Rain Prediction Research Articles

Optimizing Rain Prediction Model Using Random Forest and Grid Search Cross-Validation for Agriculture Sector

Agriculture, as a sector that is highly influenced by weather conditions, faces challenges due to increasingly unpredictable changes in weather patterns. The aim of this research is to create an optimal rainfall prediction model to help farmers create irrigation schedules, use fertilizer, and planting schedules, and protect plants from extreme weather events. The method used in this research to obtain the best rain prediction model is to use the random forest algorithm and the grid search cross-validation algorithm. Random Forest, known for its robustness and accuracy, emerged as a suitable algorithm for predicting rain. utilizing a substantial dataset from the West Nusa Tenggara Meteorology, Climatology, and Geophysics Agency covering the period 2000 to 2023. The data is then processed first to ensure its readiness for use. This process involves removing outlier data points, empty data entries, and unused features. After the preprocessing stage, the data underwent training using the Random Forest algorithm, resulting in an R-squared value of 0.1334. To obtain the optimal model, Grid Search Cross Validation is used. The results of this research obtained the best rain prediction model with an R-squared value of 0.0268. This model will be used to predict rain in the agricultural sector. This research concludes that we can get the best rain prediction model by combining Random Forest and Gird Search Cross-Validation. For further research, we can compare other rain prediction methods, add features, and combine datasets from a wider area.

Red-Wattled Lapwing (Vanellus indicus): A Traditional Rain Forecaster for Tribal Farmers of Southwestern Rajasthan

Abstract An intensive survey was carried out over the past 8 years in tribal-dominated areas of southwestern Rajasthan to document red-wattled lapwing (Vanellus indicus) behavior as a rain forecaster, relevant to farm planning and better crop production. The present study included tribal farmers, agriculture scientists, extension workers, ornithologists, and farm workers. The precision of their observations was compared with a contemporaneous study of 10–15 red-wattled lapwing nests across the 46-ha operational area of the Agricultural Research Sub-Station (ARSS) and Adaptive Trial Center (ATC), Sumerpur, Pali, Rajasthan (25.1526°N latitude, 73.0823°E longitude). Local knowledge was gathered through surveys and focus group discussions with the respondents to discuss lapwing behavior as a rain prediction indicator. Meteorological data were gathered from the ATC farm observatory. The findings revealed that the older tribal farmers have an assortment of knowledge about the behavior of red-wattled lapwing as rain prediction indicators such as nesting sites, egg position, egg arrangement, nesting material, numbers of eggs, and egg-laying patterns. These predictions have a direct correlation with modern meteorological observations. It was also noted that the elder tribal farmers planned their farms using lapwing indicators in order to increase crop production, whereas younger generations overlook the traditional rain prediction indicators and have significant knowledge about meteorological data-based weather forecasting. Both the elder and younger tribal farmers were equally interested in meteorological weather forecast announcements, but due to network connectivity challenges, residents in some remote places were unable to access modern meteorological information. Long-term accurate rain forecasting is needed for better agricultural planning and to secure crop production. Rain forecasting can be improved by blending modern meteorological information with traditional proven indications. Farmers who have considerable traditional knowledge along with modern technological updates are capable of using both sources of information in suitable and successful farm planning for better crop production. Significance Statement The purpose of this study is to examine red-wattled lapwing behavior as rain measurements and compare its reliability with modern rain forecasting methods. We also develop a theoretical framework for understanding traditional and modern rain forecasting methods for disaster management in remote and accessible areas, to assist resource management and to minimize costs of cultivation. Our results provide a guideline for blending both types of knowledge and for providing relevant information to remote tribal areas at appropriate times for better farm planning and crop production.

Impacts of Stochastic Coalescence Variability on Warm Rain Initiation Using Lagrangian Microphysics in Box and Large-Eddy Simulations

Abstract Various coalescence methods for Lagrangian microphysics schemes are tested in box and large-eddy simulation (LES) models, including the stochastic all-or-nothing (AON) superdroplet method (SDM) and a deterministic version of SDM (dSDM) that applies a fractional approach similar to the average impact method. In LES, variabilities driven by microphysics and by flow realizations are separated using the “piggybacking” technique. Rain initiation averaged over many realizations of the box model is delayed, and rain variability increases as the number of superdrops per collision volume NSD is decreased using SDM. In contrast, rain initiation time using SDM in LES is insensitive to NSD for 32 ≤ NSD ≤ 512. This is explained through the interaction between LES grid boxes, each acting as a separate collision volume. Variability across the ensemble of LES collision volumes using SDM results in rain quickly initiating in some of the LES grid cells at low NSD and leading to a similar overall timing of rain initiation from the cloud compared to simulations with high NSD. There is a ∼20% decrease in the total rain mass and mean rain flux as NSD is increased from 32 to 256, with little additional change as NSD is increased from 256 to 512. The fractional coalescence approach in dSDM leads to reduced microphysical variability and a 15–18-min delay in rain initiation compared to SDM. An additional LES ensemble with microphysical variability feeding back to the dynamics shows that flow variability dominates the impact of microphysical variability on rain properties. Thus, flow variability must be constrained to isolate impacts of microphysical variability. Significance Statement An emerging tool in cloud microphysics modeling represents drops by computational particles called “superdroplets” that evolve in the modeled flow. Recent studies have documented that different superdroplet models produce widely varying predictions of rain. Understanding these differences is an important step in the wider adoption of these models by the community. Here, we examine different methods for representing droplet collision-coalescence in a superdroplet model and sensitivity to the number of superdroplets employed. The timing of rain initiation is insensitive to the superdroplet number in 3D cloud simulations, but rain is substantially delayed using a coalescence method that limits random variability in droplet collisions. We also show that flow variability must be constrained to isolate impacts of microphysical variability on rain properties.

The added value of Himawari-8 satellite radiance data assimilation for very heavy rainfall prediction in the Indonesian Maritime Continent (Case study: East Kalimantan)

Abstract This study evaluates the impact of assimilating Advanced Himawari Imager (AHI) radiance data on the 8-10 water vapor channel using the three-dimensional variational (3DVAR) technique for the very heavy rainfall events in East Kalimantan on June 2-4, 2019. We utilized the Weather Research and Forecasting (WRF) and WRF Data Assimilation (WRFDA) system to run assimilation and prediction model simulations. To compare the assimilated model with AHI data and without assimilation, we devised two experimental schemes: 3DVAR and NODA. We assessed the enhancement in model prediction by comparing simulation results based on convection evolution, vertically integrated moisture flux convergence (VIMFC) increment, and rain prediction skill metrics, including Threat Score (TS), Equitable Threat Score (ETS), and Fractions Skill Score (FSS). Our findings suggest that the incorporation of AHI data results in a substantial improvement of the WRF model’s prediction of the very heavy rainfall events in East Kalimantan.

Acid rain prediction in the Guangdong-Hong Kong-Macao Greater Bay Area using an explainable machine learning framework

Acid rain, characterized by pH values lower than 5.6, is a critical natural disturbance of ecosystems, which threatens the sustainability of ecosystems, agriculture, and human society worldwide. However, accurately quantifying the driving factors of acid rain remains challenging due to a changing environment of significant spatial heterogeneity. Here, we established an explainable machine-learning framework (MLF) using 19 meteorological, air pollutant, and land surface variables as model input to construct the pH values of acid rain across the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) during 2006–2021. The MLF includes Extreme Gradient Boosting (XGBoost) for acid deposition prediction and a SHapley Additive exPlanations method (SHAP) for interpreting factor importance. The results indicated that the observed increases in pH values of acid rain are predominantly controlled by the significant decreases in maximum daily sulfur dioxide (SO2) concentration of air across GBA, with its relative contribution ranging from 16.2% to 31.9% for each city. Changes in the urbanization rate and the proximity to the coast also play significant roles in predicting the pH values of acid rain. Meteorological variables typically have minimal impact on acid rain predictions, with their contribution generally being less than 5%, indicating the complex physical process of acid rain generation. This study enhanced our comprehension of the spatial variability of acid rain drivers across a highly developed region, providing valuable insights and case studies for regions worldwide that frequently experience acid rain.

Analysis of the effect of plum rain on Yangtze River Delta

Every year, from June to July, there will be two or three weeks of continuous precipitation at Yangtze River Region. This precipitation is called Plum Rain. This paper is mainly about the Plum rain front, and its effect on the Yangtze River Delta. While the Yangtze River Delta is a large area, and considering that the data has to be easy to access, this paper specifics the topic to the most representable region-Shanghai. This paper is divided into 4 parts, the first part is the introduction, including the naming and formation of the plum rain. The second part is mainly about several types of anomalous plum rain and how they occurred. The third part analyzes the effects of the plum rain on the Yangtze River Delta region, and how to prevent these effects. The fourth part is conclusion. This paper believes that the prediction and alarming of anomalous plum rain should be focused on, and the prediction can let the government to advance layout, and decrease the economic loss when anomalous plum rain strikes.

Bayesian typhoon precipitation prediction with a mixture of ensemble forecast-based and historical event-based prediction functions

Statistical post-processing has been widely adopted in probabilistic forecasting, especially when a predictive distribution of weather variables is of interest. Among the available post-processing methods, a common tool is the ensemble Bayesian model averaging. Challenges arise when it is applied to heavy rain prediction, including the precipitation produced by tropical cyclones. The issues are related to the rare extreme event and limited available data for model training. To address this, we propose in this research a Bayesian mixture model, Bmix, based on information from forecast members and historical tropical cyclones, rather than solely on immediately observed precipitation data. This approach provides a grid-specific Bayesian predictive distribution and an easy-to-derive probabilistic forecast with posterior samples generated from the Markov chain Monte Carlo algorithm. This post-processing procedure can be executed on each grid simultaneously, reducing the computational time significantly. In addition, a categorized Bmix (C.Bmix) is implemented to accommodate different scenarios such as heavy or torrential rain in applications. The proposed approaches are demonstrated on the 24-hour accumulated precipitation forecast of a category 4-equivalent super typhoon Dujuan landing in Taiwan in 2015; while two others, Matmo and Soudelor, are adopted to provide historical information. Over the Taiwan main island, a total of 1282 grids are considered, each with a resolution of 5 km and 20 ensemble member forecasts from the Weather Research and Forecasting Ensemble Prediction System (WEPS) at a 6-hour interval during the typhoon period. The analyses show that the proposed mixture approaches present larger percentages of positive CRPSS values than traditional BMA. For instance, at the time when Dujuan made landfall, the percentages are 92.2 % for Bmix, 95.2 % for C.Bmix, and 15.4 % for BMA when the true precipitation amount exceeds 200 mm/24 h. The proposed methods outperform in the probability prediction and the overall pattern of precipitation.

Machine learning based forecasting model for rainfall prediction

Weather forecasts can be performed by collecting large amount of data about the present condition of the environment. The state of atmosphere or environment can be the temperature, humidity and wind. The future can be determined by the atmosphere evolution and this evolution can be done through meteorology with understanding of atmospherically processes. The foundation stone of Indian economy is the agriculture, which depends on the rain. Hence, early prediction of rain is highly necessary in India for agriculture. In worldwide, rainfall prediction is one among the challenging issues. Here, varied and most popular machine learning models are utilized for forecasting the rainfall. The outcome from the model gives the centre of the weather forecasts for predicting that whether tomorrow will rain or not. The experimental results will compare and show that good level of accuracy with the help of machine learning algorithms.

Prediction of Monsoon Rain of Telangana for The Year 2024

Prediction of Monsoon Rain of Telangana for The Year 2024

The influence variability of weather condition on predicting rain events in surrounding Jakarta

The metropolis Jakarta is a place where floods often occur which are detrimental to both property and life. Weather forecast information released by Meteorology, Climatology, and Geophysical Agency (BMKG) has very important in anticipating this disaster. Hence, it is important to pay attention to the weather forecast accuracy. The purpose of this study was to examine the effect of variations accuracy in rain events of the Jakarta area includes Central Jakarta, East Jakarta, West Jakarta, North Jakarta, South Jakarta, Bekasi, Tangerang, Depok, and Bogor as known Jabotabek. School of Meteorology Climatology and Geophysics or STMKG Weather Care developed voluntary observations of weather conditions especially rain events. Respondents filled out the form whether there was rain in the location where they lived and would be evaluated using the dichotomous method. This study shows the accuracy of rain prediction in the Jabotabek area of 66.8%, with prediction failures generally is an overestimation. The highest number of correct predictions occurred when the location was not raining. Moreover, the best accuracy is in Bekasi City and South Jakarta and West Jakarta is the worst. The evaluation confirms that it is not easy to predict rain events in a detailed location and the prediction terms used.

A multiscale approach to statistical downscaling of daily precipitation: Israel as a test case

AbstractRainfall in the Eastern Mediterranean is strongly modulated by complex topography and localized mesoscale processes. General circulation models (GCMs) struggle to capture daily precipitation variability in the region, both in time and in space. Rain in the Eastern Mediterranean occurs within a hierarchy of scales, as synoptic scale structures often drive local rainfall patterns. Daily rain prediction in the region can therefore benefit from analog downscaling—a nonlinear regression of a high‐resolution predictand from past synoptic‐scale predictors. We present a multiscaled downscaling algorithm of daily rain over Israel. The underlying goal is to create a mechanism‐based tool that will improve the analysis and prediction of precipitation on short time scales in models that cannot produce the field explicitly. We train the algorithm using coarse grid ERA5 reanalysis data and measurements from 21 rain gauges. The routine uses a k‐nearest neighbours algorithm to find the most similar past instances (i.e., analogs) for every predicted day. Analog selection is performed in two steps, based on scale (synoptic and local), as to not overshadow correlative but local predictors. The algorithm also includes several unique aspects tailored to Mediterranean climate: subdaily predictors of cyclone life cycles; representation of upper level cyclonic drivers; and the inclusion of rainfall potential using the Modified K‐Index (MKI). The proposed algorithm has better accuracy (66% correct predictions) compared to non‐downscaled reanalysis and climatological predictions. It better captures the spatial rainfall variance, mitigates the “drizzle bias,” and improves skill in extreme event prediction. However, it underestimates very rainy events and has trouble fully representing the spatial variance in the region. Nonetheless, our algorithm represents the potential for computationally inexpensive downscaling of daily precipitation in the Mediterranean with various possible applications, for example, characterization of droughts and storms, linking hydrological and synoptic scale processes and introducing uncertainty estimates using large ensembles.

Prediction of extreme rain in Kototabang using Himawari-8 satellite based on differences in cloud brightness temperature

The accurate prediction of extreme rain events is essential for the management and mitigation of hydrometeorological disasters. The Himawari-8 satellite provides cloud observations that can accurately forecast short-term extreme rain events using the Brightness Temperature (BT) data and Brightness Temperature Difference (BTD) method. The statistical evaluation of this method was conducted using optical rain gauge data taken from Kototabang, West Sumatra, Indonesia (100.32° E, 0.20° S) to determine the best threshold for detecting extreme rain. We used three bands, bands 11 (B11), 13 (B13) and 15 (B15) and tested three combinations of BTDs from these bands, namely BTD1 (B11-B13), BTD2 (B13-B15), and BTD3 (BTD1-BTD2). This study emphasizes the importance of parameter selection in extreme rain identification and forecasting using cloud BT and BTD methods. Effective parameter optimization is essential for adapting these approaches to different rainfall intensities, therefore selecting appropriate thresholds is necessary. The research particularly highlights the impact of temperature and rainfall intensity on BT accuracy, with BT excelling at 250 K for light rain but showing reduced accuracy as rainfall intensity increases. BTD1 demonstrates improved accuracy with higher rainfall intensity, especially at a 3 K threshold, allowing for predictions of extreme rain events with a 10–20 min lead time. However, the limitation of this threshold is shown by consistent critical success index (CSI). Therefore, BTD1 with threshold 0 K give better performances with good accuracy, CSI and false alarm ratio (FAR) for various rain intensities. BTD2 shows improved accuracy at lower thresholds with reduced rainfall intensity and at the 3 K threshold, offering potential for extreme rainfall anticipation, but with declining CSI as rainfall intensity rises. The 0 K threshold, despite high probability of detection (POD), yields increased FAR in moderate to severe rainfall scenarios. BTD3 generally exhibits increased accuracy and CSI with rising rainfall intensity, except at the −3 K threshold, with the 0 K threshold standing out as the optimal choice, providing a 10–20 min lead time for intense rain predictions. This study shows that the selection of the appropriate BT and BTD techniques, and parameter values should align with specific rainfall levels and forecasting goals.

Ancient science of weather forecasting in India with special reference to rainfall prediction

The monsoon on the Indian subcontinent remains a seasonal occurrence that all inhabitants of the subcontinent desire. Modern scientific knowledge of methods of rain forecasting has originated recently. However, traditional indigenous wisdom is peculiar to our country. In the past, India had a magnificent scientific and technical legacy. Even today, it is common that village astrologers (pandits) are right in a surprisingly high percentage of their rain predictions. Scientists and local traditional farmers have a long history of astronomical research and treatises that predict rainfall. They use different methods to forecast rainfall conditions based on numerous panchangs, almanac bio-indicators (Bhoum method), non-bio-indicators (Antariksh method), and predict the likely behavior of climate in the planting season. Rainfall forecasting also aids in the planning of operations by agriculturists, builders, water supply engineers, and others. All mortals from the subcontinent have looked at it from their own perspective, and it continues to be the subject of intense multi-dimensional engagement. The monsoon has provided a means of life for numerous civilizations while also shaping the drainages and palaeo-geography of the subcontinent. The objective of this article is to document some indigenous knowledge for forecasting climate and environmental dynamics towards community resilience.

MICE Implementation to Handle Missing Values in Rain Potential Prediction Using Support Vector Machine Algorithm

Support Vector Machine (SVM) is a machine learning algorithm used for classification. SVM has several advantages such as the ability to handle high-dimensional data, effective in handling nonlinear data through kernel functions, and resistance to overfitting through soft margins. However, SVM has weaknesses, especially when handling missing values in data. The use of SVM must consider the missing values strategy chosen. Missing values in data mining is a serious problem for researchers because it causes many problems such as loss of efficiency, complications in data handling and analysis, and the occurrence of bias due to differences between missing data and complete data. To overcome the above problems, this research focuses on understanding the characteristics of missing values and handling them using the Multiple Imputation by Chained Equations (MICE) technique. In this study, we utilized secondary data experiments that contain missing values from the Meteorological, Climatological, and Geophysical Agency (called BMKG) related to predictions of potential rain, especially in DKI Jakarta. Identification of types or patterns of missing values, exploration of the relationship between missing values and other variables, incorporation of the MICE method to handle missing values, and the Support Vector Machine Algorithm for classification will be carried out to produce a more reliable and accurate prediction model for rain potential. It shows that the imputation method with the MICE gives better results than other techniques (such as Complete Case Analysis, Imputation Method Mean, Median, Mode, and K-Nearest neighbor), namely an accuracy of 89% testing data when applying the Support Vector Machine algorithm for classification.

Estimating losses at 40-GHz downlink using non-meteorological techniques in heavy rain areas

The advancement of satellite communication has arisen tremendously where higher capacity communications systems are needed. Most satellite engineers are shifting to Ka, Q, and V-band upcoming since the low frequencies such as below 10 GHz are already congested. Actual measurement data at millimetre-wave frequencies in tropical regions are minimal. The prediction of rain attenuation at frequencies above 10 GHz is required to determine a reliable fade margin. In this paper, a statistical frequency scaling technique has been developed as an alternative way of estimating rain attenuation. The technique was derived based on the correlation between the attenuation ratio of a higher and lower frequency against the attenuation at a lower frequency. The attenuations from the proposed model were compared to the proposed frequency scaling by International Telecommunication Union-R (ITU-R) as well as the conventional ITU-R rain prediction model. To deliver a reliable model, validation methods have been done using a set of data with different years and locations in tropical regions. A dependent prediction technique with the lowest root mean square error (RMSE) value and error was produced. This technique is beneficial in applying suitable mitigation techniques to moderate rain fade in tropical regions.

Assimilation of data from the MWHS‐II onboard the first early morning satellite FY‐3E into the CMA global 4D‐Var system

AbstractRadiance data from satellite microwave radiometers are becoming increasingly important in the assimilation of numerical prediction systems. FengYun‐3E (FY‐3E), which was launched in July 2021, as the first early morning orbital satellite among FY‐3 polar‐orbiting meteorological satellite series, can greatly enhance the atmospheric sounding capacity in three orbits compared with the two‐orbit system that is distributed in morning or afternoon orbit. Focusing on MicroWave Humidity Sounder‐2 (MWHS‐II) onboard it, the schemes for thinning, quality control, bias correction and observational error calculation are developed. The scan‐position‐dependent biases are assessed in each channel and increasing biases near the edges of scanline are found in several temperature‐sounding channels. The data quality is compared with four analogous instruments used in Global/Regional Assimilation and Prediction System–Global Forecast System (CMA_GFS), and the performance improvements are confirmed relative to the previous FY‐3C/D MWHS‐II. Three batch experiments were implemented in CMA_GFS with four‐dimensional variational (4D‐Var) system—assimilating only the humidity‐sounding channels, the temperature‐sounding channels and the combined channels—to reveal the impact of assimilation by different channel groupings. Analysis results conclude that FY‐3E MWHS‐II radiances are beneficial to the humidity and temperature analysis fields. These results emphasize the advantage of simultaneously assimilating the humidity/temperature‐sounding channels, in which the former dominates the improvement above 700 hPa while the latter have a positive impact below 700 hPa. The forecast results also show that assimilating both the 183‐ and 118‐GHz channels further improves the forecast skills, especially short‐ to medium‐term forecasts in both hemispheres. FY‐3E MWHS‐II also improves the short‐term prediction of heavy rain, which is always weakly predicted in global forecast systems. This study shows that a three polar orbits system that FY‐3E began can produce marked effects in the numerical prediction system. This study also shines light on the application potential of the distinctive 118‐GHz channels of MWHS‐II.

Short-Term Rainfall Prediction Using Supervised Machine Learning

Floods and rain significantly impact the economy of many agricultural countries in the world. Early prediction of rain and floods can dramatically help prevent natural disaster damage. This paper presents a machine learning and data-driven method that can accurately predict short-term rainfall. Various machine learning classification algorithms have been implemented on an Australian weather dataset to train and develop an accurate and reliable model. To choose the best suitable prediction model, diverse machine learning algorithms have been applied for classification as well. Eventually, the performance of the models has been compared based on standard performance measurement metrics. The finding shows that the hist gradient boosting classifier has given the highest accuracy of 91%, with a good F1 value and receiver operating characteristic, the area under the curve score.

Rainfall Prediction Rate in Saudi Arabia Using Improved Machine Learning Techniques

Every farmer requires access to rainfall prediction (RP) to continue their exploration of harvest yield. The proper use of water assets, the successful collection of water, and the successful pre-growth of water construction all depend on an accurate assessment of rainfall. The prediction of heavy rain and the provision of information regarding natural catastrophes are two of the most challenging factors in this regard. In the twentieth century, RP was the most methodically and technically complicated issue worldwide. Weather prediction may be used to calculate and analyse the behaviour of weather with unique features and to determine rainfall patterns at an exact locale. To this end, a variety of methodologies have been used to determine the rainfall intensity in Saudi Arabia. The classification methods of data mining (DM) approaches that estimate rainfall both numerically and categorically can be used to achieve RP. This study, which used DM approaches, achieved greater accuracy in RP than conventional statistical methods. This study was conducted to test the efficacy of several machine learning (ML) approaches for forecasting rainfall, utilising southern Saudi Arabia’s historical weather data obtained from the live database that comprises various meteorological data variables. Accurate crop yield predictions are crucial and would undoubtedly assist farmers. While engineers have developed analysis systems whose performance relies on several connected factors, these methods are seldom used despite their potential for precise crop yield forecasts. For this reason, agricultural forecasting should make use of these methods. The impact of drought on crop yield can be difficult to forecast and there is a need for careful preparation regarding crop choice, planting window, harvest motive, and storage space. In this study, the relevant characteristics required to predict precipitation were identified and the ML approach utilised is an innovative classification method that can be used determine whether the predicted rainfall will be regular or heavy. The outcomes of several different methodologies, including accuracy, error, recall, F-measure, RMSE, and MAE, are used to evaluate the performance metrics. Based on this evaluation, it is determined that DT provides the highest level of accuracy. The accuracy of the Function Fitting Artificial Neural Network classifier (FFANN) is 96.1%, which is higher than that of any of the other classifiers currently used in the rainfall database.

Automatic Irrigation Model Powered by Smart Rain Prediction Device in India

This paper presents a simple rain prediction device-based automatic irrigation management algorithm using a combination of weather parameters and soil moisture measurements for the water balance required for a crop at each condition during its growing phase that will reduce farmer intervention for irrigation and avoid unnecessary irrigation by predicting the rainfall before starting the motor for irrigating the field. This device is powered by various technologies like deep learning to classify clouds responsible for rain, machine learning models to predict rainfall based on atmospheric parameters and the Internet of Things (IoT) using different sensors to collect data from the field. This algorithm is very appropriate for farmers who are in remote locations and are not able to use the internet and WIFI due to its unavailability. The device will be attached to the motor, will take the data from sensors and will do the rain prediction at device level only and will switch ON/OFF the motor based on the soil moisture value and rain prediction without any human intervention.

Smallholder farmers’ perception on climate change, information needs and adaptation strategies for improved pineapple (Ananas comosus) production in Awaé Cameroon

This study examined smallholder pineapple farmers’ experiences of climate variability and change in production, as well as ranked adaptation and information source options using data from primary sources. The primary data consisted of a survey on pineapple growing in Awaé. The analysis revealed that pineapple farmers experience climate variability and change evidenced mainly through irregular rainfall patterns (100%), excessive heat (97.14%), drying of streams (85.71%), and rising temperatures (77.14%). These variabilities cause a change in color and taste (40.00 and 45.71% respectively), a reduction in fruit size and yield (68.57 and 100% respectively), and consequently, a reduction in income from pineapple production. Farmers identify deforestation (54.29 ± 0.51%), bush fires (34.29 ± 0.48%) as the major causes of climate change. Although the use of soil conservation practices (e.g. mixed and rotational cropping), bush fallow and crop diversification emerged as the three main adaptation options employed by smallholder pineapple farmers, climate change has the potential to reduce pineapple production in Awaé. Therefore there is a need for future studies to identify adaptation measures that will help improve pineapple production in the area. These farmers are willing to receive information commencement of rain, the prediction of stopping rain, the impact, adaptation strategies, and mitigation strategies to climate change. They can be informed through phone text messages, television, radio, and even demonstration methods for them to effectively follow up on climate change to better improve their pineapple production in Awaé.