Although agriculture remains the dominant economic activity in many countries around the world, in recent years this sector has continued to be negatively impacted by climate change leading to food insecurities. This is so because extreme weather conditions induced by climate change are detrimental to most crops and affect the expected quantity of agricultural production. Although there is no way to fully mitigate these natural phenomena, it could be much better if there is information known earlier about the future so that farmers can plan accordingly. Early information sharing about expected crop production may support food insecurity risk reduction. In this regard, this work employs data mining techniques to predict future crop (i.e., Irish potatoes and Maize) harvests using weather and yields historical data for Musanze, a district in Rwanda. The study applies machine learning techniques to predict crop harvests based on weather data and communicate the information about production trends. Weather data and crop yields for Irish potatoes and maize were gathered from various sources. The collected data were analyzed through Random Forest, Polynomial Regression, and Support Vector Regressor. Rainfall and temperature were used as predictors. The models were trained and tested. The results indicate that Random Forest is the best model with root mean square error of 510.8 and 129.9 for potato and maize, respectively, whereas R2 was 0.875 and 0.817 for the same crops datasets. The optimum weather conditions for the optimal crop yield were identified for each crop. The results suggests that Random Forest is recommended model for early crop yield prediction. The findings of this study will go a long way to enhance reliance on data for agriculture and climate change related decisions, especially in low-to-middle income countries such as Rwanda.

Landslides fall under natural, unpredictable and most distractive disasters. Hence, early warning systems of such disasters can alert people and save lives. Some of the recent early warning models make use of Internet of Things to monitor the environmental parameters to predict the disasters. Some other models use machine learning techniques (MLT) to analyse rainfall data along with some internal parameters to predict these hazards. The prediction capability of the existing models and systems are limited in terms of their accuracy. In this research paper, two prediction modelling approaches, namely random forest (RF) and logistic regression (LR), are proposed. These approaches use rainfall datasets as well as various other internal and external parameters for landslide prediction and hence improve the accuracy. Moreover, the prediction performance of these approaches is further improved using antecedent cumulative rainfall data. These models are evaluated using the receiver operating characteristics, area under the curve (ROC-AUC) and false negative rate (FNR) to measure the landslide cases that were not reported. When antecedent rainfall data is included in the prediction, both models (RF and LR) performed better with an AUC of 0.995 and 0.997, respectively. The results proved that there is a good correlation between antecedent precipitation and landslide occurrence rather than between one-day rainfall and landslide occurrence. In terms of incorrect predictions, RF and LR improved FNR to 10.58% and 5.77% respectively. It is also noted that among the various internal factors used for prediction, slope angle has the highest impact than other factors. Comparing both the models, LR model’s performance is better in terms of FNR and it could be preferred for landslide prediction and early warning. LR model’s incorrect prediction rate FNR = 9.61% without including antecedent precipitation data and 3.84% including antecedent precipitation data.

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Shallow landslides are among the natural threats causing death and damage. They are mostly triggered by rainfall in mountainous areas where precipitation used to be abundant. The amount of rainfall inducing this natural threat differs from one site to another based on the geographical characteristics of that area. In addition to the rainfall depth, the determination of soil water content in a specific zone has a major contribution to the landslide prediction and early warning systems. Rwanda being a country with hilly terrains, some areas are susceptible to both rainfall and soil water content inducing landslides. But an analytical study of the physical threshold determination of both rainfall and soil water content inducing landslides is lacking. Therefore, this experimental study is conducted to determine the rainfall and soil water content threshold that can be fed in to the landslide early warning system (LEWS) for alert messages using the Internet of Things (IoT) technology. Various experiments have been conducted for the real-time monitoring of slope failure using the toolset composed of a rain gauge, soil moisture sensors, and a rainfall simulating tool. The results obtained show that the threshold for landslide occurrence does not solely correlate with the total rainfall amount (or intensity) or soil moisture, but also influenced by internal (geological, morphological) and environmental factors. Among the sampled sites, the sites covered by forest indicated no sign of slope failure, whereas sites with crops could slip. The experiments revealed that for a specific site, the minimum duration to induce slope failure was 8 hours, 41 minutes with the rainfall intensity of 8 mm/hour, and the soil moisture was above 90% for deeper sensors. These values are used as thresholds for LEWS for that specific site to improve predictions.

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