.262 mm/day, and 1.275 mm/day had more appropriate performances in estimating PE values. It was found that GPR for Gonbad-e Kavus Station with input parameters of T, W and S and GPR for Gorgan and Bandar Torkmen stations with input parameters of T, RH, W and S had the most accurate predictions and were proposed for precise estimation of PE. The findings of the current study indicated that the PE values may be accurately estimated with few easily measured meteorological parameters

Utilization of new computational intelligence methods to estimate daily Evapotranspiration of wheat using Gamma pre processing

Estimation of evapotranspiration (ET) is needed in water resources management, scheduling of farm irrigation, and environmental assessment. Hence, in practical hydrology, it is often crucial to reliably and constantly estimate evapotranspiration. Accordingly, 3 artificial intelligence (AI) techniques comprising adaptive neuro-fuzzy inference system (ANFIS), artificial neural network (ANN) and adaptive neuro-fuzzy inference- wavelet (ANFIS-Wavelet) were applied in to estimate wheat crop evapotranspiration (ETc). A case study in a Dashtenaz region located in Mazandaran, Iran, was conducted with weather daily data, including maximum temperature, minimum temperature, maximum relative humidity, minimum relative humidity, wind speed, and solar radiation since 2003 to 2011. The daily climatic data from Dashtenaz stations, (8 stations), were used as inputs AI models for estimating ET0.The assessments of the AI models were compared with the wheat crop evapotranspiration (ETc) values measured by crop coefficient approach and standard FAO-56 Penman–Monteith equation. Similarly, determination coefficient (R2), Nash–Sutcliffe (CNS) efficiency coefficient model and root mean squared error (RMSE) were applied to compare the models performance and to decide on the best one. The outcomes attained with the ANFIS-Wavelet model (with trapezoidal member function’s combination with Mayer wavelet) were better than ANN and ANFIS models for ETc estimation and confirmed the potential of this technique to provide useful tool in ETc modeling

Estimation of monthly pan evaporation using support vector machine in Three Gorges Reservoir Area, China

Pan evaporation plays a critical role in estimating water budget and modeling crop water requirements. However, it has been measured at a very limited number of meteorological stations. Estimation of pan evaporation from measured meteorological variables offers an important alternative and drawn increasing attention in the recent years. This paper investigated the performance of support vector machine (SVM) in the estimation of monthly pan evaporation using commonly measured meteorological variables in Three Gorges Reservoir Area in China. Evaluation suggested that SVM models showed remarkable performances and significantly outperformed the empirical model. The SVM model with polynomial as kernel function outperformed that with radial basis function. In the case of unavailable measurements of pan evaporation and meteorological variables to construct the SVM model, pan evaporation can be well-estimated by SVM model developed using data at other sites. The results indicated that the SVM method would be a promising alternative over the traditional approaches for estimating pan evaporation from measured meteorological variables